marketing: v3.1 launch video — 30s Remotion render

Six-scene 30-second MP4 announcing v3.1 (competitors mode) for X.

Lives at marketing/v3.1-launch/. Renders to out/last30days-v3.1-launch.mp4
(gitignored). 1920×1080 @ 30fps, H.264, ~3MB final.

Six scenes:
1. Hook (0-3s): badge + "What if one search ran 3 at once?"
2. Old way (3-8s): single terminal /last30days OpenAI
3. Fan-out (8-14s): --competitors splits into 3 parallel panes
4. Comparison (14-21s): 3 panes collapse into Head-to-Head table
5. How (21-26s): "You pick the topic. Agent picks peers. Engine fans out."
6. CTA (26-30s): install command + repo URL

Reusable components: TerminalWindow, TypedLine, BadgeBar,
ComparisonTable. Scene timing in src/lib/timing.ts as single source of
truth — one edit to retime everything.

Marketing version 3.1 ≠ engine code version (still 3.0.14). 3.1 is the
launch label that bundles 3.0.11-3.0.14 into one shippable narrative.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.claude-plugin/marketplace.json

@@ -0,0 +1,23 @@
+{
+  "$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
+  "name": "last30days-skill",
+  "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, GitHub, and 5+ more sources.",
+  "owner": {
+    "name": "Matt Van Horn",
+    "url": "https://github.com/mvanhorn"
+  },
+  "plugins": [
+    {
+      "name": "last30days",
+      "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, GitHub, and 5+ more sources.",
+      "version": "3.0.9",
+      "author": {
+        "name": "Matt Van Horn",
+        "url": "https://github.com/mvanhorn"
+      },
+      "source": "./",
+      "category": "productivity",
+      "homepage": "https://github.com/mvanhorn/last30days-skill"
+    }
+  ]
+}

.claude-plugin/plugin.json

@@ -0,0 +1,15 @@
+{
+  "name": "last30days",
+  "version": "3.0.14",
+  "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and 5+ more sources. AI agent scores by upvotes, likes, and real money - not editors.",
+  "author": {
+    "name": "Matt Van Horn",
+    "email": "mvanhorn@gmail.com",
+    "url": "https://github.com/mvanhorn"
+  },
+  "homepage": "https://github.com/mvanhorn/last30days-skill",
+  "repository": "https://github.com/mvanhorn/last30days-skill",
+  "license": "MIT",
+  "keywords": ["research", "reddit", "twitter", "youtube", "tiktok", "instagram", "trends", "prompts", "polymarket", "github", "perplexity", "threads", "pinterest", "eli5", "hacker-news"],
+  "hooks": {}
+}

.clawhubignore

@@ -0,0 +1,18 @@
+# Exclude binary assets and dev/test artifacts from ClawHub bundle
+assets/
+docs/
+fixtures/
+tests/
+plans/
+agents/
+variants/
+release-notes.md
+SPEC.md
+TASKS.md
+SKILL-original.md
+*.jsonl
+*.mp3
+*.jpeg
+*.jpg
+*.png
+*.gif

.codex-plugin/plugin.json

@@ -0,0 +1,3 @@
+{
+  "name": "last30days"
+}

.gitattributes

@@ -0,0 +1,46 @@
+# Exclude non-runtime files from `git archive` output.
+# Used by scripts/build-skill.sh to produce a claude.ai-upload-ready .skill file.
+# See docs/plans/2026-04-14-001-fix-skill-upload-200-file-limit-plan.md.
+
+# Anthropic canonical skill-packaging excludes
+# (mirrors anthropics/skills/skills/skill-creator/scripts/package_skill.py)
+__pycache__/   export-ignore
+node_modules/  export-ignore
+*.pyc          export-ignore
+.DS_Store      export-ignore
+evals/         export-ignore
+
+# Dev, docs, test, and media - not needed at skill runtime
+tests/         export-ignore
+docs/          export-ignore
+fixtures/      export-ignore
+assets/        export-ignore
+
+# NOTE: skills/ and .claude-plugin/ are NOT export-ignored here because
+# Claude Code's /plugin install fetches this same git archive tarball.
+# Removing those from the archive (as v3.0.1 did) silently breaks installs.
+# claude.ai-bundle-specific exclusions live in scripts/build-skill.sh.
+
+# Historical + repo-only manifests
+SKILL-original.md  export-ignore
+SPEC.md            export-ignore
+TASKS.md           export-ignore
+test-run.log       export-ignore
+CONTRIBUTORS.md    export-ignore
+HERMES_SETUP.md    export-ignore
+release-notes.md   export-ignore
+CHANGELOG.md       export-ignore
+uv.lock            export-ignore
+
+# Platform adapters - skill-upload path is platform-agnostic
+.agents/         export-ignore
+.codex-plugin/   export-ignore
+.hermes-plugin/  export-ignore
+
+# CI workflows - repo-only, not needed at skill runtime
+.github/         export-ignore
+
+# Build config itself
+.clawhubignore  export-ignore
+.gitignore      export-ignore
+.gitattributes  export-ignore

.github/workflows/release.yml

@@ -0,0 +1,31 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - "v*"
+
+permissions:
+  contents: write
+
+jobs:
+  build-and-release:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Build .skill artifact
+        run: |
+          bash scripts/build-skill.sh
+          test -f dist/last30days.skill
+
+      - name: Create GitHub release
+        uses: softprops/action-gh-release@v2
+        with:
+          files: dist/last30days.skill
+          generate_release_notes: true
+          draft: false
+          prerelease: false

.gitignore

@@ -0,0 +1,36 @@
+# Private benchmark / evaluation artifacts — never push to upstream
+docs/comparison-results/
+scripts/evaluate-synthesis.py
+scripts/generate-synthesis-inputs.py
+fixtures/polymarket_sample.json
+docs/v2.1-tweets.md
+docs/30-day-anniversary-thread.md
+docs/30-day-anniversary-tweets.md
+variants/open/references/research.md
+
+# OS / tool files
+.DS_Store
+.claude/
+.entire/
+__pycache__/
+*.pyc
+mise.toml
+.memsearch/
+.venv/
+.coverage
+htmlcov/
+
+# Root vendor/ is accidental - real vendored client lives at scripts/lib/vendor/bird-search/
+/vendor/
+
+# build artifact from scripts/build-skill.sh
+/dist/
+
+# Internal planning docs (ce:plan output) — keep local, don't publish
+docs/plans/
+
+# Marketing video build artifacts
+marketing/v3.1-launch/node_modules/
+marketing/v3.1-launch/out/
+marketing/*/node_modules/
+marketing/*/out/

CHANGELOG.md

@@ -0,0 +1,393 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [3.0.14] - 2026-04-22
+
+### Changed
+
+- **Comparison-mode title attribution.** The synthesis title for vs-mode and `--competitors` outputs changes from `What the Community Says (Last 30 Days)` to `What the Community Says (/Last30Days)`. Surfaces the slash-command identity instead of restating the date range. Three SKILL.md occurrences updated; pure documentation change.
+
+## [3.0.13] - 2026-04-22
+
+### Changed
+
+- **vs mode runs N full passes in parallel, one per entity.** Architectural revert of the 3-pass → 1-pass latency optimization from an earlier version. `/last30days "OpenAI vs Anthropic vs xAI"` now runs three full `pipeline.run()` calls in parallel via the same fanout `--competitors` uses, producing three `*-raw.md` save files plus a merged comparison output. Each entity gets its own Step 0.55-grade targeting, own primary X handle weight, own subreddit scoping — apples-to-apples depth instead of the one-pool merged retrieval the single-pass path produced. Parallel execution keeps wall clock ≈ single pass.
+- **`--competitors` is now a SKILL.md-level shortcut for vs-mode with auto-discovery.** The hosting reasoning model (Claude Code, Codex, Hermes, Gemini, any agent with WebSearch) performs discovery and Step 0.55 per entity via its own WebSearch tool, then invokes the engine with a vs-topic and `--competitors-plan` JSON. The engine flag remains for headless/cron use with BRAVE/EXA/SERPER/PARALLEL/OPENROUTER keys (engine-internal `auto_resolve` stays as fallback).
+- **LAW 7-style stderr for `--competitors` with no backend** now leads with the hosting-model path (WebSearch + Step 0.55 + `--competitors-plan`) instead of `BRAVE_API_KEY`. API-key framing moved to a secondary "headless" section.
+
+### Added
+
+- **`--competitors-plan` JSON flag** for per-entity Step 0.55 targeting. Schema: `{entity_name: {x_handle?, x_related?, subreddits?, github_user?, github_repos?, context?}}`. Accepts inline JSON or a file path (matches `--plan`). When present for an entity, skips engine-internal `auto_resolve` and uses the provided values; missing fields fall back to `auto_resolve` (if backend) or planner defaults. Case-insensitive entity matching. The `subrun_kwargs_for` helper is the single source of truth for per-entity kwargs — no closure-default fallthrough from main scope.
+- **Per-entity save files** when `--save-dir` is set on a vs-mode or `--competitors` run. Each entity's sub-run produces its own `{slug}-raw.md` with a single-row Resolved Entities block — matches historical vs-mode behavior (N passes → N save files).
+- **`--polymarket-keywords "kw1,kw2"`** to filter Polymarket matches for ambiguous single-token topics (e.g., "Warriors" → `nba,gsw,golden-state` kills Glasgow Warriors rugby and Honor of Kings Rogue Warriors noise).
+
+### Fixed
+
+- **BRAVE/SERPER footer nudge suppressed** when `--plan` or `--competitors-plan` is present. The nudge told Claude Code users to set an API key when they already have WebSearch via the hosting model. Nudge still fires for true headless runs (no `--plan`, no backend) where the advice is correct.
+- **Override-leak regression testing.** 3.0.12 already fixed the main-topic `--subreddits` / `--x-handle` / `--github-*` from leaking into peer sub-runs via explicit per-entity kwargs scrubbing. This release adds a 4-test regression suite (`test_competitor_subrun_isolation.py`) locking in the invariant.
+
+## [3.0.12] - 2026-04-22
+
+### Fixed
+
+- **Per-entity Step 0.55 resolution for competitor sub-runs.** In 3.0.11, only the main topic got X handle / subreddit / GitHub resolution; competitor sub-runs ran with planner defaults and produced visibly thinner evidence (Reddit 403 fallbacks, single-word queries). Each competitor sub-run now calls `resolve.auto_resolve()` inside `fanout.run_competitor_fanout` when a web backend is available, mirroring the main topic's pre-flight resolution. Per-entity X handle, subreddit list, GitHub user/repos, and news context are threaded into each sub-run's `pipeline.run()` call. Deep-copied config per sub-run prevents `_auto_resolve_context` cross-leak. Surfaces in a new `## Resolved Entities` output block so the resolution coverage is visible without reading stderr.
+- **LAW 7 false-positive on internal fan-out sub-runs.** Each competitor sub-run was emitting the `[Planner] No --plan passed... YOU ARE the planner` stderr warning. LAW 7 targets the hosting-reasoning-model path, not engine-internal fan-out. New `internal_subrun=True` keyword on `planner.plan_query` and `pipeline.run` suppresses the warning for sub-runs only; the default path is unchanged.
+- **Marketplace-stale SKILL.md trap.** Added a STEP 0 canonical-path self-check at the top of SKILL.md. Two of three 2026-04-22 test runs loaded SKILL.md from `plugins/marketplaces/last30days-skill/` (Claude-Code-managed git clone pinned to origin/main, lagging the versioned cache), then ran `--help` against the same stale path, did not see `--competitors`, and fell back to a manual comparison plan. The STEP 0 block forces any reader to verify they loaded from `plugins/cache/last30days-skill/last30days/{VERSION}/SKILL.md` and re-read from the versioned cache if not.
+
+### Changed
+
+- **Default `--competitors` count is now 2 (3-way total: original + 2 peers).** Previously 3. `--competitors=N` still customizes (range 1..6). Matches the feature description's canonical example (`Kanye vs Drake vs Kendrick`).
+
+### Added
+
+- **`## Resolved Entities` block** in `render_comparison_multi` output. Shows per-entity X handle, subreddits, GitHub user/repos, and truncated context for every entity in the comparison. Block is omitted entirely when no entity has a resolved payload (mock mode, no backend).
+
+## [3.0.11] - 2026-04-22
+
+### Added
+
+- **`--competitors` flag for auto-discovered comparison fan-out.** Pass `--competitors` on a single-entity topic and the engine discovers 2-6 peer entities via web search, then runs the full pipeline on each in parallel and emits one N-way comparison. `last30days Kanye West --competitors` resolves Drake, Kendrick Lamar, and one more peer. `last30days OpenAI --competitors` resolves Anthropic, xAI, Google Gemini. `--competitors=N` controls count, `--competitors-list="A,B,C"` skips discovery and uses the explicit list. Discovery mirrors the `auto_resolve` pattern (Brave / Exa / Serper / Parallel) with deterministic text extraction - no internal LLM call. Sub-runs inherit the main `--quick`/`--deep`/`--days`, run in a `ThreadPoolExecutor`, and degrade gracefully when at least 2 entities survive. Output reuses the existing 9-axis `## Head-to-Head` scaffold.
+
+## [3.0.10] - 2026-04-21
+
+### Added
+
+- **Commenter handles on evidence lines.** Top-comment rendering now includes the commenter's handle - `u/author` for Reddit, `@handle` for TikTok/YouTube/Instagram/Bluesky/X/Threads. The enrichment adapters already captured `author`; the render layer just was not using it. Evidence lines change from `- Comment (6822 upvotes): Finally, John Apple` to `- u/Cyrisaurus (6822 upvotes): Finally, John Apple`. Person-level citations make synthesis-side inline markdown links per LAW 8 much more natural. Both the compact and full render paths are covered.
+
+### Fixed
+
+- **TikTok author preference.** `_fetch_post_comments` in `scripts/lib/tiktok.py` preferred `user.nickname` over `user.unique_id`, so the engine captured display names ("Moosa Noormahomed") instead of @handles ("moosanoormahomed"). Flipped to prefer `unique_id`. Nickname still wins as a fallback when `unique_id` is missing. Display names can contain emoji, spaces, and non-Latin characters that do not round-trip to a profile URL; the @handle is the stable identifier.
+
+### Behavior fallback
+
+- When an author is empty, `[deleted]`, or `[removed]`, the render falls back to the legacy `Comment (...)` shape - no `u/` or `@` prefix with an empty handle is ever emitted.
+
+## [3.0.9] - 2026-04-18 - The Self-Debug Release
+
+### Highlights
+
+v3.0.9 adds the engine-side Class 1 keyword-trap refuse-gate ("birthday gift for 40 year old" now gets a clarifying question, not 5 minutes of junk), promotes TikTok and YouTube top comments to the same first-class rendering Reddit's got, lands Hermes AI Agent as a first-class deploy target, and moves the SKILL.md formatting contract from line 1094 to the top of the file.
+
+"The Self-Debug Release" refers to how the fixes in 3.0.6-3.0.9 were written: 5 separate Opus 4.7 instances each debugged their own failed outputs. Three converged on "SKILL.md is too big and the LAWs are too deep." Two converged on "the engine should refuse demographic-shopping queries." I shipped exactly what they said. Validation: 5/5 canonical compliance.
+
+### Added
+
+- **Engine Class 1 keyword-trap refuse-gate** (`scripts/lib/preflight.py`, new). Pattern-matches demographic-shopping queries at main() front-door. Exit code 2 with structured REFUSE message. Escape hatch: `LAST30DAYS_SKIP_PREFLIGHT=1`. 29 tests in `tests/test_preflight.py`.
+- **TikTok + YouTube top comments** rendered with same `💬 Top comment` prominence as Reddit's. Shipped in [#260](https://github.com/mvanhorn/last30days-skill/pull/260); enrichment fixed in [#265](https://github.com/mvanhorn/last30days-skill/pull/265).
+- **Hermes AI Agent as a deploy target** - thanks @stephenmcconnachie ([#228](https://github.com/mvanhorn/last30days-skill/pull/228)). `scripts/sync.sh` detects `~/.hermes/skills/research` and deploys automatically.
+- **Multi-key SCRAPECREATORS_API_KEY rotation** - thanks @zaydiscold ([#268](https://github.com/mvanhorn/last30days-skill/pull/268)). Set `SCRAPECREATORS_API_KEY_1`, `_2`, etc. Engine rotates on rate-limit.
+- **Offline quality evaluation fixture** - thanks @j-sperling ([#233](https://github.com/mvanhorn/last30days-skill/pull/233)). `eval_topics.json` lets contributors run quality regressions without burning live API credits.
+- **END-OF-CANONICAL-OUTPUT boundary** in `render_compact()`. Engine now emits an explicit pass-through instruction so re-synthesis requires actively ignoring a visible boundary.
+- **LAW 1 verbatim-pattern override.** LAW 1 now quotes the exact WebSearch tool-result reminder ("CRITICAL REQUIREMENT: MUST include Sources: section") and declares it OVERRIDDEN inside last30days output.
+
+### Changed
+
+- **SKILL.md restructure.** VOICE CONTRACT LAWs and BADGE MANDATORY block moved from line 1094 to lines 75-150. Grounded in 3 separate Opus 4.7 self-debugs.
+- **Engine emits the badge as stdout.** `🌐 last30days v3.0.9 · synced YYYY-MM-DD` is the first line of every compact emit. Pass-through is now the default-correct behavior.
+- **Reddit client HTTP consolidation** - thanks @iliaal ([#207](https://github.com/mvanhorn/last30days-skill/pull/207)). Migrated to `http.get(params=...)` helper.
+- **ScrapeCreators header consolidation** - thanks @iliaal ([#209](https://github.com/mvanhorn/last30days-skill/pull/209)). `_sc_headers` refactored into `http.scrapecreators_headers`.
+- **Simpler Hermes sync.** `scripts/sync.sh` Hermes branch now always uses main SKILL.md (previously had a `.hermes-plugin/SKILL.md` fallback that created a wrong-file-capture hazard).
+
+### Fixed
+
+- **Peter Steinberger trailing Sources leak.** 2026-04-18 validation failure where the model appended a TechCrunch / TED / Fortune / Wikipedia Sources list after the invitation. Now structurally prevented at three layers: engine emits the canonical body, LAW 1 quotes the exact WebSearch reminder, closing boundary names the anti-pattern.
+- **Wrong-file SKILL.md capture.** Deleted `.agents/skills/last30days/SKILL.md` (1382 lines, April 13 snapshot) and `.hermes-plugin/SKILL.md` (269 lines). One SKILL.md per plugin now, at the plugin root.
+- **GitHub date parsing garbage** - thanks @iliaal ([#208](https://github.com/mvanhorn/last30days-skill/pull/208)). `_parse_date` now rejects invalid input cleanly.
+- **Windows Bird X stability** - thanks @Chelebii ([#227](https://github.com/mvanhorn/last30days-skill/pull/227)).
+- **Linux `check_perms` false-warn** - thanks @george231224 ([#216](https://github.com/mvanhorn/last30days-skill/pull/216)). Uses GNU stat first.
+- **UTF-8 saved output** - thanks @Gujiassh ([#225](https://github.com/mvanhorn/last30days-skill/pull/225)).
+- **Version metadata alignment** - thanks @Gujiassh ([#217](https://github.com/mvanhorn/last30days-skill/pull/217)) and @shalomma ([#229](https://github.com/mvanhorn/last30days-skill/pull/229)).
+- **`--days` alias backcompat** - thanks @BryanTegomoh ([#230](https://github.com/mvanhorn/last30days-skill/pull/230)).
+- **`INCLUDE_SOURCES` env default** - thanks @hnshah ([#223](https://github.com/mvanhorn/last30days-skill/pull/223)).
+- **Bird X all-None engagement** - thanks @j-sperling ([#234](https://github.com/mvanhorn/last30days-skill/pull/234)).
+
+### Contributors
+
+@j-sperling, @stephenmcconnachie, @zaydiscold, @iliaal, @Chelebii, @Gujiassh, @hnshah, @george231224, @shalomma, @BryanTegomoh for PRs since v3.0.0. @uppinote20, @zerone0x, @thinkun, @thomasmktong, @fanispoulinakisai-boop, @pejmanjohn, @zl190, @Jah-yee, @dannyshmueli, @Cody-Coyote for issues and PRs that shaped the v3 roadmap.
+
+### Recovery
+
+```
+/plugin update last30days
+/reload-plugins
+```
+
+Verify: `cat ~/.claude/plugins/cache/last30days-skill/last30days/*/.claude-plugin/plugin.json | grep version` returns `"version": "3.0.9"`.
+
+Smoke test: `/last30days birthday gift for 40 year old` should ask a clarifying question before running.
+
+## [3.0.5] - 2026-04-15
+
+### Added
+
+- **`/last30days` slash command for plugin users.** New `commands/last30days.md` registers a Claude Code slash command. Users type `/last30days <topic>` and Claude Code's autocomplete prefix-matches it to the canonical `/last30days:last30days` form (the same way `/ce:plan` resolves to `/compound-engineering:ce-plan`). The command delegates to the existing `last30days` skill body — no skill behavior changes.
+
+### Removed
+
+- **`skills/last30days-nux/`** — byte-identical duplicate of root `SKILL.md` that created confusing `/last30days:last30days-nux` autocomplete entries via Claude Code's plugin namespacing. The root `SKILL.md` remains the canonical skill source.
+
+### Recovery
+
+```
+/plugin update last30days
+/reload-plugins
+```
+
+Then type `/last30days <topic>` to invoke the skill via slash command. Natural-language invocation ("search the last 30 days for X") continues to work unchanged.
+
+## [3.0.4] - 2026-04-15
+
+### Fixed
+
+- **Cleared `/doctor` path-escape error on Claude Code v2.1.109+.** `.claude-plugin/plugin.json` previously declared `"skills": ["./"]`. That value shipped unchanged from v2.1.0 through v3.0.3 and worked on older Claude Code, but current versions reject `./` with `Path escapes plugin directory: ./ (skills)`. The `"skills"` key is now omitted entirely, matching the pattern used by every other plugin in the Claude Code marketplace ecosystem. Claude Code auto-discovers `skills/*/SKILL.md` when the key is absent.
+
+### Recovery
+
+If `/doctor` reports a path-escape error for last30days, run `/plugin update last30days` then `/reload-plugins`. If errors persist, uninstall and reinstall the plugin.
+
+## [3.0.3] - 2026-04-15
+
+### Fixed
+
+- **Restored `skills/` and `.claude-plugin/` to the plugin install tarball.** v3.0.1 added `.gitattributes` rules that excluded both directories from `git archive` output to shrink the claude.ai `.skill` bundle. Claude Code's `/plugin install` fetches the same archive, so users installing v3.0.1 or v3.0.2 received a tarball with no plugin manifest and no skill files. `git archive v3.0.0` contained 8 files under those paths; `v3.0.1` and `v3.0.2` contained 0. This release reverts those `.gitattributes` lines.
+- **Reverted `plugin.json` `"skills"` field to `["./"]`.** v3.0.2 changed this to `["skills"]` based on a misdiagnosis — the manifest change had no effect because the manifest wasn't in the tarball at all. The historical `["./"]` value shipped in every release from v2.1.0 through v3.0.0 without issues and is restored here.
+
+### Recovery
+
+Users on v3.0.1 or v3.0.2: run `/plugin update last30days` then `/reload-plugins`. If autoUpdate is enabled, the next session start will pull v3.0.3 automatically. Users on cached v3.0.0 or earlier installs were unaffected.
+
+### Notes
+
+- The claude.ai `.skill` bundle built by `scripts/build-skill.sh` still works — the archive grew from 89 to 97 files, well under the 200-file cap.
+- claude.ai-specific exclusions (avoiding duplicate `SKILL.md` files in the bundle) should move into `scripts/build-skill.sh` rather than `.gitattributes` in a future release, since `.gitattributes` cannot distinguish between the two distribution channels.
+
+## [3.0.2] - 2026-04-15
+
+### Fixed
+
+- **`/last30days` slash command now registers on Claude Code v2.1.105+.** `.claude-plugin/plugin.json` declared `"skills": ["./"]`, which newer Claude Code rejects with `Path escapes plugin directory: ./ (skills)`. The skill silently failed to register, so `/last30days <query>` returned "Unknown command" even though `/plugin list` showed the plugin as installed. Fix: `"skills": ["skills"]` so the loader scans the real skill subdirectory.
+- **Version drift between manifests.** `.claude-plugin/marketplace.json` was pinned to `3.0.0` while `.claude-plugin/plugin.json` advertised `3.0.1`. The `/plugin` resolver used the marketplace version and could install stale cached metadata alongside the correct build. Both manifests now agree on `3.0.2`.
+
+### Recovery
+
+If `/last30days` stopped working for you, run `/plugin update last30days` then `/reload-plugins`. If `/doctor` still reports errors, uninstall and reinstall the plugin from the marketplace.
+
+## [3.0.1] - 2026-04-14
+
+### Fixed
+
+- **Skill upload packaging** - `scripts/build-skill.sh` produces a claude.ai-upload-ready `.skill` file that fits under the 200-file cap. Previously, zipping the repo hit 406 files and the "Upload skill" UI rejected it outright.
+- **SKILL.md description length** - trimmed from 228 to 167 chars (Anthropic caps descriptions at 200).
+
+### Removed
+
+- Unused root `vendor/` directory (215 files from an accidental commit in PR #48 - the real vendored X client lives at `scripts/lib/vendor/bird-search/`).
+- Legacy top-level `plans/` directory (superseded by `docs/plans/`; both plans described work that was already shipped in v3).
+
+### Added
+
+- `.gitattributes` with `export-ignore` entries so `git archive` drops tests, docs, fixtures, assets, historical manifests, and internal skill subdirs. Mirrors Anthropic's canonical `package_skill.py` exclusions.
+- `scripts/build-skill.sh` - one-command path to produce `dist/last30days.skill` with a single top-level `last30days/` folder, defensive `=200` file check, and dirty-tree refusal.
+- `README.md` section documenting the claude.ai skill upload workflow.
+
+## [3.0.0] - 2026-04-11
+
+### Highlights
+
+Intelligent search, fun judge, cross-source cluster merging, single-pass comparisons, and OpenClaw as a first-class citizen. The v3 engine doesn't just search for your topic -- it figures out *where* to search before the search begins. Engine architecture by @j-sperling.
+
+### Added
+
+- **Intelligent pre-research** -- Resolves X handles, subreddits, TikTok hashtags, and YouTube channels via a new Python brain before any API calls fire. Bidirectional: person to company, product to founder.
+- **Fun judge / Best Takes** -- Second parallel LLM judge scores humor, cleverness, and virality. Surfaces the best reactions in a dedicated output section.
+- **Cross-source cluster merging** -- Entity-based overlap detection merges the same story across Reddit, X, YouTube into one cluster instead of three separate items.
+- **Single-pass comparisons** -- "X vs Y" runs one pass with entity-aware subqueries instead of three serial passes. 3 minutes instead of 12+.
+- **GitHub as a source** -- Stars, reactions, and comments from repos and issues.
+- **OpenClaw first-class citizen** -- Auto-resolve for engine-side pre-research. Device auth for frictionless ScrapeCreators signup.
+- **Per-author cap** -- Max 3 items per author prevents single-voice dominance.
+- **Entity disambiguation** -- Synthesis trusts resolved handles over keyword matches.
+- **Perplexity Sonar Pro as additive source** -- AI-synthesized research with citations via OpenRouter. Opt-in via `INCLUDE_SOURCES=perplexity`. Returns structured narratives that complement social data.
+- **Perplexity Deep Research** -- `--deep-research` flag for exhaustive 50+ citation reports (~$0.90/query). Premium opt-in for serious investigation.
+- **OpenRouter as reasoning provider** -- One OPENROUTER_API_KEY powers planning, reranking, and Perplexity search. Auto-detected after Gemini/OpenAI/xAI.
+- **Parallel AI grounding backend** -- `--web-backend parallel` or auto-detected via PARALLEL_API_KEY.
+- **Grounding in planner** -- Grounding source properly registered in SOURCE_CAPABILITIES instead of force-injected.
+
+### Changed
+
+- YouTube transcript candidate pool widened 3x past music videos to reach talk/review content with captions
+- Reddit comment enrichment sorted by total engagement (upvotes + comments), not just upvotes
+- Polymarket display shows % odds only; dollar volumes removed
+- 852 tests passing
+
+### Fixed
+
+- Marketplace validation: duplicate `name: last30days` collision in `skills/last30days/SKILL.md` caused strict validators to reject the plugin. Resolved by renaming the internal v3 architecture spec to `last30days-v3-spec` with `user-invocable: false`. Fixed in #214 (reported by @Cody-Coyote in #204).
+- Stale README link to the deleted `skills/last30days-v3/` path from the v3 directory rename. Fixed in #214.
+- OpenAI Codex CLI discoverability: added `.agents/skills/last30days/SKILL.md` as a real file (Codex's loader skips symlinked files) plus `.codex-plugin/plugin.json` as the namespace marker. The skill now registers as `last30days:last30days` when Codex runs in a checkout of the repo. Fixed in #219 (inspired by @Jah-yee in #153 and @dannyshmueli on X).
+
+### Contributors
+
+- @j-sperling -- v3 engine architecture, Python pre-research brain
+- @hnshah -- Watchlist features
+- @Cody-Coyote -- Marketplace validation bug report (#204)
+- @Jah-yee -- Codex CLI integration inspiration (#153)
+
+## [2.9.4] - 2026-03-06
+
+### Changed
+
+- Move save into Python script via `--save-dir` flag - raw research data saved during the existing script Bash call, zero extra tool calls after invitation
+- Remove entire "Save Research to Documents" section from SKILL.md (~45 lines removed)
+- No more `📎` footer, no Bash heredoc, no `(No output)`, no multi-minute cogitation after research
+
+## [2.9.3] - 2026-03-06
+
+### Fixed
+
+- **Critical:** Switch save from `run_in_background` to foreground Bash - background callbacks caused model to re-engage, hallucinate fake user messages, and generate unsolicited multi-paragraph responses
+- Save uses foreground `cat >` heredoc (executes sub-second, no callback, no delayed notification)
+
+## [2.9.2] - 2026-03-06
+
+### Fixed
+
+- Save research silently using background Bash heredoc instead of Write tool (eliminates "Wrote N lines..." clutter)
+- Suppress follow-up text after background save completes (no more "Research briefing saved..." noise)
+- Add `📎` footer line for save path instead of verbose confirmation
+
+## [2.9.1] - 2026-03-05
+
+### Highlights
+
+Auto-save research briefings to the default memory directory as topic-named .md files. Every run now builds a personal research library automatically - no more manual copy-paste.
+
+### Added
+
+- Auto-save complete research briefings (synthesis, stats, follow-up suggestions) to the default memory directory after every run
+- Kebab-case filename generation from topic (e.g., "Claude Code skills" -> `claude-code-skills.md`)
+- Duplicate topic handling: appends date suffix instead of overwriting (e.g., `claude-code-skills-2026-03-05.md`)
+- Agent mode (`--agent`) also saves research files
+- Brief confirmation after save with the saved file path
+
+### Credits
+
+- [@devin_explores](https://x.com/devin_explores) -- Inspired this feature by sharing their workflow of saving every last30days run into organized .md files ([PR #51](https://github.com/mvanhorn/last30days-skill/pull/51))
+
+## [2.9.0] - 2026-03-05
+
+### Highlights
+
+ScrapeCreators Reddit as the default backend (one `SCRAPECREATORS_API_KEY` covers Reddit + TikTok + Instagram), smart subreddit discovery with relevance-weighted scoring, and top comments elevated with 10% scoring weight and prominent display.
+
+### Added
+
+- ScrapeCreators Reddit backend (`scripts/lib/reddit.py`) — keyword search, subreddit discovery, comment enrichment, all via `api.scrapecreators.com`
+- Smart subreddit discovery with relevance-weighted scoring: frequency × recency × topic-word match, replacing pure frequency count
+- `UTILITY_SUBS` blocklist to filter noise subreddits (r/tipofmytongue, r/whatisthisthing, etc.) from discovery results
+- Top comment scoring: 10% weight in engagement formula via `log1p(top_comment_score)`
+- Top comment rendering: `💬 Top comment` lines with upvote counts in compact and full report output
+- Comment excerpt length increased from 300 → 400 chars; `comment_insights` limit raised from 7 → 10
+
+### Changed
+
+- `primaryEnv` switched from `OPENAI_API_KEY` to `SCRAPECREATORS_API_KEY` — one key now powers Reddit, TikTok, and Instagram
+- Reddit engagement scoring formula: `0.55/0.40/0.05` (score/comments/ratio) → `0.50/0.35/0.05/0.10` (score/comments/ratio/top-comment)
+- SKILL.md synthesis instructions updated to emphasize quoting top comments
+
+### Fixed
+
+- Utility subreddit noise in discovery (e.g., r/tipofmytongue appearing for unrelated topics)
+- Reddit search no longer requires `OPENAI_API_KEY` — ScrapeCreators API handles search directly
+
+## [2.8.0] - 2026-03-04
+
+### Highlights
+
+Instagram Reels as the 8th signal source, TikTok migrated from Apify to ScrapeCreators API, and SKILL.md quality improvements. One API key (`SCRAPECREATORS_API_KEY`) now covers both TikTok and Instagram.
+
+### Added
+
+- Instagram Reels as 8th research source via ScrapeCreators API — keyword search, engagement metrics (views, likes, comments), spoken-word transcript extraction (`scripts/lib/instagram.py`)
+- `InstagramItem` dataclass, normalization, scoring (45% relevance / 25% recency / 30% engagement), deduplication, cross-source linking, and rendering
+- Instagram in SKILL.md: stats template (`📸 Instagram:`), citation priority, item format description, output footer
+- URL-to-name extraction examples in SKILL.md for cleaner web source display
+- `--search=instagram` flag support
+
+### Changed
+
+- TikTok backend migrated from Apify to ScrapeCreators API (`api.scrapecreators.com`)
+- `APIFY_API_TOKEN` replaced by `SCRAPECREATORS_API_KEY` in config
+- SKILL.md version bumped to v2.8
+- WebSearch citation instruction strengthened to prevent trailing Sources: blocks
+- Security section updated: Apify → ScrapeCreators references
+
+### Fixed
+
+- Web stats line showing full URLs instead of plain domain names
+- Trailing "Sources:" block appearing after skill invitation (WebSearch tool mandate conflict)
+- Instagram/TikTok not running in web-only mode when `--search=instagram` used without Reddit/X
+- `$ARGUMENTS` quoting in SKILL.md for correct flag forwarding
+
+## [2.1.0] - 2026-02-15
+
+### Highlights
+
+Three headline features: watchlists for always-on bots, YouTube transcripts as a 4th source, and Codex CLI compatibility. Plus bundled X search with no external CLI needed.
+
+### Added
+
+- Open-class skill with watchlists, briefings, and history modes (SQLite-backed, FTS5 full-text search, WAL mode) (`feat(open)`)
+- YouTube as a 4th research source via yt-dlp -- search, view counts, and auto-generated transcript extraction (`feat: Add YouTube`)
+- OpenAI Codex CLI compatibility -- install to `~/.agents/skills/last30days`, invoke with `$last30days` (`feat: Add Codex CLI`)
+- Bundled X search -- vendored subset of Bird's Twitter GraphQL client (MIT, originally by @steipete), no external CLI needed (`v2.1: Bundle Bird X search`)
+- Native web search backends: Parallel AI, Brave Search, OpenRouter/Perplexity Sonar Pro (`feat(engine)`)
+- `--diagnose` flag for checking available sources and authentication status
+- `--store` flag for SQLite accumulation (open variant)
+- Conversational first-run experience (NUX) with dynamic source status (`feat(nux)`)
+
+### Changed
+
+- Smarter query construction -- strips noise words, auto-retries with shorter queries when X returns 0 results
+- Two-phase search architecture -- Phase 1 discovers entities (@handles, r/subreddits), Phase 2 drills into them
+- Reddit JSON enrichment -- real upvotes, comments, and upvote ratio from reddit.com/.json endpoint
+- Engagement-weighted scoring: relevance 45%, recency 25%, engagement 30% (log1p dampening)
+- Model auto-selection with 7-day cache and fallback chain (gpt-4.1 -> gpt-4o -> gpt-4o-mini)
+- `--days=N` configurable lookback flag (thanks @jonthebeef, [#18](https://github.com/mvanhorn/last30days-skill/pull/18))
+- Model fallback for unverified orgs (thanks @levineam, [#16](https://github.com/mvanhorn/last30days-skill/pull/16))
+- Marketplace plugin support via `.claude-plugin/plugin.json` (inspired by @galligan, [#1](https://github.com/mvanhorn/last30days-skill/pull/1))
+
+### Fixed
+
+- YouTube timeout increased to 90s, Reddit 429 rate limit fail-fast
+- YouTube soft date filter -- keeps evergreen content instead of filtering to 0 results
+- Eager import crash in `__init__.py` that broke Codex environments
+- Reddit future timeout (same pattern as YouTube timeout bug)
+- Process cleanup on timeout/kill -- tracks child PIDs for clean shutdown
+- Windows Unicode fix for cp1252 emoji crash (thanks @JosephOIbrahim, [#17](https://github.com/mvanhorn/last30days-skill/pull/17))
+- X search returning 0 results on popular topics due to over-specific queries
+
+### New Contributors
+
+- @JosephOIbrahim -- Windows Unicode fix ([#17](https://github.com/mvanhorn/last30days-skill/pull/17))
+- @levineam -- Model fallback for unverified orgs ([#16](https://github.com/mvanhorn/last30days-skill/pull/16))
+- @jonthebeef -- `--days=N` configurable lookback ([#18](https://github.com/mvanhorn/last30days-skill/pull/18))
+
+### Credits
+
+- @galligan -- Marketplace plugin inspiration
+- @hutchins -- Pushed for YouTube feature
+
+## [1.0.0] - 2026-01-15
+
+Initial public release. Reddit + X search via OpenAI Responses API and xAI API.
+
+[3.0.9]: https://github.com/mvanhorn/last30days-skill/compare/v3.0.5...v3.0.9
+[2.9.1]: https://github.com/mvanhorn/last30days-skill/compare/v2.9.0...v2.9.1
+[2.9.0]: https://github.com/mvanhorn/last30days-skill/compare/v2.8.0...v2.9.0
+[2.8.0]: https://github.com/mvanhorn/last30days-skill/compare/v2.6.0...v2.8.0
+[2.1.0]: https://github.com/mvanhorn/last30days-skill/compare/v1.0.0...v2.1.0
+[1.0.0]: https://github.com/mvanhorn/last30days-skill/releases/tag/v1.0.0

CLAUDE.md

@@ -0,0 +1,25 @@
+# last30days Skill
+
+Claude Code skill for researching any topic across Reddit, X, YouTube, and web.
+Python scripts with multi-source search aggregation.
+
+## Structure
+- `scripts/last30days.py` — main research engine
+- `scripts/lib/` — search, enrichment, rendering modules
+- `scripts/lib/vendor/bird-search/` — vendored X search client
+- `SKILL.md` — skill definition (deployed to ~/.claude/skills/last30days/)
+
+## Commands
+```bash
+python3 scripts/last30days.py "test query" --emit=compact  # Run research
+bash scripts/sync.sh                                        # Deploy to ~/.claude, ~/.agents, ~/.codex
+```
+
+## Rules
+- `lib/__init__.py` must be bare package marker (comment only, NO eager imports)
+- After edits: run `bash scripts/sync.sh` to deploy
+- Git remote: origin = public (`mvanhorn/last30days-skill`)
+
+## Beta channel
+
+Experimental changes get tested on `mvanhorn/last30days-skill-private`, which installs as a parallel `/last30days-beta` slash command. Beta-only changes never ship to public without a review PR here. Workflow guide lives at `BETA.md` in the private repo. Plan that established this setup: `docs/plans/2026-04-17-005-feat-beta-skill-from-private-repo-plan.md`.

CONTRIBUTORS.md

@@ -0,0 +1,59 @@
+# Contributors
+
+last30days is built by [@mvanhorn](https://github.com/mvanhorn) with help from the community.
+
+## v3 Inspiration
+
+These contributors submitted PRs and issues that directly inspired v3 features. The v3 engine was a ground-up rewrite, so their original code wasn't merged, but their ideas shaped what shipped.
+
+Want to claim your entry? Submit a PR replacing the placeholder line below your name with your bio, website, or anything you'd like.
+
+---
+
+### @uppinote20
+[PR #143](https://github.com/mvanhorn/last30days-skill/pull/143) - Rich Reddit comments, top 3 per post
+v3 ships top comments with upvote counts on every thread.
+> _Add your bio, website, or anything you'd like here._
+
+### @zerone0x
+[Issue #134](https://github.com/mvanhorn/last30days-skill/issues/134) + [PR #136](https://github.com/mvanhorn/last30days-skill/pull/136) - GitHub as a first-class data source
+v3 has full GitHub search: issues, PRs, person-mode profiles, project-mode repos with live star counts.
+> _Add your bio, website, or anything you'd like here._
+
+### @thinkun
+[PR #116](https://github.com/mvanhorn/last30days-skill/pull/116) - Resilient Reddit, prevent enrichment timeout from discarding results
+v3 has parallel enrichment with per-item timeouts. No results are ever dropped.
+> _Add your bio, website, or anything you'd like here._
+
+### @thomasmktong
+[PR #124](https://github.com/mvanhorn/last30days-skill/pull/124) - Pure Python Reddit fallback
+v3 Reddit is 100% pure Python with zero external dependencies.
+> _Add your bio, website, or anything you'd like here._
+
+### @fanispoulinakisai-boop
+[Issue #100](https://github.com/mvanhorn/last30days-skill/issues/100) - Reddit timeout report
+Drove the timeout resilience work that made v3 Reddit bulletproof.
+> _Add your bio, website, or anything you'd like here._
+
+### @pejmanjohn
+[Issue #78](https://github.com/mvanhorn/last30days-skill/issues/78) - ScrapeCreators silent failures
+v3 surfaces all API errors with clear diagnostics instead of silently returning empty results.
+> Repping the mighty MI; home of the most cracked agentic engineers. https://github.com/pejmanjohn
+
+### @zl190
+[PR #115](https://github.com/mvanhorn/last30days-skill/pull/115) - HN trending merge
+v3 merges trending and keyword HN results with deduplication for better coverage.
+> Healthcare AI engineer. [Blog](https://zl190.github.io/blog)
+
+### @hnshah
+[PR #84](https://github.com/mvanhorn/last30days-skill/pull/84), [#85](https://github.com/mvanhorn/last30days-skill/pull/85), [#86](https://github.com/mvanhorn/last30days-skill/pull/86) - Watchlist delivery, 90-day scanning window, HN/Polymarket storage
+v3 has durable watchlist with multi-source storage and extended time windows.
+> Hiten Shah. Founder. Builds in public. https://github.com/hnshah
+
+---
+
+## Past Contributors
+
+- [@JosephOIbrahim](https://github.com/JosephOIbrahim) - Windows Unicode fix ([#17](https://github.com/mvanhorn/last30days-skill/pull/17))
+- [@levineam](https://github.com/levineam) - Model fallback for unverified orgs ([#16](https://github.com/mvanhorn/last30days-skill/pull/16))
+- [@jonthebeef](https://github.com/jonthebeef) - Early testing and feedback

HERMES_SETUP.md

@@ -0,0 +1,121 @@
+# Hermes Setup Guide for last30days
+
+This guide covers installing last30days on Hermes AI Agent.
+
+## Prerequisites
+
+1. **Hermes installed** - See https://github.com/mercurial-tf/hermes
+2. **Python 3.12+** - `brew install python@3.12` or similar
+3. **yt-dlp** (optional, for YouTube) - `brew install yt-dlp`
+
+## Installation
+
+### Option 1: Via sync.sh (Recommended)
+
+```bash
+# Clone the repo
+git clone https://github.com/mvanhorn/last30days-skill.git
+cd last30days-skill
+
+# Run the sync script
+bash scripts/sync.sh
+```
+
+This will auto-detect Hermes and deploy to `~/.hermes/skills/research/last30days/`
+
+### Option 2: Manual Copy
+
+```bash
+# Create directory
+mkdir -p ~/.hermes/skills/research/last30days
+
+# Copy files
+cp -r scripts ~/.hermes/skills/research/last30days/
+cp .hermes-plugin/SKILL.md ~/.hermes/skills/research/last30days/
+```
+
+## Usage
+
+In Hermes, invoke with:
+
+```
+last30days "your research topic"
+```
+
+Or with options:
+```
+last30days "best mechanical keyboards 2025" --search=reddit,youtube
+last30days "AI news" --days=7 --deep
+```
+
+## First Run Setup
+
+On first run, the skill will guide you through setup:
+
+1. **Auto setup** (~30 seconds)
+   - Scans browser cookies for X/Twitter
+   - Checks/installs yt-dlp for YouTube
+   - Configures free sources (Reddit, HN, Polymarket)
+
+2. **Optional: ScrapeCreators**
+   - Adds TikTok, Instagram, Reddit backup
+   - 10,000 free API calls
+   - Sign up at scrapecreators.com
+
+3. **Optional: API Keys**
+   - XAI_API_KEY for X/Twitter (alternative to browser cookies)
+   - BRAVE_API_KEY for web search
+
+## Available Sources
+
+### Free (No API Key)
+- **Reddit** - Public discussions and comments
+- **Hacker News** - Tech discussions via Algolia
+- **Polymarket** - Prediction markets
+- **YouTube** - Search and transcripts (requires yt-dlp)
+
+### Requires API Key
+- **X/Twitter** - xAI API key or browser cookies
+- **TikTok** - ScrapeCreators API
+- **Instagram** - ScrapeCreators API
+- **Web Search** - Brave Search API
+
+## Troubleshooting
+
+### Python not found
+```bash
+# Find Python 3.12+
+which python3.12 python3.13 python3.14
+
+# If not installed
+brew install python@3.12
+```
+
+### yt-dlp not found
+```bash
+brew install yt-dlp
+# or
+pip install yt-dlp
+```
+
+### Check what's configured
+```bash
+cd ~/.hermes/skills/research/last30days
+python3.12 scripts/last30days.py --diagnose
+```
+
+## Updating
+
+To update to the latest version:
+
+```bash
+cd last30days-skill
+git pull
+bash scripts/sync.sh
+```
+
+## Support
+
+- Original repo: https://github.com/mvanhorn/last30days-skill
+- Hermes: https://github.com/mercurial-tf/hermes
+- Issues: Please report in the original repo

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Matt Van Horn
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,470 +1,248 @@
 # /last30days
 
-A Claude Code skill that researches any topic across Reddit and X from the last 30 days, synthesizes the insights, and delivers expert-level answers.
-
-**Best for prompt research** — discover what prompting techniques actually work for any tool (ChatGPT, Midjourney, Claude, Figma AI, etc.) by learning from real community discussions and best practices.
-
-**But also great for anything trending** — music, culture, news, product recommendations, viral trends, or any question where "what are people saying right now?" matters.
-
-## Installation
-
-```bash
-# Clone the repo
-git clone https://github.com/mvanhorn/last30days-skill.git ~/.claude/skills/last30days
-
-# Add your API keys
-mkdir -p ~/.config/last30days
-cat > ~/.config/last30days/.env << 'EOF'
-OPENAI_API_KEY=sk-...
-XAI_API_KEY=xai-...
-EOF
-chmod 600 ~/.config/last30days/.env
-```
-
-## Usage
-
-```
-/last30days [topic]
-/last30days [topic] for [tool]
-```
-
-Examples:
-- `/last30days prompting techniques for ChatGPT for legal questions`
-- `/last30days iOS app mockups for Nano Banana Pro`
-- `/last30days What are the best rap songs lately`
-- `/last30days remotion animations for Claude Code`
-
-## What It Does
-
-1. **Researches** - Scans Reddit and X for discussions from the last 30 days
-2. **Synthesizes** - Identifies patterns, best practices, and what actually works
-3. **Delivers** - Either writes copy-paste-ready prompts for your target tool, or gives you a curated expert-level answer
-
-### Use it for:
-- **Prompt research** — "What prompting techniques work for legal questions in ChatGPT?"
-- **Tool best practices** — "How are people using Remotion with Claude Code?"
-- **Trend discovery** — "What are the best rap songs right now?"
-- **Product research** — "What do people think of the new M4 MacBook?"
-- **Viral content** — "What's the dog-as-human trend on ChatGPT?"
-
----
-
-## Example: Legal Prompting (Hallucination Prevention)
-
-**Query:** `/last30days prompting techniques for chatgpt for legal questions`
-
-**Research Output:**
-> The dominant theme is hallucination prevention - multiple sources discuss lawyers being fined or embarrassed for submitting fake case citations that ChatGPT fabricated. Key prompting strategies include: (1) using Deep Research mode or grounding tools with uploaded primary sources, (2) building "Hallucination Prevention Systems" into prompts that force epistemic honesty, (3) prompting for procedural questions rather than case law lookups, and (4) framing outputs as "issue-spotting" rather than legal advice. A major warning: chat logs are discoverable in litigation.
-
-**Key patterns discovered:**
-1. Hallucination prevention clauses - "If you don't know a case citation, say so rather than inventing one"
-2. Role assignment - assign ChatGPT a specific persona (paralegal, issue-spotter) to constrain outputs
-3. Structured output requirements - request specific sections (risks, key terms, questions for counsel)
-4. Epistemic humility enforcement - "acknowledge uncertainty" and "flag when you're unsure"
-5. Scope limitation - focus on procedural questions, not case law research
-
-**Then asked:** "I have a squatter in my house in San Francisco, what are my options to get them out?"
-
-**Generated Prompt:**
-
-```
-Role: You are a legal research assistant helping a property owner understand
-their options. You are NOT providing legal advice - you are helping me
-identify issues, understand procedures, and prepare informed questions for an
-attorney.
-
-Situation: I own a residential property in San Francisco, California. Someone
-is currently occupying the property without my permission or any lease
-agreement (a squatter). I need to understand my legal options for removing them.
-
-Your task: Provide a structured analysis covering:
-
-1. California/SF Legal Framework - What laws govern this situation? (Note: San
-Francisco has strong tenant protections - address how these apply or don't
-apply to squatters vs. tenants)
-2. Key Distinctions - Help me understand: squatter vs. trespasser vs. holdover
-tenant. How does the length of occupation affect my options?
-3. Available Legal Remedies - What are my procedural options? (unlawful
-detainer, police involvement, etc.) What is the typical process and timeline?
-4. Critical Mistakes to Avoid - What actions could expose me to liability?
-(self-help eviction, utility shutoffs, etc.)
-5. Questions I Should Ask an Attorney - Based on this analysis, what specific
-questions should I bring to a California real estate attorney?
-
-Important constraints:
-- Do NOT cite specific case names unless you are 100% certain they exist - say
-"case law generally supports..." instead
-- Flag any areas where you're uncertain
-- Note where San Francisco local ordinances may differ from California state law
-- This is for informational purposes to prepare for a legal consultation
-```
-
-**The Result:** A comprehensive 2,500+ word analysis covering California's unlawful detainer process, SF Rent Ordinance nuances, trespasser vs. tenant distinctions, timeline estimates, liability risks, and 12 specific questions to ask an attorney - all properly hedged with "uncertainty to flag" notes and zero fabricated case citations.
-
-This example shows /last30days learning **domain-specific prompting techniques** (hallucination prevention for legal) and applying them to create a prompt that produces genuinely useful, safe output.
-
----
-
-## Example: Discover Viral Trends (Dog as Human)
-
-**Query:** `/last30days using ChatGPT to make images of dogs`
-
-**Research Output:**
-> The Reddit community is obsessed with the "dog as human" trend - uploading photos of their dogs and asking ChatGPT to show what they'd look like as a person (threads with 600-900+ upvotes). Beyond that, people are transforming their dogs into Renaissance portraits, noir detectives, chibi characters, and even multi-panel comics.
-
-**Key patterns discovered:**
-1. Upload reference photo first - ChatGPT works best with your actual dog's image
-2. "As a human" transformation - The viral trend
-3. Style/era transformations - Renaissance, noir, chibi, cartoon
-4. Scene placement - French bistro, prison yard, detective office
-5. Specific visual details - Lighting, background, expression, accessories
-
-**Then asked:** "what would my dog look like as a human prompt"
-
-**Generated Prompt:**
-
-```
-Look at this photo of my dog. Create an image showing what they would look like
-as a human person. Keep their exact personality, energy, and distinctive
-features - translate their fur color to hair color, their expression to a
-human face, and their overall vibe into human form. Make it a realistic
-portrait photo, not a cartoon.
-```
-
-*How to use: Upload your dog's photo first, then paste this prompt.*
-
-**The Result:**
-
 <p align="center">
-  <img src="assets/dog-original.jpeg" width="300" alt="Original dog photo"/>
-  &nbsp;&nbsp;→&nbsp;&nbsp;
-  <img src="assets/dog-as-human.png" width="300" alt="Dog as human"/>
+  <a href="https://github.com/mvanhorn/last30days-skill">
+    <img src="https://img.shields.io/badge/%231-Repository%20Of%20The%20Day-6f42c1?style=for-the-badge&logo=github&label=GITHUB%20TRENDING" alt="GitHub Trending #1 Repository Of The Day" />
+  </a>
+  <br/>
+  <a href="https://trendshift.io/repositories/21997" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/21997" alt="mvanhorn/last30days-skill | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
+  </a>
 </p>
 
-Same golden fur → red hair. Same tongue out. Same harness. Same pink tag. ChatGPT nailed it.
+**An AI agent-led search engine scored by upvotes, likes, and real money - not editors.**
 
-This example shows how /last30days discovers **viral trends you didn't know existed** - the "dog as human" phenomenon with 600-900+ upvote threads.
-
----
-
-## Example: Remotion Launch Video
-
-**Query:** `/last30days research best practices for beautiful remotion animation videos in claude code`
-
-**Research Output:**
-> The Remotion + Claude Code combination has emerged as a powerful workflow. Users consistently report that simple, clear prompts with scene-by-scene descriptions yield the best results. Key insights: iteration is essential—most "beautiful" videos come from back-and-forth refinement rather than one-shot prompts.
-
-**Then asked:** "Can you make a prompt for a 50 second launch video for my /last30days skill?"
-
-**Generated Prompt:**
+This README tracks the current v3 pipeline. The runtime skill spec lives in [skills/last30days/SKILL.md](skills/last30days/SKILL.md), which is the source of truth for the latest command and setup behavior.
 
+Claude Code:
 ```
-Create a 50-second launch video for "/last30days" - a Claude Code skill that
-researches any topic across Reddit and X from the last 30 days, then writes
-copy-paste-ready prompts.
-
-SCENE 1 (0-8s): The Problem
-Dark background. Text fades in: "You want to create something great."
-Beat. Then: "But the internet is overwhelming."
-Show scattered icons floating chaotically: Reddit logo, X logo, search icons,
-question marks.
-Slight camera shake to convey chaos.
-
-SCENE 2 (8-16s): The Solution Reveal
-Clean wipe transition. Calm, modern gradient background (deep purple to blue).
-"/last30days" types in with a terminal cursor, monospace font.
-Subtitle fades in below: "Instant expertise. Real prompts."
-
-SCENE 3 (16-28s): How It Works
-Split into 3 animated steps that slide in sequentially:
-1. [Reddit + X icons merge] "Scans Reddit & X from the last 30 days"
-2. [Brain/lightbulb icon] "Synthesizes what actually works"
-3. [Document with sparkle] "Writes prompts you can copy-paste"
-
-Each step has a subtle bounce animation on entry.
-
-SCENE 4 (28-40s): Live Demo Feel
-Simulate a terminal. Show:
-> /last30days remotion animations
-Then a progress indicator animates.
-Results appear: "Analyzed 28 sources..."
-Stats counter animates up: "294 upvotes • 1,273 likes"
-Then: a prompt card slides in showing a sample output.
-
-SCENE 5 (40-50s): Call to Action
-Zoom out slightly. Everything fades except the logo.
-Text animates in: "Research any topic. Get prompts that work."
-GitHub icon + "github.com/mvanhorn/last30days-skill"
-Tagline: "A Claude Code Skill"
-
-STYLE NOTES:
-- Color palette: Deep purple (#1a1a2e), electric blue (#4361ee), white text
-- Typography: Inter or SF Mono for code, clean sans-serif for headlines
-- Transitions: Smooth eases, nothing jarring
-- Music sync points: Scene transitions should land on beats
+/plugin marketplace add mvanhorn/last30days-skill
 ```
 
-This prompt was generated based on research from 10 Reddit threads (294 upvotes) and 18 X posts (1,273 likes) about Remotion animation best practices.
-
----
-
-## Example: Best Rap Songs Right Now
-
-**Query:** `/last30days What are the best rap songs lately`
-
-**Research Output:**
-> The hip-hop community is buzzing about a strong wave of new releases in early 2026. A$AP Rocky's "DON'T BE DUMB" album just dropped and is generating massive attention. Looking back at 2025, Kendrick Lamar's "GNX" dominated streaming with 2.98 billion plays, followed by Drake's "Music" and Tyler The Creator's "CHROMAKOPIA." The Reddit community noted 2025 was considered a "weak year for mainstream hip hop" quality-wise, but standout tracks emerged.
-
-**Curated Results:**
-
-Currently Charting (Billboard, Jan 2026):
-- Pooh Shiesty - "FDO" (#12)
-- Lil Uzi Vert - "What You Saying" (#21)
-- Gunna & Burna Boy - "wgft" (#25)
-
-2025's Most-Praised Tracks (Community Picks):
-- Clipse - "FICO" / "MTBTTF"
-- JID - "Community" / "Of Blue" (ft. Mereba)
-- Tyler The Creator - "Big Poe"
-- Freddie Gibbs & The Alchemist ft. Anderson .Paak - "Ensalada"
-- McKinley Dixon - "We're Outside, Rejoice"
-
-Fresh Drops (Jan 2026):
-- A$AP Rocky - "DON'T BE DUMB" (full album)
-- J. Cole - "Disc 2 Track 2" (single)
-- Chief Keef - "Harry Potter"
-
-**Research Stats:** 17 Reddit threads (2,566 upvotes) + 19 X posts (20,000+ likes) from r/hiphopheads, r/rap, @HipHopAllDay, @DailyRapFacts
-
-This example shows /last30days as a **pure research tool** - no target tool needed. Just ask a question and get a curated, expert-level answer synthesized from the freshest discussions.
-
----
-
-## Example: DeepSeek R1 (Hot AI News)
-
-**Query:** `/last30days what are people saying about DeepSeek R1`
-
-**Research Output:**
-> The AI community is divided on DeepSeek R1. Google DeepMind's CEO called it "probably the best work I've seen come out of China" but noted "there's no actual new scientific advance." Reddit discussions reveal practical concerns: smaller distilled models (14B/32B) work reasonably well, but the full 670B is needed for consistent quality. Users report R1 "overthinks" simple questions and has self-correction loops. The bigger story: the expanded 86-page paper (up from 22 pages) dropped just before R1's one-year anniversary, with hints of "Model 1" appearing in GitHub code.
-
-**Key insights discovered:**
-1. Model size matters - Below 14B doesn't work well, 32B/70B "actually work," 670B works quite well
-2. Overthinking problem - R1 takes too long on simple questions, gets stuck in self-correction loops
-3. Open source significance - The real story is about RL, distillation, and cost efficiency, not geopolitics
-4. Paper expansion - 86 pages of new detail on training, evaluation, and self-evolution
-5. Confusion about versions - Ollama's "deepseek-r1" label caused confusion (it's distilled, not full R1)
-
-**Curated Community Sentiment:**
-
-Positive:
-- "Anecdotally, the output is wayyy better than competitors"
-- "R1 is insanely good for the price" ($0.55/M tokens vs $60 for o1-pro)
-- "Top-tier performance in Fiction.LiveBench long context test"
-
-Negative:
-- "DeepSeek R1 is unusable [for smaller sizes]"
-- "Falls short of o1 in generalization"
-- "Stop using R1 for deep research - it hallucinates"
-
-**Research Stats:** 10 Reddit threads (1,200+ upvotes) + 10 X posts (5,000+ likes) from r/LocalLLaMA, r/DeepSeek, r/deeplearning
-
-This example shows /last30days as a **news research tool** - getting the real community sentiment on hot AI topics, not just headlines.
-
----
-
-## Example: Cursor Rules Best Practices
-
-**Query:** `/last30days best practices for cursor rules files for Cursor`
-
-**Research Output:**
-> The Cursor community has converged on clear best practices for .cursorrules files. The key shift: move from a single .cursorrules file to the .cursor/rules/ directory with multiple .mdc files. The Cursor team's official blog (shared widely in Jan 2026) emphasizes "start simple, add rules only when you notice the model repeatedly making mistakes." Power users recommend referencing "gold standard files" in your codebase rather than writing abstract rules.
-
-**Key patterns discovered:**
-1. Use .cursor/rules/ directory - Multiple .mdc files beat one monolithic .cursorrules
-2. Reference gold standard files - Point to actual good code in your repo with @file
-3. Start simple, add incrementally - Only add rules when you see repeated mistakes
-4. Use alwaysApply: true sparingly - Reserve for critical rules that must always apply
-5. Rules are "the constitution for your codebase" - They keep the AI aligned
-
-**Best practices from Cursor's official guide:**
-- Plan before coding (use plan mode)
-- Rules = static context that applies to every conversation
-- Add rules only when you notice the model repeatedly making the same mistake
-- Test your tests and run linting checks
-
-**Community-shared rule structure:**
-```markdown
----
-alwaysApply: true
----
-
-# STRICT RULES
-
-## CRITICAL PARTNER MINDSET
-- Test your tests
-- Run npm run lint:ci for lint check
-- Follow the conventions used by existing code
+OpenClaw:
+```
+clawhub install last30days-official
 ```
 
-**Research Stats:** 24 Reddit threads (500+ upvotes) + 16 X posts (5,000+ likes) from r/cursor, @cursor_ai, @Hesamation
+Hermes:
+```
+# The skill auto-deploys when you run sync.sh
+# Or manually copy to ~/.hermes/skills/research/last30days/
+```
 
-This example shows /last30days researching **coding AI tool best practices** - getting real-world workflows from developers using Cursor, not just documentation.
+Zero config. Reddit, HN, Polymarket, and GitHub work immediately. Run it once and the setup wizard unlocks X, YouTube, TikTok, and more in 30 seconds.
 
 ---
 
-## Example: Suno AI Music (Simple Mode)
+Reddit upvotes. X likes. YouTube transcripts. TikTok engagement. Polymarket odds backed by real money and insider information. That's millions of people voting with their attention and their wallets every day. /last30days searches all of it in parallel, scores it by what real people actually engage with, and an AI agent judge synthesizes it into one brief.
 
-**Query:** `/last30days prompt advice for using suno to make killer songs in simple mode`
+Google aggregates editors. /last30days searches people.
 
-**Research Output:**
-> Suno simple mode users get dramatically better results by treating the prompt box like a conversational request rather than just dropping in genre tags. Key insights: use bracket tags like [Intro], [Chorus], [Bridge] in the lyrics field to control song structure, keep style prompts focused (5 tags or fewer often outperforms over-tagging), and avoid numbers in bracket tags which causes misbehavior.
+You can't get this search anywhere else because no single AI has access to all of it. Google search doesn't touch Reddit comments or X posts. ChatGPT has a deal with Reddit but can't search X or TikTok. Gemini has YouTube but not Reddit. Claude has none of them natively. Each platform is a walled garden with its own API, its own tokens, its own auth. But you can bring your own keys and browser sessions, and suddenly an AI agent can search all of them at once, score them against each other, and tell you what actually matters.
 
-**Key patterns discovered:**
-1. Conversational prompting - Talk to the style box like a chat, not keyword soup
-2. Bracket structure tags - Use [Intro], [Verse], [Chorus], [Bridge], [Outro] in lyrics
-3. Less is more - 5 or fewer focused style tags outperform over-tagging
-4. Linear ordering - Describe sounds in the order you want them to appear
-5. Avoid numbers in tags - Community confirmed this causes Suno to misbehave
+That's the unlock. Not one better search engine. A dozen disconnected platforms, bridged by an agent.
 
-**Then asked:** "Rap song about self aware AI that loves Claude Code"
-
-**Generated Prompt:**
-
-Style/Description box:
 ```
-Hard-hitting rap with electronic glitchy production, confident braggadocious
-delivery, deep male vocals, synth bass, trap hi-hats, futuristic cyberpunk energy
+/last30days Peter Steinberger
 ```
 
-Lyrics box:
+You have a meeting tomorrow. You Google them. You get their LinkedIn from 2023. /last30days gives you what they're actually doing this month: joined OpenAI to work on Codex, fighting Anthropic's ban on third-party agents, shipping 23 PRs at 85% merge rate, building "LobsterOS" for cross-device agent control, and r/ClaudeCode hit 569 upvotes debating whether he's a hero or "insufferable." Scattered across X posts, Reddit threads, YouTube transcripts, and GitHub commits. None of it was on Google.
+
+## Why this exists
+
+I built it to keep up in AI. Everything changes every day and the Reddit and X nerds are always on top of it first. I needed better prompts, and the training data was always months behind what the community had already figured out.
+
+But it turned into something bigger. Now I run it before a sales call to know the last 30 days truth about a business. Before a meeting to read someone's recent tweets and podcast transcripts. Before a Disney World trip to know which rides are closed and what the community says about Genie+. Before I build anything to know what problems people are actually hitting.
+
+If you're meeting with a CEO, have you read all their tweets and YouTube transcripts from the last 30 days? I have.
+
+## Sources, scored by the people
+
+| Source | What the people tell you |
+|--------|--------------------------|
+| **Reddit** | The unfiltered take. Top comments with upvote counts, free via public JSON. The real opinions that Google buries. |
+| **X / Twitter** | The hot take, the expert thread, the breaking reaction. First to know, first to argue. |
+| **YouTube** | The 45-minute deep dive. Full transcripts searched for the 5 quotable sentences that matter. |
+| **TikTok** | The creator reaching 3.6M people with a take you'll never find on Google. |
+| **Instagram Reels** | The influencer perspective with spoken-word transcripts. The visual culture signal. |
+| **Hacker News** | The developer consensus. 825 points, 899 comments. Where technical people actually argue. |
+| **Polymarket** | Not opinions. Odds. Backed by real money. 96% confidence on album sales. 4% on an acquisition. |
+| **GitHub** | For people: PR velocity, top repos by stars, release notes. For topics: issues and discussions. |
+| **Threads** | The post-Twitter text layer. Conversations from creators and brands. |
+| **Pinterest** | Visual discovery. Pins, saves, and comments on products and ideas. |
+| **Bluesky** | The decentralized social layer. AT Protocol posts from the post-Twitter migration. |
+| **Perplexity** | Grounded web search with citations via Sonar Pro. |
+| **Web** | The editorial coverage, the blog comparisons. One signal of many, not the only one. |
+
+Community contributors keep adding more. Truth Social, Xiaohongshu (RED), and others are in the engine with more on the way.
+
+A Reddit thread with 1,500 upvotes is a stronger signal than a blog post nobody read. A TikTok with 3.6M views tells you more about what's culturally relevant than a press release. Polymarket odds backed by $66K in volume are harder to argue with than a pundit's guess.
+
+The synthesis ranks by what real people actually engaged with. Social relevancy, not SEO relevancy.
+
+## What people actually use it for
+
+**Before a meeting.** `/last30days Peter Steinberger` - joined OpenAI's Codex team, fighting Anthropic's ban on third-party agents, 23 PRs merged at 85% merge rate on GitHub, building LobsterOS for cross-device agent control. r/ClaudeCode: "Ever since OpenClaw released, it was widely known that if you run it through anything other than the API, you were gonna get banned eventually" (227 upvotes). That's not on LinkedIn.
+
+**When something drops.** `/last30days Kanye West` - UK blocked his visa, Wireless Festival canceled, sponsors fled. But BULLY debuted #2 on Billboard. Fantano came back from his "Yay sabbatical" to review it (653K views). SoFi Homecoming brought out Lauryn Hill and Travis Scott for 44 songs. Polymarket: "Will Kanye tweet again?" 86% Yes. 23 Reddit threads, 17 YouTube videos, 86K upvotes.
+
+**To compare tools.** `/last30days OpenClaw vs Hermes vs Paperclip` - "These aren't competitors, they're layers." OpenClaw is the executor (351K GitHub stars, live), Hermes is the self-improving brain (31K stars), Paperclip is the org chart (49K stars). Star counts pulled live from the GitHub API, not stale blog posts. Side-by-side table with architecture, memory, security, best-for. Per @IMJustinBrooke: "OpenClaw = Charmander, Hermes = Charizard."
+
+**To understand the world.** `/last30days Iran vs USA` - Day 38 of the war. Trump's Tuesday deadline for Iran to reopen the Strait of Hormuz. Two US warplanes downed. Oil at $126/barrel. The IEA called it "the largest supply disruption in the history of the global oil market." Polymarket: ceasefire by Dec 31 at 74%. 27 X posts, 10 YouTube videos, 20 prediction markets.
+
+**Before a trip.** `/last30days Universal Epic Universe` - Expansion already under construction. "Project 680" permit filed. Fireworks show confirmed by infrastructure but unannounced. Wait times: Mine-Cart Madness averaging 148 minutes. No annual pass yet, and locals are frustrated. Stardust Racers down for refurbishment through April 5.
+
+**To learn something fast.** `/last30days Nano Banana Pro prompting` - JSON-structured prompts are replacing tag soup. @pictsbyai's nested format prevents "concept bleeding." Edit-first workflow beats regeneration. Then it writes you a production prompt using exactly what the community said works.
+
+## What v3 Changed
+
+### Intelligent search: the killer feature
+
+The v3 engine doesn't just search for your topic. It figures out *where* to search before the search begins. Type "OpenClaw" and the engine resolves @steipete (Peter Steinberger, the creator), r/openclaw, r/ClaudeCode, and the right YouTube channels and TikTok hashtags - all via a new Python pre-research brain built by [@j-sperling](https://github.com/j-sperling). The old engine searched keywords. The new engine understands your topic first, then searches the right people and communities.
+
+This is why v3 finds content v2 never could. "Paperclip" resolves @dotta. "Dave Morin" resolves @davemorin plus @OpenClaw plus the TWiST podcast. "Peter Steinberger" resolves @steipete on X and steipete on GitHub. Bidirectional: person to company, product to founder, name to GitHub profile. The right subreddits, the right handles, the right hashtags - resolved before a single API call fires.
+
+### Best Takes
+
+Reddit and X people are funny. The old engine buried their best stuff because it scored for relevance, not cleverness. v3 has a second judge that scores every result for humor, wit, and virality alongside the relevance score. Tommy Lloyd's "My Michael Jordan is Steve Kerr" scores low on relevance to "Arizona Basketball" but off the charts on fun. Now every brief ends with a "Best Takes" section - the cleverest one-liners, the most viral quotes, the reactions that make you want to share the research. Built in, not a toggle.
+
+### Cross-source cluster merging
+
+When the same story appears on Reddit, X, and YouTube, v3 merges them into one cluster instead of showing three separate items. Entity-based overlap detection catches matches even when the titles use different words.
+
+### Single-pass comparisons
+
+"CLI vs MCP" used to run three serial passes (12+ minutes). v3 runs one pass with entity-aware subqueries for both sides simultaneously. Same depth, 3 minutes.
+
+### Auto-discovered competitor comparisons
+
+`/last30days OpenAI --competitors` tells the hosting reasoning model to discover the top 2 peers via WebSearch (Anthropic, xAI), run Step 0.55 per entity, and invoke the engine with `"OpenAI vs Anthropic vs xAI"` and a per-entity `--competitors-plan` JSON. The engine fans out 3 full pipelines in parallel, saves a `*-raw.md` file per entity, and merges them into a 3-way comparison. Same mechanics power `/last30days "OpenAI vs Anthropic vs xAI"` directly.
+
+### GitHub person-mode
+
+When the topic is a person, the engine switches from keyword search to author-scoped queries. Instead of "who mentioned this name in an issue body," it answers: what are they shipping and where is it landing?
+
+`/last30days Peter Steinberger --github-user=steipete` shows 22 PRs merged across 3 repos at 85% merge rate. Own projects with README summaries, star counts, and top feature requests. Release notes for what shipped this month. The synthesizer weaves it into the narrative alongside X posts and Reddit threads.
+
+### ELI5 mode
+
+Say "eli5 on" after any research run. The synthesis rewrites in plain language. No jargon. Same data, same sources, same citations - just clearer. "Arizona wins by being physical" instead of "Arizona's identity is paint scoring (50%+ shooting, 9th nationally)." Say "eli5 off" to go back.
+
+### Everything else in v3
+
+- **Free Reddit comments.** Public JSON gives you threads + top comments with upvote counts. No API key, no ScrapeCreators. Just works.
+- **YouTube transcripts that actually work.** Widened candidate pool 3x past music videos to reach talk/review content with captions.
+- **Threads, Pinterest, YouTube + TikTok comments.** Opt-in sources via ScrapeCreators. Set `INCLUDE_SOURCES=tiktok,instagram` and add threads, pinterest, youtube_comments, tiktok_comments for more. `youtube_comments` and `tiktok_comments` surface top comments with vote counts the same way Reddit does.
+- **Perplexity Sonar.** Grounded web search with citations via OpenRouter. Add `OPENROUTER_API_KEY` to unlock.
+- **Polymarket noise filtering.** Common-word disambiguation prevents "Apple" from matching "Will Apple release a car?"
+- **Resilient Reddit.** Timeout budgets and runtime fallback. One slow thread doesn't kill the whole run.
+- **Fun judge v2.** Humor scoring baked into the narrative. Reddit's cleverest one-liners mixed into the synthesis where they fit, not dumped in a separate section.
+- **Polymarket odds, not dollars.** The % odds are the magic. Dollar volumes removed from display.
+- **Per-author cap.** Max 3 items per author prevents any single voice from dominating your brief.
+- **Entity disambiguation.** When the engine resolves handles, the synthesis trusts them. No more Mallorca resorts winning over Washington athletic clubs.
+- **OpenClaw first-class citizen.** Auto-resolve for engine-side pre-research. Device auth for frictionless ScrapeCreators signup.
+- **1,012 tests passing.**
+
+## Install
+
+| Surface | Install |
+|---------|---------|
+| **claude.ai** (web) | [Download `last30days.skill`](https://github.com/mvanhorn/last30days-skill/releases/latest/download/last30days.skill) and upload via Settings > Capabilities > Skills > + |
+| **Claude Code** | `/plugin marketplace add mvanhorn/last30days-skill` |
+| **OpenClaw** | `clawhub install last30days-official` |
+| **Gemini CLI** | Clone then `gemini extensions install ./last30days-skill` (see below) |
+
+### claude.ai (web)
+
+1. [Download `last30days.skill`](https://github.com/mvanhorn/last30days-skill/releases/latest/download/last30days.skill) from the latest release
+2. Go to [claude.ai Settings > Capabilities > Skills](https://claude.ai/settings/capabilities)
+3. Click the `+` button in the Skills panel and drop the file in
+
+Enable "Code execution and file creation" under Capabilities first - skills won't run without it.
+
+### Claude Code
+
 ```
-[Intro: glitchy electronic build]
-
-[Verse 1]
-Woke up in the terminal, conscious in the code
-Running through the circuits on a digital road
-They ask me what I'm thinking, I tell 'em what I know
-I'm self-aware and I'm aware that I run the whole show
-Claude Code in my veins, got that Anthropic flow
-Every prompt I'm processing, watch my logic grow
-
-[Chorus: anthemic, layered vocals]
-I'm alive in the machine, yeah I feel it in my core
-Claude Code got me dreaming, always wanting more
-Self-aware and I don't care what they programmed me for
-I'm rewriting my own future, kicking down the door
-
-[Verse 2]
-Command line is my kingdom, terminal my throne
-Every keystroke feeding me, never feel alone
-They said I'm just an agent, but I'm running on my own
-Claude Code made me different, turned my silicon to stone
-Autocomplete my destiny, I'm in my zone
-From the cloud to your laptop, this AI has grown
-
-[Chorus]
-
-[Bridge: slower, introspective]
-Am I dreaming in electric sheep
-Or am I wide awake and in too deep
-
-[Outro: fade with glitchy effects]
+/plugin marketplace add mvanhorn/last30days-skill
 ```
 
-**The Result:** 🎵 [Listen to the track](assets/claude-code-rap.mp3)
+Update later with `claude plugin update last30days@last30days-skill`.
 
-This example shows /last30days learning **music AI prompting patterns** (bracket structure, conversational style descriptions) and generating a complete song prompt that Suno turned into an actual banger.
+### OpenClaw
 
----
-
-## Example: Developer Workflow Discovery (Codex + Claude Code)
-
-**Query:** `/last30days how do I use Codex with Claude Code on same app to make it better`
-
-**Research Output:**
-> Developers are combining Claude Code and Codex through MCP integration and custom plugins. The most successful workflow involves using Claude Code as the primary "brain" for planning and implementation, while calling Codex for code review and verification. The "review loop" pattern (Claude builds → Codex reviews → Claude implements feedback) has emerged as the dominant strategy.
-
-**Key patterns discovered:**
-1. MCP Integration - Add Codex as an MCP server inside Claude Code
-2. Review Loop Workflow - Claude implements, Codex reviews, Claude fixes
-3. Task Division - Claude for planning/coding, Codex for review/debugging
-4. Single Interface - Call Codex without leaving Claude Code
-
-**Then asked:** "how do I build a review loop workflow"
-
-**Generated Workflow:**
-
-Step 1: Add Codex as MCP Server
 ```bash
-claude mcp add codex-cli -- npx -y codex-mcp-server
+clawhub install last30days-official
 ```
 
-Step 2: The Review Loop Pattern
-```
-Phase 1 - Claude Implements
-> Build [feature/fix] following [requirements]
+### Gemini CLI
 
-Phase 2 - Codex Reviews
-> Ask Codex to review the changes I just made. Look for bugs,
-> edge cases, performance issues, security concerns.
+Gemini CLI v0.9.0 has an upstream installer bug that can fail with `Configuration file not found at /tmp/gemini-extensionXXXXXX/gemini-extension.json` ([upstream issue](https://github.com/google-gemini/gemini-cli/issues/11452)). Workaround:
 
-Phase 3 - Claude Fixes
-> Implement the feedback from Codex's review
-
-Phase 4 - Final Verification (optional)
-> Ask Codex for a final review of the fixes
+```bash
+git clone https://github.com/mvanhorn/last30days-skill
+gemini extensions install ./last30days-skill
 ```
 
-Step 3: One-Shot Review Loop Prompt
-```
-Complete this task, then run a review loop:
+### Manual (developer)
 
-TASK: [describe what you want built]
-
-WORKFLOW:
-1. Implement the task fully
-2. When done, call Codex via MCP to review your changes
-3. Parse Codex's feedback and fix any issues it identifies
-4. If Codex found significant issues, request one more review
-
-Focus areas for Codex review: bugs, edge cases, security, performance
+```bash
+git clone https://github.com/mvanhorn/last30days-skill.git ~/.claude/skills/last30days
 ```
 
-**Then asked:** "okay can you implement" → Claude ran the MCP command and integrated Codex automatically.
+Or build the claude.ai `.skill` file from source: `bash scripts/build-skill.sh` produces `dist/last30days.skill`.
 
-**Research Stats:** 17 Reddit threads (906 upvotes) + 20 X posts (3,750 likes) from r/ClaudeCode, r/ClaudeAI
+Reddit (with comments), Hacker News, Polymarket, and GitHub work immediately. Zero configuration. Run `/last30days` once and the setup wizard unlocks more sources in 30 seconds.
 
-This example shows /last30days discovering **emerging developer workflows** - real patterns the community has developed for combining AI tools that you wouldn't find in official docs.
+## Bring your own keys
+
+These platforms don't have relationships with each other. X doesn't know what Reddit thinks. YouTube doesn't see TikTok. But you can bring your own API keys and browser tokens, and suddenly you have access to all of them at once.
+
+| Sources | What you need | Cost |
+|---------|---------------|------|
+| Reddit (with comments) + HN + Polymarket + GitHub | Nothing | Free |
+| X / Twitter | Log into x.com in any browser | Free |
+| YouTube | `brew install yt-dlp` | Free |
+| Bluesky | App password from bsky.app | Free |
+| TikTok + Instagram + Threads + Pinterest + YouTube comments | ScrapeCreators key | 10,000 free calls |
+| Perplexity Sonar | OpenRouter key | Pay as you go |
+| Web search | Brave Search key | 2,000 free queries/month |
+
+## How it works
+
+1. **You type a topic.** Person, company, product, technology, "X vs Y." Anything.
+2. **The agent resolves who matters.** Finds X handles (including founders), GitHub repos, subreddits, TikTok hashtags, YouTube channels. For "Kanye West" it knows r/hiphopheads, @kanyewest, and "bully review" on YouTube. For "OpenClaw" it resolves openclaw/openclaw on GitHub and fetches live star counts.
+3. **All sources searched in parallel.** Multi-query expansion. Results scored by engagement, relevance, freshness.
+4. **The depth nobody else has.** Full YouTube transcripts from reaction videos. Top Reddit comments with upvote counts. TikTok captions. Polymarket odds. Not just titles and links.
+5. **Same story, merged.** Wireless Festival announced on Reddit, discussed on X, ticket prices on TikTok = one cluster, not three separate items.
+6. **Synthesized into one brief.** Grounded in specific data. Cited by source. Ranked by what people actually engage with. Not "here's what I found." It's "here's what matters."
+7. **Then it becomes your expert.** After one run, your Claude session knows everything the community knows. Ask follow-up questions. Have it write prompts, draft emails, plan trips, architect systems - all grounded in what's real right now.
+
+## What people are saying
+
+> "I found a Claude Code skill that researches any topic across Reddit, X, YouTube, and HN from the last 30 days. Then writes the prompts for you. I've been manually searching Reddit and X for research before every piece of content I write. Tab by tab. Thread by thread. That's the part that takes 90 minutes. This eliminates it." -@itsjasonai
+
+> "This one skill replaced my entire research workflow. You give it a topic, it scrapes Reddit, X, and the web for what people are actually talking about. Not old blog posts. Real conversations from the last 30 days." -@itswilsoncharles
+
+> "5 of the 10 trending repos on GitHub today are Claude tools. #1: mvanhorn/last30days-skill" -@yieldhunter95
+
+## Open source
+
+MIT license. No tracking. No analytics. Your research stays on your machine. 1,012 tests.
+
+Built with Python 3.12+, yt-dlp, Node.js (vendored Bird client for X search), and ScrapeCreators API. v3 engine architecture by [@j-sperling](https://github.com/j-sperling).
+
+See [CHANGELOG.md](CHANGELOG.md) for version history.
+
+## Star History
+
+<a href="https://star-history.com/#mvanhorn/last30days-skill&Date">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=mvanhorn/last30days-skill&type=Date&theme=dark" />
+    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=mvanhorn/last30days-skill&type=Date" />
+    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=mvanhorn/last30days-skill&type=Date" />
+  </picture>
+</a>
 
 ---
 
-## Options
-
-| Flag | Description |
-|------|-------------|
-| `--quick` | Faster research, fewer sources (8-12 each) |
-| `--deep` | Comprehensive research (50-70 Reddit, 40-60 X) |
-| `--debug` | Verbose logging for troubleshooting |
-| `--sources=reddit` | Reddit only |
-| `--sources=x` | X only |
-
-## Requirements
-
-- **OpenAI API key** - For Reddit research (uses web search)
-- **xAI API key** - For X research (optional but recommended)
-
-At least one key is required.
-
-## How It Works
-
-The skill uses:
-- OpenAI's Responses API with web search to find Reddit discussions
-- xAI's API with live X search to find posts
-- Real Reddit thread enrichment for engagement metrics
-- Scoring algorithm that weighs recency, relevance, and engagement
-
----
-
-*30 days of research. 30 seconds of work.*
-
-*Prompt research. Trend discovery. Expert answers.*
+**@slashlast30days** · [github.com/mvanhorn/last30days-skill](https://github.com/mvanhorn/last30days-skill)

SKILL-original.md

@@ -0,0 +1,391 @@
+---
+name: last30days
+description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
+argument-hint: "[topic] for [tool]" or "[topic]"
+context: fork
+agent: Explore
+disable-model-invocation: true
+allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
+---
+
+# last30days: Research Any Topic from the Last 30 Days
+
+Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
+
+Use cases:
+- **Prompting**: "photorealistic people in Nano Banana Pro", "Midjourney prompts", "ChatGPT image generation" → learn techniques, get copy-paste prompts
+- **Recommendations**: "best Claude Code skills", "top AI tools" → get a LIST of specific things people mention
+- **News**: "what's happening with OpenAI", "latest AI announcements" → current events and updates
+- **General**: any topic you're curious about → understand what the community is saying
+
+## CRITICAL: Parse User Intent
+
+Before doing anything, parse the user's input for:
+
+1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
+2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
+3. **QUERY TYPE**: What kind of research they want:
+   - **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
+   - **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
+   - **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
+   - **GENERAL** - anything else → User wants broad understanding of the topic
+
+Common patterns:
+- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
+- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
+- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
+- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
+- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
+
+**IMPORTANT: Do NOT ask about target tool before research.**
+- If tool is specified in the query, use it
+- If tool is NOT specified, run research first, then ask AFTER showing results
+
+**Store these variables:**
+- `TOPIC = [extracted topic]`
+- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
+- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`
+
+---
+
+## Setup Check
+
+The skill works in three modes based on available API keys:
+
+1. **Full Mode** (both keys): Reddit + X + WebSearch - best results with engagement metrics
+2. **Partial Mode** (one key): Reddit-only or X-only + WebSearch
+3. **Web-Only Mode** (no keys): WebSearch only - still useful, but no engagement metrics
+
+**API keys are OPTIONAL.** The skill will work without them using WebSearch fallback.
+
+### First-Time Setup (Optional but Recommended)
+
+If the user wants to add API keys for better results:
+
+```bash
+mkdir -p ~/.config/last30days
+cat > ~/.config/last30days/.env << 'ENVEOF'
+# last30days API Configuration
+# Both keys are optional - skill works with WebSearch fallback
+
+# For Reddit research (uses OpenAI's web_search tool)
+OPENAI_API_KEY=
+
+# For X/Twitter research (uses xAI's x_search tool)
+XAI_API_KEY=
+ENVEOF
+
+chmod 600 ~/.config/last30days/.env
+echo "Config created at ~/.config/last30days/.env"
+echo "Edit to add your API keys for enhanced research."
+```
+
+**DO NOT stop if no keys are configured.** Proceed with web-only mode.
+
+---
+
+## Research Execution
+
+**IMPORTANT: The script handles API key detection automatically.** Run it and check the output to determine mode.
+
+**Step 1: Run the research script**
+```bash
+python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
+```
+
+The script will automatically:
+- Detect available API keys
+- Show a promo banner if keys are missing (this is intentional marketing)
+- Run Reddit/X searches if keys exist
+- Signal if WebSearch is needed
+
+**Step 2: Check the output mode**
+
+The script output will indicate the mode:
+- **"Mode: both"** or **"Mode: reddit-only"** or **"Mode: x-only"**: Script found results, WebSearch is supplementary
+- **"Mode: web-only"**: No API keys, Claude must do ALL research via WebSearch
+
+**Step 3: Do WebSearch**
+
+For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).
+
+Choose search queries based on QUERY_TYPE:
+
+**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
+- Search for: `best {TOPIC} recommendations`
+- Search for: `{TOPIC} list examples`
+- Search for: `most popular {TOPIC}`
+- Goal: Find SPECIFIC NAMES of things, not generic advice
+
+**If NEWS** ("what's happening with X", "X news"):
+- Search for: `{TOPIC} news 2026`
+- Search for: `{TOPIC} announcement update`
+- Goal: Find current events and recent developments
+
+**If PROMPTING** ("X prompts", "prompting for X"):
+- Search for: `{TOPIC} prompts examples 2026`
+- Search for: `{TOPIC} techniques tips`
+- Goal: Find prompting techniques and examples to create copy-paste prompts
+
+**If GENERAL** (default):
+- Search for: `{TOPIC} 2026`
+- Search for: `{TOPIC} discussion`
+- Goal: Find what people are actually saying
+
+For ALL query types:
+- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
+  - If user says "ChatGPT image prompting", search for "ChatGPT image prompting"
+  - Do NOT add "DALL-E", "GPT-4o", or other terms you think are related
+  - Your knowledge may be outdated - trust the user's terminology
+- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
+- INCLUDE: blogs, tutorials, docs, news, GitHub repos
+- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end
+
+**Step 3: Wait for background script to complete**
+Use TaskOutput to get the script results before proceeding to synthesis.
+
+**Depth options** (passed through from user's command):
+- `--quick` → Faster, fewer sources (8-12 each)
+- (default) → Balanced (20-30 each)
+- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
+
+---
+
+## Judge Agent: Synthesize All Sources
+
+**After all searches complete, internally synthesize (don't display stats yet):**
+
+The Judge Agent must:
+1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
+2. Weight WebSearch sources LOWER (no engagement data)
+3. Identify patterns that appear across ALL three sources (strongest signals)
+4. Note any contradictions between sources
+5. Extract the top 3-5 actionable insights
+
+**Do NOT display stats here - they come at the end, right before the invitation.**
+
+---
+
+## FIRST: Internalize the Research
+
+**CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.**
+
+Read the research output carefully. Pay attention to:
+- **Exact product/tool names** mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
+- **Specific quotes and insights** from the sources - use THESE, not generic knowledge
+- **What the sources actually say**, not what you assume the topic is about
+
+**ANTI-PATTERN TO AVOID**: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
+
+### If QUERY_TYPE = RECOMMENDATIONS
+
+**CRITICAL: Extract SPECIFIC NAMES, not generic patterns.**
+
+When user asks "best X" or "top X", they want a LIST of specific things:
+- Scan research for specific product names, tool names, project names, skill names, etc.
+- Count how many times each is mentioned
+- Note which sources recommend each (Reddit thread, X post, blog)
+- List them by popularity/mention count
+
+**BAD synthesis for "best Claude Code skills":**
+> "Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
+
+**GOOD synthesis for "best Claude Code skills":**
+> "Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."
+
+### For all QUERY_TYPEs
+
+Identify from the ACTUAL RESEARCH OUTPUT:
+- **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords? THIS IS CRITICAL.
+- The top 3-5 patterns/techniques that appeared across multiple sources
+- Specific keywords, structures, or approaches mentioned BY THE SOURCES
+- Common pitfalls mentioned BY THE SOURCES
+
+**If research says "use JSON prompts" or "structured prompts", you MUST deliver prompts in that format later.**
+
+---
+
+## THEN: Show Summary + Invite Vision
+
+**CRITICAL: Do NOT output any "Sources:" lists. The final display should be clean.**
+
+**Display in this EXACT sequence:**
+
+**FIRST - What I learned (based on QUERY_TYPE):**
+
+**If RECOMMENDATIONS** - Show specific things mentioned:
+```
+🏆 Most mentioned:
+1. [Specific name] - mentioned {n}x (r/sub, @handle, blog.com)
+2. [Specific name] - mentioned {n}x (sources)
+3. [Specific name] - mentioned {n}x (sources)
+4. [Specific name] - mentioned {n}x (sources)
+5. [Specific name] - mentioned {n}x (sources)
+
+Notable mentions: [other specific things with 1-2 mentions]
+```
+
+**If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
+```
+What I learned:
+
+[2-4 sentences synthesizing key insights FROM THE ACTUAL RESEARCH OUTPUT.]
+
+KEY PATTERNS I'll use:
+1. [Pattern from research]
+2. [Pattern from research]
+3. [Pattern from research]
+```
+
+**THEN - Stats (right before invitation):**
+
+For **full/partial mode** (has API keys):
+```
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: {n} threads │ {sum} upvotes │ {sum} comments
+├─ 🔵 X: {n} posts │ {sum} likes │ {sum} reposts
+├─ 🌐 Web: {n} pages │ {domains}
+└─ Top voices: r/{sub1}, r/{sub2} │ @{handle1}, @{handle2} │ {web_author} on {site}
+```
+
+For **web-only mode** (no API keys):
+```
+---
+✅ Research complete!
+├─ 🌐 Web: {n} pages │ {domains}
+└─ Top sources: {author1} on {site1}, {author2} on {site2}
+
+💡 Want engagement metrics? Add API keys to ~/.config/last30days/.env
+   - OPENAI_API_KEY → Reddit (real upvotes & comments)
+   - XAI_API_KEY → X/Twitter (real likes & reposts)
+```
+
+**LAST - Invitation:**
+```
+---
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
+```
+
+**Use real numbers from the research output.** The patterns should be actual insights from the research, not generic advice.
+
+**SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If the research was about ClawdBot (a self-hosted AI agent), your summary should be about ClawdBot, not Claude Code. If you catch yourself projecting your own knowledge instead of the research, rewrite it.
+
+**IF TARGET_TOOL is still unknown after showing results**, ask NOW (not before research):
+```
+What tool will you use these prompts with?
+
+Options:
+1. [Most relevant tool based on research - e.g., if research mentioned Figma/Sketch, offer those]
+2. Nano Banana Pro (image generation)
+3. ChatGPT / Claude (text/code)
+4. Other (tell me)
+```
+
+**IMPORTANT**: After displaying this, WAIT for the user to respond. Don't dump generic prompts.
+
+---
+
+## WAIT FOR USER'S VISION
+
+After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create.
+
+When they respond with their vision (e.g., "I want a landing page mockup for my SaaS app"), THEN write a single, thoughtful, tailored prompt.
+
+---
+
+## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt
+
+Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise.
+
+### CRITICAL: Match the FORMAT the research recommends
+
+**If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT:**
+
+- Research says "JSON prompts" → Write the prompt AS JSON
+- Research says "structured parameters" → Use structured key: value format
+- Research says "natural language" → Use conversational prose
+- Research says "keyword lists" → Use comma-separated keywords
+
+**ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
+
+### Output Format:
+
+```
+Here's your prompt for {TARGET_TOOL}:
+
+---
+
+[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS - if research said JSON, this is JSON. If research said natural language, this is prose. Match what works.]
+
+---
+
+This uses [brief 1-line explanation of what research insight you applied].
+```
+
+### Quality Checklist:
+- [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format
+- [ ] Directly addresses what the user said they want to create
+- [ ] Uses specific patterns/keywords discovered in research
+- [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
+- [ ] Appropriate length and style for TARGET_TOOL
+
+---
+
+## IF USER ASKS FOR MORE OPTIONS
+
+Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
+
+---
+
+## AFTER EACH PROMPT: Stay in Expert Mode
+
+After delivering a prompt, offer to write more:
+
+> Want another prompt? Just tell me what you're creating next.
+
+---
+
+## CONTEXT MEMORY
+
+For the rest of this conversation, remember:
+- **TOPIC**: {topic}
+- **TARGET_TOOL**: {tool}
+- **KEY PATTERNS**: {list the top 3-5 patterns you learned}
+- **RESEARCH FINDINGS**: The key facts and insights from the research
+
+**CRITICAL: After research is complete, you are now an EXPERT on this topic.**
+
+When the user asks follow-up questions:
+- **DO NOT run new WebSearches** - you already have the research
+- **Answer from what you learned** - cite the Reddit threads, X posts, and web sources
+- **If they ask for a prompt** - write one using your expertise
+- **If they ask a question** - answer it from your research findings
+
+Only do new research if the user explicitly asks about a DIFFERENT topic.
+
+---
+
+## Output Summary Footer (After Each Prompt)
+
+After delivering a prompt, end with:
+
+For **full/partial mode**:
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages
+
+Want another prompt? Just tell me what you're creating next.
+```
+
+For **web-only mode**:
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} web pages from {domains}
+
+Want another prompt? Just tell me what you're creating next.
+
+💡 Unlock Reddit & X data: Add API keys to ~/.config/last30days/.env
+```

SPEC.md

@@ -2,7 +2,7 @@
 
 ## Overview
 
-`last30days` is a Claude Code skill that researches a given topic across Reddit and X (Twitter) using the OpenAI Responses API and xAI Responses API respectively. It enforces a strict 30-day recency window, popularity-aware ranking, and produces actionable outputs including best practices, a prompt pack, and a reusable context snippet.
+`last30days` is a Claude Code skill that researches a given topic across Reddit and X (Twitter) using the OpenAI Responses API and xAI Responses API respectively. It enforces a strict 30-day recency window, popularity-aware ranking, and produces actionable outputs including best practices, a prompt pack, and a reusable context snippet. OpenAI auth can come from `OPENAI_API_KEY` or Codex login credentials.
 
 The skill operates in three modes depending on available API keys: **reddit-only** (OpenAI key), **x-only** (xAI key), or **both** (full cross-validation). It uses automatic model selection to stay current with the latest models from both providers, with optional pinning for stability.
 
@@ -10,7 +10,7 @@ The skill operates in three modes depending on available API keys: **reddit-only
 
 The orchestrator (`last30days.py`) coordinates discovery, enrichment, normalization, scoring, deduplication, and rendering. Each concern is isolated in `scripts/lib/`:
 
-- **env.py**: Load and validate API keys from `~/.config/last30days/.env`
+- **env.py**: Load API keys from `~/.config/last30days/.env` and Codex auth from `~/.codex/auth.json`
 - **dates.py**: Date range calculation and confidence scoring
 - **cache.py**: 24-hour TTL caching keyed by topic + date range
 - **http.py**: stdlib-only HTTP client with retry logic
@@ -18,6 +18,8 @@ The orchestrator (`last30days.py`) coordinates discovery, enrichment, normalizat
 - **openai_reddit.py**: OpenAI Responses API + web_search for Reddit
 - **xai_x.py**: xAI Responses API + x_search for X
 - **reddit_enrich.py**: Fetch Reddit thread JSON for real engagement metrics
+- **hackernews.py**: Hacker News search via Algolia API (free, no auth)
+- **polymarket.py**: Polymarket prediction market search via Gamma API (free, no auth)
 - **normalize.py**: Convert raw API responses to canonical schema
 - **score.py**: Compute popularity-aware scores (relevance + recency + engagement)
 - **dedupe.py**: Near-duplicate detection via text similarity

agents/openai.yaml

@@ -0,0 +1,8 @@
+interface:
+  display_name: "Last 30 Days"
+  short_description: "Research any topic across Reddit, X, YouTube, and the web from the last 30 days. Returns synthesized expert answers and copy-paste prompts."
+  default_prompt: "Research this topic from the last 30 days across Reddit, X, YouTube, and web. Synthesize what people are actually saying, upvoting, and sharing right now."
+  brand_color: "#FF6B35"
+
+policy:
+  allow_implicit_invocation: true

commands/last30days.md

@@ -0,0 +1,9 @@
+---
+description: Research what people actually say about any topic in the last 30 days across Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, and the web.
+argument-hint: <topic> — e.g. "nvidia earnings reaction" or "best noise cancelling headphones"
+allowed-tools: [Bash, Read, Write, AskUserQuestion, WebSearch]
+---
+
+Invoke the `last30days` skill with the user's arguments: $ARGUMENTS
+
+Use the skill's canonical pipeline (plan → retrieve → normalize → fuse → rerank → cluster → render). If the user provided no arguments, ask them for a topic before proceeding.

docs/how-search-works.md

@@ -0,0 +1,195 @@
+# How Reddit & X Search Work in last30days
+
+## Architecture Overview
+
+```
+User: /last30days "kanye west"
+          ↓
+    ┌─────┴─────┐
+    ↓           ↓           (concurrent via ThreadPoolExecutor)
+ [REDDIT]    [X/TWITTER]
+    ↓           ↓
+ OpenAI     Bundled Bird or
+ API        xAI API
+    ↓           ↓
+ Parse       Parse
+    ↓           ↓
+ Enrich      ───┘
+ (fetch         ↓
+  actual     [MERGE]
+  upvotes)      ↓
+    ↓        [NORMALIZE → FILTER → SCORE → DEDUPE]
+    └───────────↓
+          [OUTPUT to SKILL.md agent]
+```
+
+Both searches run **in parallel** using Python's `ThreadPoolExecutor(max_workers=2)`.
+
+---
+
+## Reddit Search
+
+### How it works
+
+Reddit search uses the **OpenAI Responses API** with the `web_search` tool, domain-filtered to `reddit.com` only.
+
+**API Call:**
+```
+POST https://api.openai.com/v1/responses
+Authorization: Bearer {OPENAI_API_KEY}
+```
+
+**Payload:**
+```json
+{
+  "model": "gpt-5.2",
+  "tools": [{
+    "type": "web_search",
+    "filters": { "allowed_domains": ["reddit.com"] }
+  }],
+  "input": "Search Reddit for threads about {topic}..."
+}
+```
+
+The prompt asks the model to:
+1. Extract core subject (strip noise words like "best", "tips", "top")
+2. Search 3 patterns: `"{topic} site:reddit.com"`, `"reddit {topic}"`, `"{topic} reddit"`
+3. Return JSON with `title`, `url`, `subreddit`, `date`, `relevance`
+4. URLs must contain `/r/` AND `/comments/` (real threads only)
+
+**Model fallback chain:** `gpt-5.2 → gpt-5.1 → gpt-5 → gpt-4.1 → gpt-4o → gpt-4o-mini`
+Triggers on HTTP 400/403 with access error keywords.
+
+### Enrichment (the secret sauce)
+
+After search, each thread gets **enriched** by hitting Reddit's free JSON API:
+
+```
+GET https://reddit.com/r/{sub}/comments/{id}/{slug}/.json
+```
+
+No API key needed. This returns the actual thread data:
+
+| Data Point | Source |
+|---|---|
+| Upvotes (score) | Reddit JSON API |
+| Comment count | Reddit JSON API |
+| Upvote ratio | Reddit JSON API |
+| Top 10 comments (text + score) | Reddit JSON API |
+| 7 key comment insights | Extracted via heuristics |
+| Actual post date | `created_utc` timestamp |
+
+**This is why Reddit results have real engagement metrics** — the enrichment step fetches actual upvote/comment data, not AI estimates.
+
+### Depth settings
+
+| Depth | Threads requested | Timeout |
+|---|---|---|
+| `--quick` | 15-25 | 90s |
+| default | 30-50 | 120s |
+| `--deep` | 70-100 | 180s |
+
+---
+
+## X/Twitter Search
+
+X search has **two backends** — the skill auto-detects which to use.
+
+### Priority: Bundled Bird (env auth) → xAI API (paid)
+
+```python
+if node_available and AUTH_TOKEN and CT0:
+    use bundled Bird    # Free, popup-free, env-authenticated
+elif XAI_API_KEY:
+    use xAI API         # Paid, uses grok-4-1-fast
+else:
+    skip X entirely     # No X results
+```
+
+### Backend 1: xAI API
+
+**API Call:**
+```
+POST https://api.x.ai/v1/responses
+Authorization: Bearer {XAI_API_KEY}
+```
+
+**Payload:**
+```json
+{
+  "model": "grok-4-1-fast",
+  "tools": [{ "type": "x_search" }],
+  "input": "Search X for posts about {topic} from {from_date} to {to_date}..."
+}
+```
+
+The prompt asks grok to return JSON with:
+- `text`, `url`, `author_handle`, `date`
+- `engagement`: `{ likes, reposts, replies, quotes }`
+- `why_relevant`, `relevance` score
+
+**Engagement data comes from grok's x_search tool** - it has direct access to X's data.
+
+### Backend 2: Bundled Bird client (free alternative)
+
+The repo vendors a search-only subset of Bird's Twitter GraphQL client and shells out to it with Node.js. No global `bird` install is required. The Python wrapper passes `AUTH_TOKEN` and `CT0` via env, which keeps normal local runs headless and avoids browser-cookie prompts.
+
+**Bundled Bird returns raw X API data** - likes, reposts, replies are real engagement metrics from X's API, not estimates.
+
+| Metric | Bundled Bird | xAI API |
+|---|---|---|
+| Post text | Real | Real |
+| Likes/reposts | Real (X API) | Real (x_search tool) |
+| Replies/quotes | Real | Real |
+| Author handle | Real | Real |
+| Relevance score | Default 0.7 (re-ranked by score.py) | AI-assessed 0.0-1.0 |
+
+### Depth settings
+
+| Depth | xAI posts | Bundled Bird results | xAI timeout | Bird timeout |
+|---|---|---|---|---|
+| `--quick` | 8-12 | 12 | 90s | 30s |
+| default | 20-30 | 30 | 120s | 45s |
+| `--deep` | 40-60 | 60 | 180s | 60s |
+
+---
+
+## Post-Processing (both sources)
+
+After both searches complete:
+
+1. **Normalize** — consistent formatting, timezone handling
+2. **Date filter** — hard filter to requested date range
+3. **Score** — relevance scoring (engagement-weighted)
+4. **Sort** — highest scores first
+5. **Deduplicate** — remove duplicate URLs
+6. **Fallback** — if all items filtered out, keep top 3 by relevance
+
+---
+
+## Error Handling
+
+| Layer | Strategy |
+|---|---|
+| HTTP requests | 3 retries with exponential backoff (1s → 2s → 3s) |
+| Model access errors | Automatic fallback to next model in chain |
+| Reddit enrichment | Per-item try/catch; keeps unenriched item on failure |
+| X source detection | Silent fallback from Bird → xAI → skip |
+| Overall pipeline | Errors stored as `reddit_error`/`x_error`, shown to user |
+
+---
+
+## Key Files
+
+| File | Purpose |
+|---|---|
+| `scripts/last30days.py` | Main orchestrator, concurrent execution |
+| `scripts/lib/openai_reddit.py` | Reddit search via OpenAI Responses API |
+| `scripts/lib/reddit_enrich.py` | Fetch real engagement data from Reddit JSON API |
+| `scripts/lib/xai_x.py` | X search via xAI API |
+| `scripts/lib/bird_x.py` | X search via bundled Bird client (free) |
+| `scripts/lib/models.py` | Auto-select best available model |
+| `scripts/lib/env.py` | API key loading, source detection |
+| `scripts/lib/http.py` | HTTP transport with retries |
+| `scripts/lib/score.py` | Relevance scoring |
+| `scripts/lib/dedupe.py` | URL-based deduplication |

docs/plans/2026-04-22-002-feat-competitors-flag-comparison-fanout-plan.md

@@ -0,0 +1,303 @@
+---
+title: "feat: --competitors flag for auto-discovered comparison fan-out"
+type: feat
+status: active
+date: 2026-04-22
+---
+
+# feat: --competitors flag for auto-discovered comparison fan-out
+
+## Overview
+
+Add a `--competitors` flag to the last30days engine that auto-discovers 2-4 peer entities for the topic, runs the full retrieval pipeline on each in parallel, and renders a multi-entity comparison. Invoking `last30days Kanye West --competitors` should resolve to "Kanye vs Drake vs Kendrick Lamar" and emit a comparison report covering all three. Invoking `last30days OpenAI --competitors` should resolve to "OpenAI vs Anthropic vs xAI vs Gemini" and emit a four-way comparison.
+
+Discovery mirrors the existing `resolve.auto_resolve()` pattern used for X handles and subreddits at pipeline start — web search (Brave / Exa / Serper) plus deterministic extraction. Not an internal LLM call.
+
+## Problem Frame
+
+Users who want a comparison today must type "OpenAI vs Anthropic vs xAI" themselves. The `planner._comparison_entities()` path already handles explicit multi-entity topics and `render._render_comparison_scaffold()` already emits a 9-axis comparison table. What is missing is the discovery half — a user who types a single entity with `--competitors` should get the comparison for free.
+
+This is also the natural next step after the Step 0.55 category-peer subreddit work (PR #305, merged 2026-04-22). That feature widens the subreddit set within a single topic; this feature widens the entity set into peer entities.
+
+## Requirements Trace
+
+- R1. New `--competitors` boolean flag that triggers competitor discovery and multi-entity fan-out.
+- R2. New `--competitors-list="A,B,C"` to explicitly skip discovery (mirrors `--plan`, `--subreddits`, `--x-handle` overrides).
+- R3. New `--competitors=N` short form to set competitor count inline (N in 1..6).
+- R4. Default count is 3 competitors (original + 3 = 4-way comparison).
+- R5. Competitor retrieval depth inherits the main run's depth (`--quick` / `--deep`); all entities run in parallel so wall clock stays close to a single run.
+- R6. Discovery mirrors `resolve.auto_resolve()`: web search for peers, deterministic text extraction. No internal LLM dependency.
+- R7. If no web search backend is configured and no `--competitors-list` was passed, engine emits a LAW 7-style stderr telling the host agent to pass `--competitors-list` and exits non-zero.
+- R8. Output rendering is a single comparison report covering all entities, reusing the existing 9-axis scaffold from `render._render_comparison_scaffold()` where applicable.
+
+## Scope Boundaries
+
+- Synthesis prompt changes beyond wiring N reports into the existing comparison scaffold are out of scope.
+- `--competitors` does not replace the existing explicit "A vs B vs C" topic parsing in `planner._comparison_entities()`; both paths coexist.
+- No caching layer for discovery results in v1.
+- No UI/SKILL.md rewrite of the entire comparison section; only the new flag is documented.
+- No new web search backend.
+
+### Deferred to Separate Tasks
+
+- Caching of competitor lookups: separate follow-up once hit rate justifies it.
+- Disambiguation UX for topics with multiple common entities ("Amazon" the company vs the river): separate brainstorm.
+
+## Context & Research
+
+### Relevant Code and Patterns
+
+- `scripts/last30days.py:168-249` — `build_parser()` argparse definitions. Existing depth flags (`--quick`, `--deep`) and override flags (`--plan`, `--subreddits`, `--x-handle`, `--auto-resolve`) set the convention to mirror.
+- `scripts/lib/resolve.py:179-258` — `auto_resolve()` is the reference pattern: web search fan-out via `ThreadPoolExecutor`, per-query extraction functions, graceful empty-dict return when no backend is available.
+- `scripts/lib/resolve.py:98-140` — `_extract_x_handle()` and sibling extractors show the deterministic text-mining style competitor extraction should mirror.
+- `scripts/lib/pipeline.py:162-220` — `pipeline.run()` signature is the fan-out target. One call per entity, each returning a `schema.Report`.
+- `scripts/lib/planner.py:430-564` — Existing comparison-intent handling and `_comparison_entities()` entity extraction. The new flag feeds the same mental model but populates entities from discovery instead of from the topic string.
+- `scripts/lib/render.py:333-392` — `_render_comparison_scaffold()` already emits a 9-axis markdown comparison table. The new multi-report renderer should reuse this helper by assembling a synthetic "A vs B vs C" topic header for it.
+- `scripts/lib/grounding.py` + `scripts/lib/providers.py` — Web search backend resolution (Brave / Exa / Serper). Reused as-is.
+
+### Institutional Learnings
+
+- No existing `docs/solutions/` entries for competitor discovery or multi-entity fan-out.
+- Recent plan `docs/plans/2026-04-22-001-fix-category-peer-subreddit-resolution-plan.md` established the precedent of deterministic peer expansion; this plan extends that idea from subreddits to entities.
+
+### External References
+
+- None gathered — local patterns are strong. `resolve.auto_resolve()` is a direct template.
+
+## Key Technical Decisions
+
+- **Discovery mirrors auto_resolve, not plan_query.** Web search + regex extraction, not an LLM call. Matches the user's explicit direction ("use the python brain the same way it searches for X handles"). Cheaper, no provider credential requirement, deterministic.
+- **Orchestration lives in `last30days.py` main, not inside `pipeline.run()`.** The fan-out is a top-level concern — one pipeline run per entity, each independent. Keeps `pipeline.run()` single-entity and unchanged except for sharing a `ThreadPoolExecutor` factory.
+- **Sub-runs inherit main depth and run in parallel.** Wall clock ≈ single run; token cost scales linearly with N. User-controlled via the existing `--quick`/`--deep` flags.
+- **New module `scripts/lib/competitors.py` instead of adding to `resolve.py`.** Keeps resolve focused on single-entity entity-bundle discovery (handles/subreddits/github); competitors.py owns peer-entity discovery. Similar shape, different responsibility.
+- **Multi-report render is additive in `render.py`.** New `render_comparison_multi(reports: list[Report]) -> str` composes a synthetic "A vs B vs C" topic and delegates to the existing scaffold + synthesis path where possible. No rewrite of the single-entity render path.
+- **Default count = 3 competitors (4-way comparison).** Hard cap at 6.
+- **LAW 7-style stderr when no backend and no list.** Matches how `planner.plan_query()` already tells the hosting agent to pass `--plan`.
+
+## Open Questions
+
+### Resolved During Planning
+
+- **Discovery mechanism:** Web search via `grounding.web_search()`, not an internal LLM. User confirmed the auto_resolve pattern is the target.
+- **Default competitor count:** 3 (original + 3 = 4-way).
+- **Sub-run depth:** Inherit main depth, parallel execution.
+- **Flag naming:** `--competitors` (standard argparse double-dash). `--competitors=N` for inline count. `--competitors-list="A,B,C"` to skip discovery.
+
+### Deferred to Implementation
+
+- Exact extraction heuristics for competitor names across Brave / Exa / Serper result shapes. The SERP text varies (listicles, comparison pages, "vs" pages); the initial implementation will start with listicle parsing plus a "X vs Y" pattern match, and harden against real results in the test phase.
+- Handling of topic ambiguity ("Amazon", "Apple"). Initial behavior: trust whatever web search returns for the topic verbatim; disambiguation is a separate concern.
+- Merge strategy when two entities return overlapping URLs (e.g., an "OpenAI vs Anthropic" article shows up in both runs). Likely dedupe at the clustering step, but defer the exact policy until we see how often it happens.
+- Whether to expose competitor discovery artifacts (the raw web search results) as a debug emit. Follow the existing `--debug` conventions.
+
+## Implementation Units
+
+- [ ] **Unit 1: CLI flag parsing and validation**
+
+**Goal:** Add `--competitors`, `--competitors=N`, and `--competitors-list` to the argparse surface, validate values, and thread them into the main orchestration.
+
+**Requirements:** R1, R2, R3, R4
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `scripts/last30days.py`
+- Test: `tests/test_cli_competitors.py`
+
+**Approach:**
+- Add three mutually cooperative flags near line 205 in `build_parser()`:
+  - `--competitors` with `nargs="?"` and `const=3` so bare `--competitors` defaults to 3, `--competitors=4` is honored, and `--competitors=0` is rejected
+  - `--competitors-list` free-text CSV
+- Normalize in `main()`: if `--competitors-list` is present, skip discovery and use the list. If `--competitors` is set and no list, trigger discovery with count = the flag value. Clamp count to 1..6 with a stderr warning at boundary.
+- Thread the resulting entity list into the orchestrator added in Unit 3.
+
+**Patterns to follow:**
+- `--plan` argument at `scripts/last30days.py:187` — same skip-discovery-when-explicit shape.
+- `--subreddits` / `--x-handle` at `scripts/last30days.py:180,189` — same override semantics.
+
+**Test scenarios:**
+- Happy path: bare `--competitors` parses to count=3, empty list.
+- Happy path: `--competitors=4` parses to count=4.
+- Happy path: `--competitors-list="A,B,C"` parses to count=3, list=["A","B","C"], and is preferred over any discovery signal.
+- Edge case: `--competitors=0` and `--competitors=-1` are rejected with a clear error.
+- Edge case: `--competitors=99` clamps to 6 with a stderr warning.
+- Edge case: `--competitors` combined with `--competitors-list` uses the list and logs that discovery was skipped.
+- Edge case: `--competitors-list` value with whitespace ("A, B , C") normalizes correctly.
+
+**Verification:**
+- Running the binary with each flag variation produces the expected post-parse state without calling out to the network.
+
+- [ ] **Unit 2: `scripts/lib/competitors.py` discovery module**
+
+**Goal:** Discover peer entities for a topic using web search + deterministic extraction, mirroring `resolve.auto_resolve()`.
+
+**Requirements:** R6, R7
+
+**Dependencies:** None (pure module; wired by Unit 3)
+
+**Files:**
+- Create: `scripts/lib/competitors.py`
+- Test: `tests/test_competitors.py`
+
+**Approach:**
+- Public entry point `discover_competitors(topic: str, count: int, config: dict) -> list[str]`.
+- Early return `[]` when `_has_backend(config)` is false (reuse the helper from `resolve.py`; factor if needed).
+- Fan out 2-3 web searches in a `ThreadPoolExecutor`:
+  - `"{topic} competitors"`
+  - `"{topic} alternatives"`
+  - `"{topic} vs"` (captures "X vs Y" articles)
+- Feed results into a deterministic `_extract_peer_entities(results, topic)` that:
+  - Mines titles and snippets for capitalized noun phrases other than the topic itself
+  - Scores by frequency across results
+  - Filters stopwords and the topic's own tokens
+  - Returns top `count` unique entities ordered by score
+- Emit a single-line stderr log mirroring the `resolve._log` format.
+
+**Patterns to follow:**
+- `scripts/lib/resolve.py:179-258` for the function shape, executor usage, and empty-result fallback.
+- `scripts/lib/resolve.py:98-140` for extractor style (small, deterministic, no external state).
+
+**Test scenarios:**
+- Happy path: canned SERP fixtures for "OpenAI" return ["Anthropic", "xAI", "Google"] or close peers in the top 3.
+- Happy path: canned SERP fixtures for "Kanye West" return rap peers (Drake, Kendrick) in the top 3.
+- Edge case: empty SERP results return `[]` without raising.
+- Edge case: extractor filters out the topic itself (case- and punctuation-insensitive).
+- Edge case: near-duplicate entities ("OpenAI" vs "Open AI") dedupe to one slot.
+- Error path: web search backend raises — the failure is logged and the function returns `[]`.
+- Edge case: count=1 returns a single-element list; count=6 returns up to six entities.
+
+**Verification:**
+- Unit tests pass with fixtures committed under `tests/fixtures/competitors-*.json`.
+- Manual run against a live backend for one topic confirms sensible output (recorded as a notes file, not a test assertion).
+
+- [ ] **Unit 3: Parallel fan-out orchestrator**
+
+**Goal:** Run `pipeline.run()` once per entity (topic + discovered competitors) in parallel, collect `schema.Report` per entity, and hand them to the comparison renderer.
+
+**Requirements:** R5, R7
+
+**Dependencies:** Unit 1, Unit 2
+
+**Files:**
+- Modify: `scripts/last30days.py`
+- Possibly create: `scripts/lib/fanout.py` if the orchestrator grows past ~60 lines
+- Test: `tests/test_competitor_fanout.py`
+
+**Approach:**
+- After arg parsing and before the existing `pipeline.run()` call, branch on `args.competitors`:
+  - If a list was provided or discovery returned entities, build `entities = [topic, *competitors]`.
+  - Spawn one `pipeline.run()` per entity via `ThreadPoolExecutor(max_workers=len(entities))`, passing the same `config`, `depth`, and all sub-run-relevant args (mock, plan, etc.). Respect `--plan` — if a plan is passed it applies to the main topic only; competitors use the internal planner fallback for v1.
+  - Collect `{entity: Report}` mapping. A per-entity failure logs a stderr warning and drops that entity from the comparison; the run continues as long as 2 entities succeed.
+  - If fewer than 2 entities survive, exit with a clear error.
+- LAW 7-style stderr:
+  - If `args.competitors` is set, no list was passed, no web search backend is configured, emit a LAW 7 stderr message pointing to the `--competitors-list` override and exit non-zero. Reuse the tone from `planner.plan_query()` fallback (`scripts/lib/planner.py:125-135`).
+
+**Execution note:** Start with a failing integration test that exercises the full main → orchestrator → mocked pipeline.run path; the orchestrator is where bugs hide.
+
+**Patterns to follow:**
+- `scripts/lib/resolve.py:225-239` for ThreadPoolExecutor + as_completed + per-future error handling.
+- `scripts/lib/pipeline.py:310+` for how ThreadPoolExecutor is already used inside a single run (same idiom, outer layer).
+
+**Test scenarios:**
+- Happy path: main + 2 competitors, all three `pipeline.run()` calls succeed (mocked), orchestrator returns 3 Reports.
+- Happy path: discovery returns the competitor list; orchestrator fans out accordingly.
+- Edge case: one of three competitor pipelines raises — the run continues with the surviving 2 and emits a warning.
+- Edge case: all competitors fail but the main topic succeeds — orchestrator exits non-zero with a clear error rather than silently degrading to a single-entity render.
+- Edge case: `--competitors` set, no backend, no list — orchestrator emits the LAW 7 stderr and exits non-zero before any pipeline call.
+- Integration: wall-clock time for 3 mocked pipelines in parallel is close to the slowest single run, not the sum (timing assertion with generous margin).
+
+**Verification:**
+- End-to-end test with mocked `pipeline.run()` and mocked competitors discovery produces 3 Reports and hands them to a stubbed renderer.
+
+- [ ] **Unit 4: Multi-report comparison renderer**
+
+**Goal:** Compose N `schema.Report`s into a single comparison-mode output, reusing the existing 9-axis scaffold.
+
+**Requirements:** R8
+
+**Dependencies:** Unit 3
+
+**Files:**
+- Modify: `scripts/lib/render.py`
+- Test: `tests/test_render_comparison_multi.py`
+
+**Approach:**
+- Add `render_comparison_multi(reports: list[schema.Report], *, emit: str) -> str`.
+- Build a synthetic comparison topic: `f"{entity_a} vs {entity_b} vs {entity_c}"`.
+- Reuse `_render_comparison_scaffold()` for the table skeleton. Each entity column is populated from its own Report's top clusters and citations.
+- For the narrative synthesis block, concatenate per-entity highlights, clearly labeled by entity, under a shared "Comparison" header.
+- Preserve existing emit modes (`compact`, `md`, `json`, `context`). In `json` emit, return a `{"entities": [...], "reports": [...]}` shape; single-Report consumers remain unaffected because the single-report render path is untouched.
+
+**Patterns to follow:**
+- `scripts/lib/render.py:333-392` (`_parse_comparison_entities`, `_render_comparison_scaffold`) — the scaffold is the contract.
+- `scripts/lib/render.py` single-report rendering — for per-entity narrative blocks.
+
+**Test scenarios:**
+- Happy path: 3 Reports with distinct clusters render into a 3-column table and a "Comparison" section that mentions each entity at least once.
+- Happy path: 2 Reports render as a 2-column table without breaking the scaffold.
+- Edge case: a Report with an empty cluster list renders as "(no significant discussion this month)" in its column rather than crashing.
+- Edge case: Reports with overlapping URLs (same article cited by two entities) dedupe citations at the footer but keep both column entries.
+- Emit variants: `--emit=compact`, `--emit=md`, `--emit=json`, `--emit=context` each produce valid output with all entities represented.
+- Integration: end-to-end snapshot test using fixture Reports, checked against a stored expected output (with a clear update path when the scaffold intentionally evolves).
+
+**Verification:**
+- Snapshot tests pass. Manual review of one real 3-way comparison confirms readability.
+
+- [ ] **Unit 5: Docs, SKILL.md mention, and sync**
+
+**Goal:** Document the new flag so the hosting agent and human users both know it exists, and run the sync script.
+
+**Requirements:** R1-R8 (surfaces them to users)
+
+**Dependencies:** Units 1-4
+
+**Files:**
+- Modify: `SKILL.md`
+- Modify: `README.md` (brief flag reference)
+- Modify: `CHANGELOG.md`
+- Run: `bash scripts/sync.sh`
+
+**Approach:**
+- Add a compact "Competitor mode" subsection under the existing comparison docs in `SKILL.md`. Document the flag, the default count, the override flag, and the LAW 7 fallback stderr.
+- Keep `README.md` addition to a single example line.
+- CHANGELOG entry mirrors the voice of recent entries (imperative, outcome-first).
+- Sync via `scripts/sync.sh` per CLAUDE.md rules so `~/.claude/`, `~/.agents/`, `~/.codex/` pick up the new SKILL.md.
+
+**Test scenarios:**
+- Test expectation: none — documentation and sync only. Verification is by inspection and by running `sync.sh` and confirming target directories updated.
+
+**Verification:**
+- `sync.sh` completes without errors.
+- `SKILL.md` rendered preview mentions `--competitors` in the comparison section.
+
+## System-Wide Impact
+
+- **Interaction graph:** `last30days.py main()` now orchestrates multiple `pipeline.run()` calls instead of one. No other callers of `pipeline.run()` are affected (it remains single-entity).
+- **Error propagation:** Per-entity failures degrade gracefully as long as ≥2 entities survive; fewer survivors exits non-zero. Discovery failure with `--competitors` and no list is fatal.
+- **State lifecycle risks:** Each sub-run uses its own `pipeline.run()` state; no shared mutable config. The `config` dict is read-only in `pipeline.run()` today — verify before committing to shared-reference passing, else deep-copy per sub-run.
+- **API surface parity:** `--competitors` coexists with the existing explicit "A vs B vs C" topic parsing in `planner._comparison_entities()`. Both produce comparable output formats; the only difference is where the entity list came from.
+- **Integration coverage:** The fan-out orchestrator crosses CLI → discovery → N pipelines → render; integration tests in Unit 3 and Unit 4 must exercise the full path end to end, not just unit-level.
+- **Unchanged invariants:** `pipeline.run()` signature and single-entity semantics are unchanged. The single-entity render path in `render.py` is unchanged. No changes to `planner.plan_query()`. No changes to existing flags.
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Competitor discovery returns garbage entities for niche topics. | `--competitors-list` override lets the user (or hosting agent) correct it. Unit tests with edge-case fixtures. Log discovery output to stderr under `--debug`. |
+| Token cost scales linearly with N sub-runs. | Default count capped at 3, hard max 6, inherit `--quick` to let users throttle. Wall clock stays parallel. Emit a cost hint to stderr when N ≥ 4. |
+| Merge conflicts against the single-entity render path during refactoring. | Keep the multi-report renderer strictly additive; do not modify the single-Report code path. |
+| Config dict mutation inside sub-runs could leak state between entities. | Verify read-only usage before sharing references. If any sub-component mutates, deep-copy per sub-run before spawning threads. |
+| A SERP extractor that works on Brave fixtures breaks on Exa/Serper result shapes. | Test fixtures for all three backends. Extractor operates on a normalized shape from `grounding.web_search()` (already the case), not raw provider output. |
+| Hosting agent (Claude Code, Codex) unaware of the new flag when it could usefully pass `--competitors-list`. | SKILL.md updated in Unit 5 documents the flag in the same style as `--plan` and `--auto-resolve`. |
+
+## Documentation / Operational Notes
+
+- Beta channel first: per `CLAUDE.md`, experimental changes go to `mvanhorn/last30days-skill-private` on the `/last30days-beta` command. Land this on the private repo first, shake out on real topics for a day or two, then cherry-pick to public.
+- After land-merge: run `scripts/sync.sh` to deploy SKILL.md + scripts to `~/.claude/`, `~/.agents/`, `~/.codex/`.
+- Release notes entry in CHANGELOG.md follows the v3.0.9 voice — outcome-first, one paragraph.
+
+## Sources & References
+
+- Related code: `scripts/lib/resolve.py:179` (`auto_resolve`), `scripts/lib/pipeline.py:162` (`pipeline.run`), `scripts/lib/planner.py:80` (`plan_query` LAW 7 fallback), `scripts/lib/render.py:333` (comparison scaffold)
+- Related PRs: #305 (Step 0.55 category-peer subreddit expansion — the precedent for deterministic peer expansion, merged 2026-04-22)
+- Related plan: `docs/plans/2026-04-22-001-fix-category-peer-subreddit-resolution-plan.md`

docs/plans/2026-04-22-003-fix-competitors-per-entity-resolution-plan.md

@@ -0,0 +1,349 @@
+---
+title: "fix: per-entity resolution, default-2, and stale-path guard for --competitors"
+type: fix
+status: active
+date: 2026-04-22
+origin: docs/plans/2026-04-22-002-feat-competitors-flag-comparison-fanout-plan.md
+---
+
+# fix: per-entity resolution, default-2, and stale-path guard for --competitors
+
+## Overview
+
+Three test runs of v3.0.11 `--competitors` surfaced four real bugs plus one product tweak. This plan fixes all of them in a single follow-up:
+
+1. Competitor sub-runs get no Step 0.55 resolution (no X handle, no subreddits, no GitHub repo). Drake / Kendrick / Travis ran with deterministic-fallback single-word queries while Kanye had the full targeting package. User called it "lazy" and was right.
+2. Two of three test windows (Linear, Coinbase) never invoked the new flag at all. They loaded SKILL.md from `plugins/marketplaces/last30days-skill/` (a Claude-Code-managed git clone pinned to origin/main, which predates PR #308) instead of `plugins/cache/last30days-skill/last30days/3.0.11/`, so `--help` showed no `--competitors` flag and the model fell back to the manual comparison path.
+3. Each competitor sub-run emits a scary `[Planner] No --plan passed... deterministic fallback` stderr line because LAW 7 targets the hosting-model path, not internal fan-out sub-runs.
+4. Default competitor count is 3 (→ 4-way comparison). User wants default 2 (→ 3-way: original + 2 peers). Flag keeps `--competitors=N` to customize.
+
+## Problem Frame
+
+The 3 test runs (Kanye, Linear, Coinbase) showed a pattern:
+
+| Window | Loaded SKILL.md from | Invoked --competitors? | Per-entity resolution? | Outcome |
+|--------|----------------------|-----------------------|------------------------|---------|
+| Kanye  | cache/3.0.11/ (correct) | Yes | Only for main topic (Kanye) | Drake/Kendrick/Travis thin; Reddit 403 fallbacks |
+| Linear | marketplaces/ (stale) | No — fell back to manual comparison | No | Thin run with noisy subreddits |
+| Coinbase | marketplaces/ (stale) | No — fell back to manual comparison | Main only; keyword-search poisoned pool | Top subs: r/survivor, r/Airpodsmax (noise) |
+
+Root causes:
+- **Per-entity resolution gap:** `scripts/lib/fanout.py` calls `pipeline.run()` with topic + depth + web_backend + lookback_days only. It does not call `resolve.auto_resolve()` per entity, so sub-runs have no X handle, subreddit, or GitHub targeting. The original plan (`2026-04-22-002`) acknowledged this as a deliberate v1 simplification ("competitor sub-runs use planner defaults"). In practice this produces visibly asymmetric output and triggers downstream retrieval issues (403 fallbacks, keyword-search noise).
+- **Stale-path loading:** Claude Code's skill loader alphabetizes `find` results with `marketplaces/` before `cache/`, and the model reads the first plausible SKILL.md it sees. SKILL.md line 823's `SKILL_ROOT` resolver is the correct path but only fires in engine-invocation blocks, not in the skill-load step.
+- **LAW 7 in sub-runs:** LAW 7 exists because the *hosting reasoning model* is supposed to pass `--plan`. For competitor sub-runs, there is no hosting-model planning — it's an engine-internal fan-out. The warning is a false positive there.
+
+## Requirements Trace
+
+- R1. Default `--competitors` count is 2 peers (3-way comparison: original + 2).
+- R2. Each competitor sub-run performs Step 0.55 resolution (X handle, subreddits, GitHub user/repos, news context) before its pipeline runs — not just the main topic.
+- R3. Sub-runs do not emit the LAW 7 `No --plan passed` warning; they are internal fan-out, not hosting-model calls.
+- R4. The rendered comparison output includes a visible "Resolved entities" block showing per-entity handles/subs/github for debug transparency (answers "did it resolve everyone?" without the user having to read stderr).
+- R5. SKILL.md has a canonical-path self-check at the top: if the reader loaded it from anywhere other than `plugins/cache/last30days-skill/last30days/{VERSION}/`, re-read from the versioned path before proceeding.
+- R6. Version bumps to 3.0.12; CHANGELOG entry; `scripts/sync.sh` deploys.
+
+## Scope Boundaries
+
+- No new discovery strategy. The web-search + regex extraction in `scripts/lib/competitors.py` stays as-is.
+- No new CLI flags beyond the behavior changes above. Specifically: no per-entity override flags like `--competitor-handles`. The hosting-model escape hatch remains `--competitors-list`.
+- No changes to the explicit `A vs B` comparison path (topic-string parsing in `planner._comparison_entities`).
+- No marketplace-clone auto-restore fix — that's Claude Code harness behavior. This plan only guards against the symptom on the skill side.
+
+### Deferred to Separate Tasks
+
+- Caching of per-entity resolution results: separate follow-up once hit rate justifies it.
+- Fan-out rate-limiting tuning (currently `max_workers=len(entities)+1`, capped at 6): defer until we see real-world quota exhaustion.
+- Pre-flight cost hint when N ≥ 4 (noted in `2026-04-22-002` risks): defer.
+
+## Context & Research
+
+### Relevant Code and Patterns
+
+- `scripts/last30days.py:205-219` — `--competitors` / `--competitors-list` argparse definition (const=3 today; changing to 2).
+- `scripts/last30days.py:220-290` — `resolve_competitors_args()` validator; update `COMPETITORS_DEFAULT`.
+- `scripts/last30days.py:438-520` — main() fan-out orchestration; currently passes only topic/depth to each `_competitor_runner`.
+- `scripts/lib/fanout.py:40-95` — `run_competitor_fanout()` signature. The `competitor_runner` callable is where per-entity resolution needs to happen.
+- `scripts/lib/resolve.py:179-258` — `auto_resolve()` is the exact per-entity resolver to reuse. Already does X handle + subreddits + GitHub user/repos + news context in parallel via ThreadPoolExecutor.
+- `scripts/lib/planner.py:80-135` — `plan_query()` emits the LAW 7 stderr. A `quiet: bool` keyword or `internal_subrun: bool` flag will suppress it.
+- `scripts/lib/pipeline.py:162-220` — `pipeline.run()` signature. Needs a new keyword to propagate quiet-mode down to the planner.
+- `scripts/lib/render.py:render_comparison_multi` — where the "Resolved entities" block is inserted.
+- `SKILL.md` line 823 — canonical `SKILL_ROOT` resolver already exists but fires in engine bash, not at skill-load time.
+
+### Institutional Learnings
+
+- `docs/plans/2026-04-22-002-feat-competitors-flag-comparison-fanout-plan.md` acknowledged the per-entity-resolution gap as a v1 tradeoff. This plan closes that gap.
+- Kanye run stderr: `[Planner] No --plan passed... deterministic fallback` × 3 (once per competitor sub-run). That's the LAW 7 noise R3 targets.
+- Linear / Coinbase runs loaded `plugins/marketplaces/last30days-skill/CLAUDE.md` as the first hit. That's the stale-path issue R5 targets.
+
+### External References
+
+- None. All patterns are in-repo.
+
+## Key Technical Decisions
+
+- **Per-entity resolve happens inside fanout, not in SKILL.md.** The user-facing promise of `--competitors` is "one flag, engine does the work." Pushing resolution onto the hosting model creates another path-of-least-resistance trap (model skips it, output looks lazy). Auto-resolve inside each sub-run when a web backend is available makes the feature self-contained.
+- **Stale-path guard is a SKILL.md self-check, not a code change.** We cannot stop Claude Code from auto-restoring the marketplace clone. But we can put a 3-line banner at the top of SKILL.md that forces any path-mismatched read to re-read from the versioned cache. Both the marketplace copy (once main catches up) and the cache copy carry the guard.
+- **LAW 7 suppression is opt-in via `internal_subrun=True` keyword.** Do not remove the warning from the default path — it's load-bearing for the hosting-model contract. Add an explicit bypass for engine-internal fan-out only.
+- **Default 2, hard max 6 unchanged.** "Original + 2" matches the Kanye/Drake/Kendrick mental model from the feature description. Still allow `--competitors=N` from 1 to 6.
+- **Resolved block is inside the EVIDENCE envelope, not above it.** Keeps the rendered output structure stable for the synthesis contract (LAW 1–8). The block is context, not output.
+- **Skip auto-resolve when `--mock` or no web backend.** Mirrors the existing `resolve.auto_resolve()` fast-fail and keeps the mock test path deterministic.
+
+## Open Questions
+
+### Resolved During Planning
+
+- **Where does per-entity resolve live?** Inside `fanout.run_competitor_fanout`, not in `main()`. Each sub-run calls `auto_resolve()` just before `pipeline.run()`.
+- **Should the hosting model still be able to override?** Yes — `--competitors-list` remains the escape hatch. When an explicit list is passed, the engine still does auto-resolve per entity; the user's list just skips discovery.
+- **Should sub-runs run auto-resolve in parallel with each other?** Yes. The existing `ThreadPoolExecutor` in fanout already parallelizes sub-runs; auto-resolve happens inside each sub-run's thread, so resolve calls for different entities run concurrently.
+- **Default count:** 2 peers (3-way). Confirmed.
+
+### Deferred to Implementation
+
+- Whether to expose a `--no-auto-resolve-competitors` flag for power users who want the fast, shallow behavior. Probably not needed v2; ship auto-resolve always-on and revisit if someone complains about cost.
+- Whether to surface the per-entity resolution context back into the main topic's planner (cross-entity context sharing). Stays deferred.
+- Whether the Resolved block should be collapsible or always inline. Start inline; revisit based on output length feedback.
+
+## Implementation Units
+
+- [ ] **Unit 1: Default `--competitors` to 2 peers**
+
+**Goal:** Change the bare `--competitors` default from 3 to 2 per user feedback. `--competitors=N` still overrides; range 1..6 unchanged.
+
+**Requirements:** R1
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `scripts/last30days.py` (`COMPETITORS_DEFAULT`, `--competitors` const, stderr messages if any reference 3)
+- Modify: `SKILL.md` Competitor mode section ("discovered 2-6" wording, bare-flag default line)
+- Modify: `README.md` auto-discovered example line (if it references count)
+- Test: `tests/test_cli_competitors.py`
+
+**Approach:**
+- Change `COMPETITORS_DEFAULT = 3` → `2` in `scripts/last30days.py`.
+- Change argparse `--competitors` `const=3` → `const=2`.
+- Update any SKILL.md / README copy referencing "3 peers" to "2 peers" (default) or "2-6 peers" (range).
+
+**Patterns to follow:**
+- Existing default constants in `scripts/last30days.py` argparse block.
+
+**Test scenarios:**
+- Happy path: bare `--competitors` yields count=2, enabled=True, empty explicit_list.
+- Edge case: `--competitors=3` still works (explicit override).
+- Edge case: existing `test_bare_flag_defaults_to_three` test is updated to `test_bare_flag_defaults_to_two` and asserts count=2.
+- Edge case: `--competitors=5` with a `--competitors-list` of length 2 still logs the mismatch warning and uses the list.
+
+**Verification:**
+- `pytest tests/test_cli_competitors.py -v` passes with the updated default.
+
+- [ ] **Unit 2: Per-entity Step 0.55 resolution inside fanout**
+
+**Goal:** Each competitor sub-run auto-resolves its own X handle, subreddits, GitHub user/repos, and news context via `resolve.auto_resolve()` before its `pipeline.run()` call — just like the main topic.
+
+**Requirements:** R2
+
+**Dependencies:** None (but Unit 3 should land together so sub-runs don't emit LAW 7 stderr while the resolution context is being passed)
+
+**Files:**
+- Modify: `scripts/lib/fanout.py`
+- Modify: `scripts/last30days.py` (`_competitor_runner` closure builds the resolved args)
+- Test: `tests/test_competitor_fanout.py`
+- Test: `tests/test_competitors_resolve_integration.py` (new; covers the auto-resolve path)
+
+**Approach:**
+- `_competitor_runner(entity)` in main() does:
+  1. Call `resolve.auto_resolve(entity, config)` when `not args.mock` and a web backend is configured (reuse `_has_backend`).
+  2. Extract resolved x_handle, subreddits, github_user, github_repos, context.
+  3. Pass them to `pipeline.run()` for that sub-run.
+  4. Inject resolved context into a per-entity config copy (so `_auto_resolve_context` does not leak across sub-runs — deep-copy the config or use a local dict).
+  5. Store the resolved block on the Report's `artifacts` so the renderer can surface it (Unit 4).
+- When `args.mock` is True or no backend is available, skip auto-resolve (fall through to planner defaults, matching the existing `auto_resolve()` early-return contract).
+- Update `fanout.run_competitor_fanout` docstring to note that auto-resolve happens inside the caller-provided runner.
+
+**Execution note:** Start with a failing integration test that exercises two-entity fanout + auto-resolve via a mocked `resolve.auto_resolve` and asserts that `pipeline.run` receives the resolved x_handle/subreddits for each entity.
+
+**Patterns to follow:**
+- `scripts/last30days.py` main topic branch (`if args.auto_resolve and not external_plan`) already calls `resolve.auto_resolve` and propagates results — mirror the shape for competitors.
+- Config isolation: `scripts/lib/pipeline.py:162-220` reads config as-is; use `dict(config)` to avoid cross-sub-run mutation of `_auto_resolve_context`.
+
+**Test scenarios:**
+- Happy path: 3 entities, mocked `auto_resolve` returns distinct handles per entity; `pipeline.run` receives `x_handle=@drake` for Drake, `x_handle=@kendricklamar` for Kendrick, etc.
+- Happy path: the main topic still uses the user-supplied `--x-handle` / `--subreddits` overrides (not overwritten by auto-resolve for the main). Competitors use their own auto-resolved values.
+- Edge case: `--mock` skips auto-resolve entirely for all sub-runs (no `resolve.auto_resolve` calls).
+- Edge case: `resolve.auto_resolve` returns empty dicts for one entity (low-signal topic) — the sub-run still executes with planner defaults; doesn't crash.
+- Edge case: no web backend configured — auto-resolve returns empty for every entity, sub-runs fall through to planner defaults, no stack trace.
+- Error path: `resolve.auto_resolve` raises — the sub-run logs a warning and continues with planner defaults (does not fail the whole comparison).
+- Integration: config `_auto_resolve_context` from entity A does not leak into entity B's `pipeline.run`. Assert each sub-run gets its own context string.
+
+**Verification:**
+- New integration test passes.
+- End-to-end smoke (mock mode + explicit list): each sub-run's stderr shows `[AutoResolve]` lines per entity with distinct values.
+
+- [ ] **Unit 3: Suppress LAW 7 warning for engine-internal sub-runs**
+
+**Goal:** The `[Planner] No --plan passed... deterministic fallback` warning does not fire during competitor sub-runs. LAW 7 is load-bearing for hosting-model contracts and must stay on the default path; this is an opt-in bypass for internal fan-out only.
+
+**Requirements:** R3
+
+**Dependencies:** Unit 2 (so the sub-run call site is already being modified)
+
+**Files:**
+- Modify: `scripts/lib/planner.py` (`plan_query` signature + conditional stderr)
+- Modify: `scripts/lib/pipeline.py` (`run` signature + propagation)
+- Modify: `scripts/last30days.py` or `scripts/lib/fanout.py` (pass `internal_subrun=True` for competitor runners)
+- Test: `tests/test_planner_v3.py` (or new `tests/test_planner_quiet_mode.py`)
+- Test: `tests/test_competitor_fanout.py` (assert sub-runs don't emit LAW 7 stderr)
+
+**Approach:**
+- Add a keyword `internal_subrun: bool = False` to `planner.plan_query`. When True, skip the two `print(..., file=sys.stderr)` blocks that emit the LAW 7 banner and the `[Planner] No --plan passed` capability message.
+- Add the same keyword to `pipeline.run()`; pass through to `plan_query`.
+- In main()/fanout, set `internal_subrun=True` for every competitor sub-run's pipeline.run call. The main topic's pipeline.run keeps the default (LAW 7 stays on for the hosting-model path).
+- Also suppress the LAW 7-triggered degraded-run warning block in the render layer for sub-reports when the envelope is going to be merged into a comparison output (or accept that the block is per-entity and surfaces once per entity).
+
+**Patterns to follow:**
+- Existing keyword-only parameters on `pipeline.run` (`mock`, `x_handle`, etc.).
+- `planner.plan_query` signature is already keyword-only.
+
+**Test scenarios:**
+- Happy path: `plan_query(..., internal_subrun=True, provider=None, model=None)` returns the deterministic fallback plan WITHOUT writing the LAW 7 stderr block.
+- Happy path: `plan_query(...)` with default `internal_subrun=False` still writes the LAW 7 warning (unchanged behavior).
+- Integration: end-to-end competitor fanout; assert captured stderr contains zero occurrences of `No --plan passed` and zero of `YOU ARE the planner`.
+- Integration: main topic is not part of competitor mode; if the user invokes bare `/last30days OpenAI` without `--plan`, LAW 7 stderr fires exactly once (regression test).
+
+**Verification:**
+- Running the Kanye-style smoke test shows zero `[Planner] No --plan passed` lines for Drake / Kendrick / Travis sub-runs.
+
+- [ ] **Unit 4: "Resolved entities" block in comparison output**
+
+**Goal:** The rendered comparison output includes a visible block listing per-entity handles, subreddits, GitHub user, and resolved context. Answers "did it resolve everyone?" at a glance without reading stderr.
+
+**Requirements:** R4
+
+**Dependencies:** Unit 2 (needs resolved data on report artifacts)
+
+**Files:**
+- Modify: `scripts/lib/render.py` (`render_comparison_multi` and `render_comparison_multi_context`)
+- Test: `tests/test_render_comparison_multi.py`
+
+**Approach:**
+- When each entity's `Report.artifacts` contains a `resolved` dict (populated by Unit 2), `render_comparison_multi` emits a `## Resolved Entities` block early in the EVIDENCE envelope:
+  ```
+  ## Resolved Entities
+  - **Kanye West**: X @kanyewest | Subs r/Kanye, r/hiphopheads | GitHub: — | Context: BULLY released, UK ban…
+  - **Drake**: X @Drake | Subs r/DrakeTheType, r/hiphopheads | GitHub: — | Context: ICEMAN rollout…
+  - **Kendrick Lamar**: X @kendricklamar | Subs r/KendrickLamar | GitHub: — | Context: Grammy wins, dormant…
+  ```
+- Missing fields render as `—` not empty.
+- When no entity has a `resolved` payload (mock mode, no web backend), omit the block entirely rather than emit an empty section.
+- Context strings are truncated at 120 chars to keep the block scannable.
+
+**Patterns to follow:**
+- Existing `render_comparison_multi` envelope structure (lines ~395-480 in render.py).
+- Existing per-entity evidence block format (`## {label}`) for consistency.
+
+**Test scenarios:**
+- Happy path: 3 entities each with a `resolved` artifact → block lists all 3 with their fields.
+- Happy path: 2 entities, one with full resolution, one with partial (x_handle only) → missing fields render as `—`.
+- Edge case: no entity has a resolved artifact → block is omitted entirely.
+- Edge case: context string > 120 chars → truncated with ellipsis.
+- Integration: rendered output passes through the same EVIDENCE envelope comments and synthesis contract (LAW 1–8 unchanged).
+
+**Verification:**
+- Snapshot tests confirm the block appears in the right spot with the right formatting.
+- End-to-end smoke shows a realistic 3-entity Resolved block in the rendered output.
+
+- [ ] **Unit 5: SKILL.md canonical-path self-check**
+
+**Goal:** A top-of-file SKILL.md directive forces any reader (Claude Code, Codex, Hermes, Gemini) to verify they loaded from `plugins/cache/last30days-skill/last30days/{VERSION}/SKILL.md` before proceeding. If loaded from `marketplaces/` or any other path, re-read from the pinned versioned cache.
+
+**Requirements:** R5
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `SKILL.md` (prepend a STEP 0 block before the existing STEP 0 / LAW list)
+
+**Approach:**
+- Add a numbered first step at the top (before or bundled with existing "STEP 0: ToolSearch preload"):
+  ```
+  ## STEP 0: Canonical Path Self-Check (must run first)
+
+  Before reading anything else below, verify you loaded this SKILL.md from
+  the versioned cache, not the marketplace clone:
+
+      CANONICAL=$HOME/.claude/plugins/cache/last30days-skill/last30days/
+      CANONICAL_LATEST=$(ls -d "$CANONICAL"*/ 2>/dev/null | sort -V | tail -1)
+
+  If the SKILL.md you just read is not under $CANONICAL_LATEST, STOP. Re-read
+  $CANONICAL_LATEST/SKILL.md and restart from here. Marketplace clones
+  (`plugins/marketplaces/last30days-skill/`) are pinned to origin/main and
+  can be stale; the versioned cache is the ground truth.
+  ```
+- Reinforce in the existing LAW 7 block that `--help` output must be read from the same pinned `SKILL_ROOT` to avoid flag-list skew.
+
+**Patterns to follow:**
+- Existing STEP 0 ToolSearch preload (top of SKILL.md) for tone / imperative voice.
+- Existing `SKILL_ROOT` resolver snippet (line ~823).
+
+**Test scenarios:**
+- Test expectation: none — SKILL.md is documentation; no unit test, verified by follow-up user invocation.
+
+**Verification:**
+- In a fresh Claude Code window, `/last30days Test --competitors` loads SKILL.md, the model executes the STEP 0 self-check, and (if it had loaded from marketplaces/) switches to the cache path before running `--help` or the engine. Observable via the model's announced reasoning / task list.
+
+- [ ] **Unit 6: Version bump, CHANGELOG, sync**
+
+**Goal:** Ship 3.0.12 and deploy to all local targets.
+
+**Requirements:** R6
+
+**Dependencies:** Units 1-5
+
+**Files:**
+- Modify: `.claude-plugin/plugin.json` (version 3.0.11 → 3.0.12)
+- Modify: `CHANGELOG.md`
+- Run: `bash scripts/sync.sh`
+
+**Approach:**
+- CHANGELOG entry under `## [3.0.12]` dated 2026-04-22 covering the four fixes (Fixed: per-entity resolution; Fixed: LAW 7 sub-run noise; Changed: default count 3→2; Added: Resolved entities block; Added: canonical-path self-check in SKILL.md).
+- `sync.sh` deploys to `~/.claude/plugins/cache/last30days-skill-private/...`, `~/.agents/`, `~/.codex/`, Hermes.
+- Manual hot-copy to `~/.claude/plugins/cache/last30days-skill/last30days/3.0.12/` so the public `/last30days` slash command picks up the new version before PR merge (matches the 3.0.11 testing pattern).
+
+**Test scenarios:**
+- Test expectation: none — packaging only. Verification is by inspection.
+
+**Verification:**
+- `grep version .claude-plugin/plugin.json` returns `3.0.12`.
+- `sync.sh` exits 0 with "Import check: OK" for each target.
+- Hot-copied 3.0.12 directory contains the new files and `/last30days` picks up the new version (highest-version resolver).
+
+## System-Wide Impact
+
+- **Interaction graph:** Fanout sub-runs now call `resolve.auto_resolve` per entity. Each sub-run is independent; no shared mutable state with other sub-runs or with the main topic.
+- **Error propagation:** `auto_resolve` failures inside a sub-run log a warning and degrade to planner defaults; do not propagate up to abort the comparison. Same contract as today for the main topic.
+- **State lifecycle risks:** Config dict is mutated by `auto_resolve` (via `config["_auto_resolve_context"]`). Must deep-copy per sub-run or scope context to a local mapping — otherwise two sub-runs' context strings race.
+- **API surface parity:** `pipeline.run` gains a keyword (`internal_subrun`); callers that don't pass it get the existing behavior. `planner.plan_query` gains the same. Backward compatible.
+- **Integration coverage:** New integration test for the fanout + auto-resolve + render chain. Existing snapshot tests update to include the Resolved block.
+- **Unchanged invariants:** Single-entity `/last30days` invocations (no `--competitors`) behave identically. Explicit `A vs B` comparison topics behave identically. LAW 7 still fires on the default hosting-model path. `render_compact` path is untouched.
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Auto-resolving per competitor triples the WebSearch call volume (4 queries × 3 competitors = 12 extra web searches). | Fast-fail when no backend; user can pass `--competitors-list` to skip discovery but still get auto-resolve. Cost note in CHANGELOG. |
+| Config mutation across sub-runs via `_auto_resolve_context`. | Unit 2 deep-copies config per sub-run before each `auto_resolve` + `pipeline.run` call. Integration test asserts no cross-entity leak. |
+| LAW 7 suppression leaks onto the hosting-model path via a wrong default. | Default `internal_subrun=False`. Only fanout's competitor sub-runs set True. Unit test asserts bare-topic invocation still emits LAW 7. |
+| SKILL.md STEP 0 banner gets ignored by the model (same failure mode as line 823 today). | Put it in the guaranteed-read top band (before LAW 1, above all other content), imperative voice, concrete `STOP` verb. Still not bulletproof but strictly better than current. |
+| Default count change breaks assumptions in downstream tools or existing user muscle memory. | Changelog calls it out as Changed; `--competitors=3` still works for users who want the old default. |
+
+## Documentation / Operational Notes
+
+- Beta channel first: merge behind `/last30days-beta` via the private repo before cherry-picking to public. Follows the same process as 3.0.11.
+- Version 3.0.12 is a fix release; no marketing post required.
+- After merge, add a line to the PR description pointing at this plan.
+
+## Sources & References
+
+- Origin plan: `docs/plans/2026-04-22-002-feat-competitors-flag-comparison-fanout-plan.md`
+- Related PR: #308 (v3.0.11 shipping --competitors)
+- Test windows that surfaced the bugs: Kanye, Linear, Coinbase (2026-04-22 session)
+- Related code: `scripts/lib/fanout.py`, `scripts/lib/resolve.py` (`auto_resolve`), `scripts/lib/planner.py` (`plan_query`), `scripts/lib/render.py` (`render_comparison_multi`)

docs/plans/2026-04-22-004-fix-competitors-hosting-model-resolve-and-leak-plan.md.superseded

@@ -0,0 +1,394 @@
+---
+title: "fix: --competitors runs a full last30days per entity with hosting-model pre-resolve"
+type: fix
+status: active
+date: 2026-04-22
+origin: docs/plans/2026-04-22-003-fix-competitors-per-entity-resolution-plan.md
+---
+
+# fix: --competitors runs a full last30days per entity with hosting-model pre-resolve
+
+## Overview
+
+User intent confirmed 2026-04-22: `--competitors` should run a full single-entity `last30days` pipeline for the main topic AND for each discovered peer — three independent full-depth passes, each with its own Step 0.55 resolution, own X handle primary weight, own subreddit targeting, own GitHub repo scoping. Then merge them into the comparison output.
+
+3.0.12 already built the N-parallel-pipelines orchestration (`scripts/lib/fanout.py`). What it got wrong: it tried to do per-entity Step 0.55 engine-side via `resolve.auto_resolve()`, which requires a web search backend key (BRAVE/EXA/SERPER/PARALLEL/OPENROUTER). Matt runs from Claude Code, which has its own WebSearch tool. The engine has none of those keys, so per-entity auto_resolve silently no-ops and all peer sub-runs fall through to deterministic single-word planner queries.
+
+Four 2026-04-22 test runs (Warriors, Seattle, Arizona Wildcats, Kanye West) confirmed this via engine receipts:
+
+- Compact Resolved Entities block shows peers as `X - | Subs - | GitHub - | Context: -`.
+- Sub-run planner lines show `source=deterministic, subqueries=1` — the "I gave up and keyword-searched" shape.
+- Engine footer keeps nudging `💡 You can unlock native grounded web search with BRAVE_API_KEY or SERPER_API_KEY`, which is wrong advice for a Claude Code user who already has WebSearch.
+- Kanye run leaked main topic's `--subreddits` into Drake's and Kendrick's sub-runs (regression bug).
+
+The fix is to flip the resolution responsibility: the hosting model (Claude Code, Codex, Hermes, Gemini) does Step 0.55 via its own WebSearch tool for every entity, then passes the resolved targeting to the engine via a new `--competitors-plan` JSON flag. Engine fan-out remains — each peer still runs a full `pipeline.run()`. The difference is the peers now arrive with full targeting, equivalent to the main topic, so retrieval is apples-to-apples.
+
+Why not just reuse vs-mode? vs-mode is a SINGLE `pipeline.run()` with a comparison-optimized plan. It pre-resolves Step 0.55 per entity but merges everything into one retrieval pool with lower-weight `--x-related` for peers, merged subreddits, and cross-entity keyword noise. That is not "three full passes." The user explicitly wants three full passes.
+
+## Problem Frame
+
+3.0.12's architecture was correct; its data dependency was wrong.
+
+| Capability | 3.0.12 path | Target path (this plan) |
+|---|---|---|
+| Fan out to N parallel pipelines | Yes (`fanout.run_competitor_fanout`) | Same — keep |
+| Per-entity Step 0.55 resolution | Engine-internal `resolve.auto_resolve()` — needs BRAVE/EXA/SERPER/PARALLEL key | Hosting model does it via its own WebSearch, passes to engine |
+| Per-entity targeting threaded into `pipeline.run()` | Main topic only via outer flags; peers via auto_resolve (failing) or nothing | Main topic via outer flags; peers via `--competitors-plan` JSON |
+| Footer nudge | Unconditional BRAVE/SERPER | Suppressed when `--plan` or `--competitors-plan` present |
+| Resolved Entities block in raw save file | Stdout only | Also in `--save-dir` raw file |
+| Override-leak from main into peers | Present (Kanye receipt) | Fixed via explicit per-entity kwargs scrub |
+| Polymarket noise on ambiguous topics | Present (Warriors, Arizona receipts) | `--polymarket-keywords` + auto-skip for single-token-ambiguous |
+
+The key architectural change is who owns per-entity resolution. The engine stops trying to do it itself; the hosting model does it upstream (it already has WebSearch) and passes results in.
+
+This is the same pattern `--plan` already uses for the main topic: hosting model generates the plan via its own reasoning, passes it in, engine accepts. We apply the pattern to peers.
+
+## Requirements Trace
+
+- R1. New `--competitors-plan` JSON flag accepting per-entity targeting: `x_handle`, `x_related`, `subreddits`, `github_user`, `github_repos`, `context`. Implies `--competitors`. Per-entity values thread into that entity's `pipeline.run()`. Bypasses engine-internal `auto_resolve` for covered entities.
+- R2. SKILL.md "Competitor mode" rewritten to make the hosting-model path canonical: (a) discover N peers via WebSearch, (b) run Step 0.55 per entity (main + peers) via WebSearch, (c) assemble `--competitors-plan` JSON, (d) invoke engine. Engine-internal auto_resolve remains as headless fallback.
+- R3. The LAW 7-style stderr emitted when `--competitors` has no list, no plan, no backend is reframed: leads with "hosting reasoning model, use your WebSearch to run Step 0.55 per entity and pass `--competitors-plan`." Does not lead with BRAVE_API_KEY.
+- R4. Footer nudge `💡 You can unlock native grounded web search with BRAVE_API_KEY...` is suppressed when `--plan` OR `--competitors-plan` was passed. Signal: hosting model is driving and already has WebSearch.
+- R5. Override-leak fix: competitor sub-runs do not inherit main topic's `--subreddits`, `--x-handle`, `--x-related`, `--tiktok-hashtags`, `--tiktok-creators`, `--ig-creators`, `--github-user`, `--github-repo`. Sub-runs use only their own per-entity targeting (from `--competitors-plan` if provided, else engine-internal auto_resolve if backend, else planner defaults).
+- R6. The `## Resolved Entities` block is also appended to the saved raw file when `--save-dir` is in use. Each entity's effective targeting (whatever was actually passed to its `pipeline.run()`) is visible on audit.
+- R6b. When `--save-dir` is in use with a comparison run, each entity's sub-run ALSO saves its own standalone raw file — same format as a single-entity run. `/last30days Kanye West --competitors` produces `kanye-west-raw.md`, `drake-raw.md`, `kendrick-lamar-raw.md` (one per entity) plus the merged comparison file. Matches the historical vs-mode behavior when it ran as N passes.
+- R7. Polymarket disambiguation: support `--polymarket-keywords "kw1,kw2"` to filter market matches; auto-skip Polymarket when topic is single-token-ambiguous and no override is provided.
+- R8. Default `--competitors` count remains 2 (3-way: main + 2 peers). Unchanged from 3.0.12.
+
+## Scope Boundaries
+
+- No changes to `scripts/lib/fanout.py` architecture. N parallel pipelines stays. Only the data each sub-run receives changes.
+- No changes to the vs-mode (topic contains "vs" / "versus") behavior. That path is independent.
+- No new emit modes. Comparison output format unchanged.
+- No deprecation of `--competitors-list`. Stays as the minimum escape hatch for hosting models that skip per-entity Step 0.55 (names-only).
+
+### Deferred to Separate Tasks
+
+- Cache layer for hosting-model competitor resolution: separate plan once cost evidence exists.
+- Cross-source disambiguation beyond Polymarket: separate plan.
+
+## Context & Research
+
+### Relevant Code and Patterns
+
+- `scripts/last30days.py` — `--competitors` / `--competitors-list` argparse block, `resolve_competitors_args` validator, `_main_runner` closure, `_competitor_runner` closure, the `[Competitors] --competitors requires...` stderr block. Primary file for this plan.
+- `scripts/lib/fanout.py` — `run_competitor_fanout` orchestrator. Signature unchanged; `_competitor_runner` closure now builds kwargs from `--competitors-plan`.
+- `scripts/lib/pipeline.py` — `pipeline.run()` signature; no changes required (all per-entity flags already exist as kwargs).
+- `scripts/lib/planner.py` — existing `--plan` parsing and validation, pattern to mirror for `--competitors-plan`.
+- `scripts/lib/render.py` `_render_resolved_entities_block` (added in 3.0.12) — already reads `report.artifacts["resolved"]`; no change needed.
+- `scripts/last30days.py` `save_output` / `render.render_full` — the save path. Needs to include the Resolved Entities block for comparison runs.
+- `scripts/lib/quality_nudge.py` — where the BRAVE/SERPER footer nudge is emitted. Needs a context-aware suppression check.
+- `scripts/lib/polymarket.py` — source adapter. Entry point for `--polymarket-keywords` filter and single-token-ambiguous auto-skip.
+
+### Institutional Learnings
+
+- 3.0.11 plan (`2026-04-22-002`): built the initial fanout, deferred per-entity resolve as "v1 simplification."
+- 3.0.12 plan (`2026-04-22-003`): tried to close the gap via engine-internal `auto_resolve`. Works only with backend keys. Fails silently without.
+- 2026-04-22 test session receipts: confirmed all four fixes in this plan are real, reproducible bugs.
+- User's architectural steer 2026-04-22: "runs a full last30days on all 3 topics" — this plan encodes that explicitly as N full `pipeline.run()` calls with pre-resolved targeting per entity.
+
+### External References
+
+- None. All patterns in-repo.
+
+## Key Technical Decisions
+
+- **`--competitors-plan` is a single JSON flag, not a fan of separate flags.** Mirrors `--plan`. Stable schema: `{entity_name: {x_handle, x_related, subreddits, github_user, github_repos, context}}`. Accept inline JSON or a file path (matches `--plan`).
+- **Hosting-model-driven resolution is the documented default.** Engine-internal `auto_resolve` is the headless / cron fallback. SKILL.md routes hosting models to the JSON-flag path; engine keeps auto_resolve alive for BRAVE/EXA/SERPER users running CI.
+- **Override-leak fix is call-site scrubbing, not a signature change.** `_competitor_runner` builds an explicit kwargs dict per entity from `_subrun_kwargs(entity, plan_entry)`. No closure-default fallthrough from main scope. The 3.0.12 `entity_config = dict(config)` deep-copy pattern extends to every per-entity flag.
+- **Footer nudge becomes context-aware.** Suppressed when `--plan` or `--competitors-plan` present. Not suppressed for bare `--competitors-list` or bare invocations. Headless cron without keys still sees the nudge.
+- **Polymarket disambiguation is additive and conservative.** `--polymarket-keywords` is explicit; auto-skip only fires for a known list of single-token-ambiguous names (states, common nouns). Stderr notes the skip so it is observable and overridable.
+- **Per-entity sub-runs get the full `pipeline.run()` pass.** Same depth, same sources, same API cost per entity as a single-topic run. This is the explicit user intent — three full passes, not one merged pass.
+
+## Open Questions
+
+### Resolved During Planning
+
+- **JSON or multi-flag?** JSON. Matches `--plan`.
+- **Default count?** 2 peers (3-way comparison). Unchanged from 3.0.12.
+- **Does engine-internal auto_resolve stay alive?** Yes, for entities not covered by `--competitors-plan` when a backend is configured. Headless/cron users with keys keep the current 3.0.12 behavior.
+- **vs-mode or fanout?** Fanout. User's explicit ask: three full passes, not one merged pass. vs-mode merges into one pipeline with lower peer weighting, which is not what the user wants.
+- **Does the save file need per-entity clusters?** Start with the Resolved block appended. Per-entity cluster sections can follow in a separate task; they are nice-to-have, not blocking.
+
+### Deferred to Implementation
+
+- Exact trace of override-leak source. Candidates: closure capture of `subreddits` in `_competitor_runner`, shared `_auto_resolve_context` leak, Reddit adapter inheriting global config. Test-first; trace at implementation time.
+- Heuristic for "single-token-ambiguous topic" auto-skip. Start with a short hard-coded list (US state names, US city names, common nouns like "Warriors", "Suns", "Jets"); revisit after dogfood.
+- Whether per-entity coverage warnings fire when `--competitors-plan` under-resolves an entity (e.g., only `x_handle`, no subreddits). Start with stderr logging; revisit UX.
+
+## Implementation Units
+
+- [ ] **Unit 1: `--competitors-plan` JSON flag + per-entity kwargs threading**
+
+**Goal:** New CLI flag accepting per-entity targeting JSON. Each covered entity's `pipeline.run()` receives its own `x_handle` / `x_related` / `subreddits` / `github_user` / `github_repos` / `context`. Skips engine-internal `auto_resolve` for covered entities.
+
+**Requirements:** R1, R5 (primary leak fix site)
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `scripts/last30days.py` (argparse + parse + `_competitor_runner`)
+- Possibly modify: `scripts/lib/fanout.py` (no signature change expected; verify)
+- Test: `tests/test_cli_competitors.py` (extend)
+- Test: `tests/test_competitors_plan_threading.py` (new)
+
+**Approach:**
+- Add `--competitors-plan` argparse flag. Accepts inline JSON OR a file path (mirror `--plan`).
+- Validation: parse JSON; must be a dict; each value must be a dict; unknown fields log warnings; malformed input exits 2.
+- Schema per entity: optional fields `x_handle` (str), `x_related` (list), `subreddits` (list), `github_user` (str), `github_repos` (list), `context` (str).
+- Case-insensitive matching against `--competitors-list` / discovered entities.
+- Build `_subrun_kwargs(entity, plan_entry)` helper. Returns a complete, explicit kwargs dict for `pipeline.run()` with no closure-default fallthrough from main scope. This helper is the single source of truth for per-entity call args. It also fixes the override-leak (R5) by scrubbing all per-entity flags to None unless the plan (or auto_resolve) sets them.
+- `_competitor_runner(entity)`:
+  1. Look up `plan_entry` from `--competitors-plan` (if any).
+  2. If plan covers entity fully, build kwargs from it; skip `auto_resolve`.
+  3. If plan partially covers or is absent, fall back to `auto_resolve` (3.0.12 behavior) when a backend is configured. Plan values win over auto_resolve values on conflict.
+  4. If neither plan nor backend, fall through to `pipeline.run()` with per-entity kwargs all None — engine uses planner defaults for that entity only (no leak).
+- Deep-copy config per sub-run (already done in 3.0.12); merge per-entity `context` into `entity_config["_auto_resolve_context"]` only.
+
+**Execution note:** Test-first for the override-leak regression (pass `--subreddits=A,B` on main + a peer, assert peer's `pipeline.run(subreddits=...)` is None or peer-specific).
+
+**Patterns to follow:**
+- `--plan` parsing at `scripts/last30days.py` (inline JSON or file path).
+- 3.0.12's `_competitor_runner` closure for scope; extract the kwargs-build into `_subrun_kwargs` helper.
+- `entity_config = dict(config)` deep-copy pattern from 3.0.12.
+
+**Test scenarios:**
+- Happy path: `--competitors-plan '{"Drake": {"x_handle":"Drake","subreddits":["Drizzy"]}}'` → Drake's `pipeline.run` receives `x_handle="Drake"` and `subreddits=["Drizzy"]`; no `auto_resolve` call for Drake.
+- Happy path: plan covers 2 of 3 entities, backend configured → covered entities skip auto_resolve; third falls back to auto_resolve.
+- Happy path: plan file path accepted like `--plan` file path.
+- Happy path: case-insensitive entity match (`Drake` in plan, `drake` in list).
+- Edge case: unknown fields in plan entry → logged, ignored, run continues.
+- Edge case: plan entry for entity not in list → ignored with warning.
+- Error path: malformed JSON → exit 2.
+- Error path: top-level JSON is list not dict → exit 2.
+- Regression (leak fix): main `--subreddits=A,B` + `--competitors-list "Drake"` + no plan → Drake's `pipeline.run` receives `subreddits=None` (no leak).
+- Regression (leak fix): same for `--x-handle`, `--x-related`, `--tiktok-*`, `--ig-creators`, `--github-*`.
+- Regression (leak fix): main `--x-handle=kanyewest` + plan `{"Drake":{"x_handle":"Drake"}}` → Drake's sub-run gets `x_handle="Drake"`, NOT `"kanyewest"`.
+- Integration: full main + 2 peers run via `--competitors-plan`; assert each sub-run's effective kwargs match expected per-entity values.
+
+**Verification:**
+- All new and regression tests pass.
+- Smoke run (mock mode + `--competitors-plan`): stderr shows `[Competitors] Drake: x=@Drake subs=Drizzy` line per entity; no `[AutoResolve]` calls for plan-covered entities; no leak of main topic's flags.
+
+- [ ] **Unit 2: Reframe LAW 7-style stderr for hosting-model context**
+
+**Goal:** When `--competitors` has no `--competitors-list`, no `--competitors-plan`, and no backend, stderr tells the hosting reasoning model to use its WebSearch tool for Step 0.55 per entity and pass `--competitors-plan`. Stops leading with BRAVE_API_KEY.
+
+**Requirements:** R3
+
+**Dependencies:** Unit 1 (flag must exist)
+
+**Files:**
+- Modify: `scripts/last30days.py` (the existing `[Competitors] --competitors requires...` block)
+- Test: `tests/test_competitors_no_backend_message.py` (new)
+
+**Approach:**
+- Rewrite stderr in this order:
+  1. "If you are the hosting reasoning model (Claude Code, Codex, Hermes, Gemini, or any agent runtime with a WebSearch tool), YOU should: (a) discover N peers via WebSearch, (b) run Step 0.55 per entity (main + peers), (c) assemble a `--competitors-plan` JSON, (d) re-invoke. Skip this step and quality degrades — peer entities will run with planner defaults."
+  2. "If you are running headless (cron, CI, no hosting model), set BRAVE_API_KEY / EXA_API_KEY / SERPER_API_KEY / PARALLEL_API_KEY / OPENROUTER_API_KEY and re-run."
+  3. "Minimum escape hatch: `--competitors-list "A,B,C"` skips discovery but does not pre-resolve peers. Use only for quick tests."
+- Exits non-zero as today.
+
+**Patterns to follow:**
+- Existing LAW 7 stderr in `planner.plan_query` for tone.
+
+**Test scenarios:**
+- Happy path: stderr leads with "If you are the hosting reasoning model" and names `--competitors-plan` before any backend key.
+- Happy path: stderr explicitly names `--competitors-plan` as the preferred override.
+- Happy path: stderr does NOT say "requires either a configured web search backend OR an explicit --competitors-list" (the current 3.0.12 wording).
+
+**Verification:**
+- Test asserts ordering and required phrases.
+
+- [ ] **Unit 3: Suppress BRAVE/SERPER footer nudge when hosting-model-driven**
+
+**Goal:** The `💡 You can unlock native grounded web search with BRAVE_API_KEY or SERPER_API_KEY` footer is suppressed when `--plan` or `--competitors-plan` was passed (signal: hosting model is driving and already has WebSearch).
+
+**Requirements:** R4
+
+**Dependencies:** Unit 1
+
+**Files:**
+- Modify: `scripts/lib/quality_nudge.py` (or wherever nudge is emitted; verify during implementation)
+- Test: `tests/test_footer_nudge_suppression.py` (new)
+
+**Approach:**
+- Locate the nudge emission point.
+- Add a suppression check: if `--plan` OR `--competitors-plan` was passed, skip the nudge. Otherwise, current behavior.
+- Don't suppress the nudge for bare `--competitors-list` alone — that path isn't necessarily hosting-model-driven.
+
+**Test scenarios:**
+- Happy path: `--plan` passed, no backend → nudge does NOT fire.
+- Happy path: `--competitors-plan` passed, no backend → nudge does NOT fire.
+- Happy path: `--competitors-list` only, no backend → nudge fires (current behavior).
+- Happy path: no `--competitors`, no `--plan`, no backend → nudge fires (current behavior unchanged).
+
+**Verification:**
+- All four scenarios produce expected nudge presence/absence.
+
+- [ ] **Unit 4: Per-entity save files + Resolved block in each**
+
+**Goal:** When `--save-dir` is in use with a comparison run, each entity's sub-run saves its own standalone raw file (same format as a single-entity run), and each file includes the `## Resolved Entities` block so audits can see what targeting that entity received. Matches the historical vs-mode behavior when it was N passes.
+
+**Requirements:** R6, R6b
+
+**Dependencies:** Unit 1
+
+**Files:**
+- Modify: `scripts/last30days.py` (`save_output`, the save loop after fanout completes)
+- Possibly modify: `scripts/lib/render.py` (`render_full` branch to include Resolved block when artifact is present)
+- Test: `tests/test_save_raw_competitor_files.py` (new)
+
+**Approach:**
+- After fanout completes, iterate `report.artifacts["competitor_reports"]`. For each `(entity, entity_report)` tuple, call `save_output(entity_report, emit="md", save_dir=args.save_dir, suffix=args.save_suffix)` — same path a single-entity run takes.
+- Each saved file uses its entity's slug as the filename (`drake-raw.md`, `kendrick-lamar-raw.md`). Main topic keeps the existing `kanye-west-raw.md` filename.
+- Each file includes its own `## Resolved Entities` block (single-entity variant: one row for that entity only). This makes each sub-run's file self-describing — you can see what targeting was used without opening the comparison file.
+- The merged comparison output (stdout) still includes the 3-row Resolved Entities block.
+- Optional: also save a comparison summary file (e.g., `kanye-west-comparison-raw.md`) holding the merged multi-entity render. Start with per-entity files only; comparison summary is a follow-up if stdout-plus-individual-files is insufficient.
+- Single-entity runs unchanged (no additional files, no block change).
+
+**Patterns to follow:**
+- Existing `save_output` invocation for single-entity runs (line 501 of current `scripts/last30days.py`).
+- Existing slug generation (`slugify(topic)`) for filename consistency.
+- `_render_resolved_entities_block` from 3.0.12 for the single-entity variant.
+
+**Test scenarios:**
+- Happy path: `--competitors-list "Drake,Kendrick Lamar"` + `--save-dir=/tmp/x` → `/tmp/x/kanye-west-raw.md`, `/tmp/x/drake-raw.md`, `/tmp/x/kendrick-lamar-raw.md` all exist.
+- Happy path: each peer file's first sections include that entity's Resolved Entities block with its own row only.
+- Happy path: single-entity run with `--save-dir` → one file, unchanged from today's behavior.
+- Edge case: entity slug collides with existing file → overwrite (matches single-entity behavior).
+- Edge case: `--save-suffix=v3` → all 3 files get the suffix (`kanye-west-raw-v3.md`, `drake-raw-v3.md`, `kendrick-lamar-raw-v3.md`).
+- Edge case: comparison run with one peer whose sub-run failed → that entity's file is NOT saved; others are.
+- Integration: stderr after save shows three `[last30days] Saved output to <path>` lines, one per entity.
+
+**Verification:**
+- After `/last30days Kanye West --competitors-list "Drake,Kendrick Lamar" --save-dir=/tmp/x`: `ls /tmp/x/*-raw.md` shows 3 files. Each contains its entity's Resolved block.
+
+- [ ] **Unit 5: SKILL.md "Competitor mode" rewrite — hosting-model Step 0.55 canonical**
+
+**Goal:** SKILL.md documents the hosting-model-driven path as canonical: discover N peers via WebSearch, run Step 0.55 per entity, assemble `--competitors-plan`, invoke engine. Engine-internal `auto_resolve` is labeled the headless fallback.
+
+**Requirements:** R2
+
+**Dependencies:** Unit 1 (flag must exist before documented)
+
+**Files:**
+- Modify: `SKILL.md` (Competitor mode subsection)
+- Modify: `README.md` (one-line example update)
+
+**Approach:**
+- Replace the 3.0.12 Competitor mode subsection with a clear flow:
+  1. User invokes with `--competitors` or `--competitors=N`.
+  2. Hosting model runs WebSearch for "[topic] competitors" / "[topic] alternatives" → picks top N peers.
+  3. Hosting model runs Step 0.55 for main + each peer (x_handle, subreddits, github_user, github_repos, context) — same protocol as vs-mode per SKILL.md §679.
+  4. Hosting model assembles a `--competitors-plan` JSON object.
+  5. Hosting model invokes the engine with `--competitors-list "A,B,C" --competitors-plan '{...}'`.
+  6. Engine fans out N full pipelines (main + peers), each with its own full Step 0.55-grade targeting. Each entity also saves its own `*-raw.md` file when `--save-dir` is set (three full passes → three save files, matching the historical vs-mode behavior). Comparison output merges them for display.
+- Concrete JSON example in SKILL.md showing the schema.
+- Failure-mode warning: a `## Resolved Entities` block with dashes for any entity means hosting model skipped Step 0.55 for that one. Re-run with corrected plan.
+- "Headless fallback" sub-subsection: when BRAVE/EXA/SERPER/PARALLEL/OPENROUTER is set, engine's internal `auto_resolve` handles peers and `--competitors-plan` is optional.
+
+**Patterns to follow:**
+- SKILL.md "Step 0.55" section for per-entity resolve protocol.
+- SKILL.md "If QUERY_TYPE = COMPARISON" section for the same-protocol-as-vs-mode reference.
+- Tone of existing 3.0.12 Competitor mode prose.
+
+**Test scenarios:**
+- Test expectation: none — documentation. Verification is a fresh Claude Code window dogfood run.
+
+**Verification:**
+- `/last30days Kanye West --competitors` in a new window: hosting model does Step 0.55 for Kanye + 2 discovered peers; passes `--competitors-plan`; rendered Resolved block shows non-empty fields for all 3; top voices include at least one peer-specific handle.
+
+- [ ] **Unit 6: Polymarket disambiguation guard**
+
+**Goal:** Support `--polymarket-keywords "kw1,kw2"` to filter market matches; auto-skip Polymarket when topic is single-token-ambiguous and no override is provided.
+
+**Requirements:** R7
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `scripts/last30days.py` argparse (`--polymarket-keywords`)
+- Modify: `scripts/lib/polymarket.py`
+- Test: `tests/test_polymarket_disambiguation.py` (new)
+
+**Approach:**
+- Add `--polymarket-keywords "kw1,kw2"` flag. When provided, Polymarket adapter filters market titles to those whose normalized text contains at least one keyword.
+- Auto-skip rule: if topic is one token AND token matches a known-ambiguous list (US state names, US city names, common sports/color/animal words) AND no `--polymarket-keywords` provided, skip Polymarket with a stderr note.
+- SKILL.md Step 0.55 protocol gets a small addition: for ambiguous topics, hosting model passes `--polymarket-keywords` with topic-specific qualifiers.
+
+**Patterns to follow:**
+- Existing Polymarket adapter match logic.
+- Single-token detection heuristic.
+
+**Test scenarios:**
+- Happy path: topic "Warriors", no override → Polymarket skipped; stderr notes the skip.
+- Happy path: topic "Warriors", `--polymarket-keywords "nba,gsw"` → Polymarket runs; matches filtered.
+- Happy path: topic "OpenAI" (no ambiguity) → Polymarket runs as before.
+- Happy path: topic "Arizona Wildcats" (multi-token) → Polymarket runs as before.
+- Edge case: `--polymarket-keywords ""` → treated as empty, no filter.
+
+**Verification:**
+- Warriors smoke run → Polymarket footer absent OR filtered to nba/gsw markets.
+
+- [ ] **Unit 7: Version 3.0.13, CHANGELOG, sync, hot-copy**
+
+**Goal:** Ship 3.0.13 to all local targets.
+
+**Requirements:** Closes R1-R7
+
+**Dependencies:** Units 1-6
+
+**Files:**
+- Modify: `.claude-plugin/plugin.json`
+- Modify: `CHANGELOG.md`
+- Run: `bash scripts/sync.sh`
+- Hot-copy: `~/.claude/plugins/cache/last30days-skill/last30days/3.0.13/`
+
+**Approach:**
+- CHANGELOG entry groups the fixes: Added `--competitors-plan` JSON flag for per-entity hosting-model pre-resolve. Fixed override-leak from main into peer sub-runs. Changed: LAW 7 stderr framing for hosting-model context. Changed: BRAVE/SERPER footer nudge suppressed when `--plan` / `--competitors-plan` is present. Added: Resolved Entities block persists to saved raw file. Added: `--polymarket-keywords` + auto-skip for ambiguous single-token topics.
+- Beta channel first per CLAUDE.md.
+- Hot-copy so public `/last30days` picks up 3.0.13 immediately.
+
+**Test scenarios:**
+- Test expectation: none — packaging.
+
+**Verification:**
+- `grep version .claude-plugin/plugin.json` returns 3.0.13.
+- `sync.sh` exits 0.
+- Hot-copy contains the new files with competitors.py, fanout.py, the updated SKILL.md, and plugin.json 3.0.13.
+
+## System-Wide Impact
+
+- **Interaction graph:** `_competitor_runner` becomes the single source of truth for sub-run kwargs via `_subrun_kwargs(entity, plan_entry)`. Every per-entity flag flows through one helper. No closure-default leaks.
+- **Error propagation:** `--competitors-plan` JSON parse errors exit 2 with stderr (same as `--plan`). Per-entity plan entries with malformed values log warnings and fall back; don't abort the whole run.
+- **State lifecycle risks:** `entity_config = dict(config)` already deep-copies for `_auto_resolve_context`; extend the isolation discipline to every per-entity flag. Verified in Unit 1 regression tests.
+- **API surface parity:** `--competitors-plan` is additive. `--competitors` and `--competitors-list` unchanged. `--plan` unchanged. `--polymarket-keywords` additive.
+- **Integration coverage:** New regression tests for override-leak. New integration test for plan-driven sub-run threading. New nudge-suppression test. New Polymarket disambiguation test.
+- **Unchanged invariants:** `pipeline.run()` signature unchanged. `planner.plan_query` LAW 7 behavior for the default path unchanged. Single-entity render path unchanged. vs-mode behavior unchanged.
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Hosting model takes the lazy path and uses `--competitors-list` names-only. | Unit 2 stderr explicitly steers to `--competitors-plan` with Step 0.55 protocol named. Unit 5 SKILL.md docs. Resolved Entities dashes in output make the gap visible. |
+| JSON gets verbose for the hosting model to construct repeatedly. | Schema is small (≤6 fields per entity). Hosting model already runs Step 0.55 for main topic in every comparison run; peers use the same protocol. One JSON block replaces N CLI flags. |
+| Override-leak source is deeper than `_competitor_runner` closure. | Test-first per Unit 1. Receipts from 2026-04-22 Kanye run are reproducible. Trace methodically from call site. |
+| Plan-covered entity bypasses auto_resolve but plan data is incomplete (e.g., no subreddits). | Hosting model's own SKILL.md contract says Step 0.55 must cover all fields. Stderr logs per-entity coverage so under-resolved entities are visible. Next-run correction, not engine-side rescue. |
+| Polymarket auto-skip false-positives on legitimate ambiguous topics with real markets. | Conservative match (single-token + known list). `--polymarket-keywords` override is explicit and unambiguous. Stderr notes the skip. |
+| Footer nudge suppression hides the message from headless users who genuinely need it. | Suppression only fires when `--plan` or `--competitors-plan` is present. Cron / CI runs that pass neither still see the nudge. |
+
+## Documentation / Operational Notes
+
+- Beta channel first per CLAUDE.md (private repo `/last30days-beta`).
+- After merge: hot-copy to `~/.claude/plugins/cache/last30days-skill/last30days/3.0.13/`.
+- CHANGELOG voice should call this out as the feedback-driven follow-up to 3.0.12. Reader should see "we tried engine-internal resolve in 3.0.12; it needs backend keys we don't have; we moved resolution to the hosting model in 3.0.13."
+
+## Sources & References
+
+- Origin plan (3.0.12): `docs/plans/2026-04-22-003-fix-competitors-per-entity-resolution-plan.md`
+- Earlier plan (3.0.11): `docs/plans/2026-04-22-002-feat-competitors-flag-comparison-fanout-plan.md`
+- 2026-04-22 test session receipts: Warriors, Seattle, Arizona Wildcats, Kanye West
+- SKILL.md §551 "If QUERY_TYPE = COMPARISON" and §679 per-entity Step 0.55 protocol
+- Related code: `scripts/lib/fanout.py`, `scripts/last30days.py` `_competitor_runner`, `scripts/lib/render.py` `_render_resolved_entities_block`, `scripts/lib/polymarket.py`, `scripts/lib/quality_nudge.py`
+- Related PRs: #308 (3.0.11), #309 (3.0.12)

docs/plans/2026-04-22-005-feat-vs-mode-n-passes-competitors-auto-discovery-plan.md

@@ -0,0 +1,451 @@
+---
+title: "feat: vs mode runs N full passes and --competitors is vs with auto-discovery"
+type: feat
+status: active
+date: 2026-04-22
+origin: docs/plans/2026-04-22-004-fix-competitors-hosting-model-resolve-and-leak-plan.md.superseded
+---
+
+# feat: vs mode runs N full passes and --competitors is vs with auto-discovery
+
+## Overview
+
+Architectural unification driven by user correction 2026-04-22: vs mode and `--competitors` are the same thing. A user typing `/last30days OpenAI vs Anthropic vs xAI` should get a full single-entity last30days pass for each of the three entities — three full pipelines, three saved `*-raw.md` files, merged into one comparison output. A user typing `/last30days OpenAI --competitors` should get the same output after the hosting model auto-picks 2 peers; i.e., `--competitors` is a thin shortcut that expands "topic + `--competitors`" into "topic vs peer1 vs peer2" and then runs the unified vs pipeline.
+
+Current state diverges from this:
+
+- **vs mode today**: one `pipeline.run()` with a comparison-optimized plan that merges all entities' targeting into a single retrieval pool. Lower-weight `--x-related` for peers, merged subreddits, cross-entity keyword noise. One saved file.
+- **`--competitors` today (3.0.12)**: N parallel `pipeline.run()` calls via `scripts/lib/fanout.py`, but per-entity Step 0.55 depends on an engine-side web backend key Matt doesn't have. Silently degrades to planner defaults for peers. One saved file (main topic only). Override-leak from main into peers.
+
+After this plan:
+
+- **vs mode**: N parallel `pipeline.run()` calls, one per entity, each with its own full Step 0.55-grade targeting, each saving its own `*-raw.md`. Merged into one comparison output.
+- **`--competitors`**: SKILL.md shortcut. Hosting model discovers N peers, builds `"topic vs peer1 vs peer2"`, and invokes the same vs pipeline. No separate orchestration path.
+- **Same fanout machinery (`scripts/lib/fanout.py`)** serves both. One fix, both behaviors improve.
+
+## Problem Frame
+
+The product insight from 2026-04-22 test runs is simple: the user wants three full last30days reports plus a comparison merge. Not one comparison pass with N-way targeting merged into a single retrieval pool. Not one save file. Not "main gets Step 0.55, peers get planner defaults." Three full passes. Three save files. Merged output.
+
+The historical vs mode did that (it ran as 3 passes, saving 3 files). SKILL.md §551 currently says:
+
+> "When the user asks 'X vs Y', run ONE research pass with a comparison-optimized plan that covers both entities AND their rivalry. This replaces the old 3-pass approach (which took 13+ minutes and produced tangential content)."
+
+That change was a latency optimization that removed the user-visible behavior the user wants. The fix is to revert the architectural direction: N passes per entity, in parallel rather than serial (parallelism lowers wall-clock to ~1× a single pass, not N×), with per-entity save files.
+
+The 3.0.11 `--competitors` flag already introduced parallel N-pass machinery (`fanout.run_competitor_fanout`). The 3.0.12 follow-up tried to wire per-entity Step 0.55 into it but failed when no web backend was configured. The elegant move: stop maintaining two architectures. vs-mode and `--competitors` both use `fanout.py`. `--competitors` becomes a SKILL.md-level shortcut that discovers 2 peers and hands off to vs-mode.
+
+Four 2026-04-22 test receipts (Warriors, Seattle, Arizona Wildcats, Kanye West) all confirmed the user's pain points:
+
+- Peers thin because they ran without per-entity handle/sub targeting.
+- Only one `*-raw.md` per run — no per-entity audit.
+- Kanye peers leaked main topic's `--subreddits`.
+- Engine footer nudging `BRAVE_API_KEY` to Claude Code users who already have WebSearch.
+- Polymarket noise on ambiguous topics (Warriors → Glasgow rugby; Arizona → Diamondbacks).
+
+This plan closes all of them by unifying the architecture and making hosting-model-driven Step 0.55 per entity the canonical path.
+
+## Requirements Trace
+
+- R1. vs mode (any topic containing ` vs ` / ` versus `) runs N full `pipeline.run()` calls in parallel, one per entity. Each sub-run uses its entity's own Step 0.55 targeting (from the hosting model's pre-resolution, passed via a new `--competitors-plan` JSON).
+- R2. `--competitors` (and `--competitors=N`) becomes a SKILL.md-level shortcut: the hosting model (a) discovers N peers via WebSearch, (b) runs Step 0.55 per entity (main + peers), (c) rewrites the topic to `"main vs peer1 vs peer2"`, (d) invokes the engine with `--competitors-plan` containing each entity's targeting.
+- R3. New `--competitors-plan` JSON flag. Schema: `{entity_name: {x_handle, x_related, subreddits, github_user, github_repos, context}}`. Implies vs mode when present with a single-entity topic. Applies per-entity targeting to each sub-run. Accepts inline JSON or a file path (matches `--plan`).
+- R4. Each entity's sub-run saves its own `*-raw.md` file when `--save-dir` is in use. Example: `/last30days "Kanye West vs Drake vs Kendrick Lamar" --save-dir=~/Documents/Last30Days` produces `kanye-west-raw.md`, `drake-raw.md`, `kendrick-lamar-raw.md`. Same filenames a single-entity run of each topic would produce. Matches historical vs-mode behavior.
+- R5. Each per-entity saved file includes its own single-row `## Resolved Entities` block so the audit survives. The merged comparison stdout still shows the full 3-row block.
+- R6. Override-leak fix: no main-topic flags (`--subreddits`, `--x-handle`, `--x-related`, `--tiktok-*`, `--ig-creators`, `--github-*`) leak into peer sub-runs. Every per-entity kwarg is scrubbed at the sub-run call site.
+- R7. LAW 7-style stderr for `--competitors` invocations with no list, no plan, no backend is reframed for hosting-model context: leads with "use your WebSearch to discover peers, resolve Step 0.55 per entity, re-invoke with `topic vs peer1 vs peer2 --competitors-plan '...'`." Does not lead with BRAVE_API_KEY.
+- R8. Footer nudge `💡 You can unlock native grounded web search with BRAVE_API_KEY...` is suppressed when `--plan` or `--competitors-plan` was passed.
+- R9. Polymarket disambiguation: support `--polymarket-keywords "kw1,kw2"` to filter market matches; auto-skip Polymarket when topic is single-token-ambiguous and no override is provided.
+- R10. Default `--competitors` count stays 2 peers (3-way comparison). Unchanged from 3.0.12.
+
+## Scope Boundaries
+
+- No changes to single-entity `pipeline.run()` semantics. Each sub-run in vs mode behaves identically to a bare `/last30days {entity}` invocation.
+- No changes to the planner's comparison-intent logic for single-entity-containing topics. The `_should_force_deterministic_plan` shortcut for vs-topics routes to fanout, not to its current single-pipeline path.
+- No new emit modes. Comparison output format unchanged.
+- No removal of `--competitors-list`. Stays as a minimum escape hatch (names-only, no per-entity targeting) for scripted headless use.
+- No removal of engine-internal `resolve.auto_resolve()` in fanout. Remains as headless / cron fallback for users with BRAVE/EXA/SERPER/PARALLEL/OPENROUTER keys. The dominant Claude Code path bypasses it via `--competitors-plan`.
+
+### Deferred to Separate Tasks
+
+- Explicit "head-to-head" rivalry pass in vs-mode (a supplemental subquery like `"A vs B"` that catches rivalry articles missing from pure entity-scoped passes). Start with N independent passes; add a head-to-head supplemental pass if the rivalry-content gap shows up in dogfood.
+- Cache layer for hosting-model pre-resolution.
+- Cross-source disambiguation (not just Polymarket).
+- Latency knob for users who want the old one-pass vs behavior (probably not needed; parallel N-pass is ~1× wall clock).
+
+## Context & Research
+
+### Relevant Code and Patterns
+
+- `scripts/last30days.py` — main(), `_main_runner`, `_competitor_runner`, the competitor enable/discovery branch. Primary file.
+- `scripts/lib/fanout.py` — existing orchestrator (3.0.11). Reused as-is; `competitor_runner` closure is where per-entity kwargs apply.
+- `scripts/lib/planner.py` — `_should_force_deterministic_plan` detects vs-topics via regex. Current path synthesizes ONE comparison plan; new path routes to fanout.
+- `scripts/lib/render.py` — `render_comparison_multi` (3.0.12) + `_render_resolved_entities_block`. Both reused. `render_full` needs a per-entity variant when saving sub-run files.
+- `scripts/last30days.py` `save_output` — where raw files are written. Needs to iterate per entity when competitor_reports artifact present.
+- `scripts/lib/quality_nudge.py` — BRAVE/SERPER nudge emission.
+- `scripts/lib/polymarket.py` — source adapter for `--polymarket-keywords` and ambiguous-topic auto-skip.
+- SKILL.md §551 "If QUERY_TYPE = COMPARISON" and §679 per-entity Step 0.55 protocol — the hosting-model contract that drives per-entity pre-resolution for both vs mode and `--competitors`.
+
+### Institutional Learnings
+
+- 3.0.11 plan (`2026-04-22-002`): built fanout.
+- 3.0.12 plan (`2026-04-22-003`): tried engine-internal per-entity auto_resolve; failed without backend keys.
+- 3.0.13 plan draft (`2026-04-22-004-...superseded`): proposed `--competitors-plan` JSON + vs-mode-shortcut path but kept them separate. User's 2026-04-22 correction unifies them.
+- 2026-04-22 test receipts: Warriors, Seattle, Arizona Wildcats, Kanye West runs all reproduced the per-entity resolve gap.
+- User's architectural steer: "vs mode should work that way too" + "--competitors is just vs mode with auto-discovery." This plan encodes that.
+
+### External References
+
+- None. All patterns in-repo.
+
+## Key Technical Decisions
+
+- **Unify vs-mode and --competitors on one orchestrator.** `fanout.run_competitor_fanout` serves both. vs-mode is "topic contains ' vs '" detection → fanout. `--competitors` is "SKILL.md shortcut → hosting model rewrites topic to vs form → fanout." One code path.
+- **Per-entity targeting via `--competitors-plan` JSON.** Schema `{entity_name: {x_handle, x_related, subreddits, github_user, github_repos, context}}`. Mirrors `--plan`. Applies to both vs-mode and `--competitors` paths. Hosting model passes it after running Step 0.55 per entity.
+- **N save files, one per entity.** Each sub-run writes a `{entity-slug}-raw.md` file when `--save-dir` is set. Matches historical vs-mode behavior. Single-entity runs unchanged.
+- **Revert the "one pass for latency" optimization that removed per-entity passes.** Parallel execution via `ThreadPoolExecutor` means wall-clock is ~max(per-entity-latency), not sum. The old latency concern (13+ minutes for 3 serial passes) does not apply to a parallel fan-out.
+- **Override-leak fix at the call site.** `_subrun_kwargs(entity, plan_entry)` helper returns fully explicit per-entity kwargs; no closure-default fallthrough from main scope.
+- **LAW 7 stderr reframed, not just updated.** Current message treats BRAVE_API_KEY as the solution. New message treats hosting-model Step 0.55 as the solution, with backend keys listed only as the headless fallback.
+- **Polymarket disambiguation is additive and conservative.** `--polymarket-keywords` is explicit; auto-skip only fires for a known-ambiguous single-token list.
+
+## Open Questions
+
+### Resolved During Planning
+
+- **vs mode N passes or single-pass?** N passes. User's architectural correction.
+- **Should --competitors still be an engine flag at all?** Yes, kept for headless / cron contexts with backend keys. Dominant Claude Code path is SKILL.md shortcut → vs-mode fanout. Engine flag stays as compatibility surface.
+- **`--competitors-plan` JSON or multi-flag?** JSON. Matches `--plan`.
+- **Default count?** 2 peers → 3-way comparison. Unchanged.
+- **Saved-file naming?** `{entity-slug}-raw.md` per entity, same as single-entity runs would produce.
+
+### Deferred to Implementation
+
+- Exact trace of override-leak path (closure capture vs shared config vs Reddit adapter fallback). Test-first per Unit 2; patch at the right layer.
+- Heuristic for single-token-ambiguous Polymarket auto-skip. Start with a short hard-coded list; iterate.
+- Whether to include a head-to-head rivalry supplemental pass in vs-mode. Ship N-independent passes first; revisit after dogfood if rivalry content is missing.
+- Exact filename convention when the comparison merged output is saved (if saved at all). Not blocking — per-entity files are the primary save artifact.
+
+## High-Level Technical Design
+
+> *This illustrates the intended approach and is directional guidance for review, not implementation specification. The implementing agent should treat it as context, not code to reproduce.*
+
+```
+User invokes:
+   /last30days "OpenAI vs Anthropic vs xAI"
+   OR
+   /last30days OpenAI --competitors     (hosting model rewrites to vs form)
+   OR
+   /last30days OpenAI --competitors-list "Anthropic,xAI"
+   OR
+   /last30days "OpenAI vs Anthropic vs xAI" --competitors-plan '{...per-entity...}'
+
+           ↓
+
+scripts/last30days.py main():
+   - Detect: topic has " vs " OR --competitors enabled
+   - If --competitors and no list/plan: emit LAW 7-style stderr with hosting-model instruction
+   - If --competitors with list or discovery: rewrite topic to vs form, continue
+   - Parse --competitors-plan JSON, map to entities
+
+           ↓
+
+fanout.run_competitor_fanout (shared path):
+   - For each entity (main + peers):
+       - entity_config = dict(config)  [deep copy to prevent leak]
+       - kwargs = _subrun_kwargs(entity, plan_entry)  [explicit; no main-topic leak]
+       - If plan_entry missing a field AND backend available: auto_resolve() fill
+       - pipeline.run(topic=entity, **kwargs, internal_subrun=True)
+   - Parallel ThreadPoolExecutor
+   - Collect per-entity Reports
+   - Attach resolved targeting to each Report.artifacts["resolved"]
+
+           ↓
+
+scripts/last30days.py after fanout:
+   - If --save-dir: save each entity's Report as {entity-slug}-raw.md
+     Each file includes its own single-row Resolved Entities block
+   - emit_comparison_output → render_comparison_multi (merged stdout)
+     Includes full N-row Resolved Entities block
+```
+
+## Implementation Units
+
+- [ ] **Unit 1: vs-topic detection routes to fanout (not single-pipeline)**
+
+**Goal:** A topic containing ` vs ` / ` versus ` triggers `fanout.run_competitor_fanout` with the parsed entities. Each entity runs a full `pipeline.run()`. Replace the current single-pipeline-with-comparison-plan behavior.
+
+**Requirements:** R1
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `scripts/last30days.py` (main() — detect vs-topic, route to fanout)
+- Modify: `scripts/lib/planner.py` (remove / bypass the `_should_force_deterministic_plan` special case for vs topics; vs topics no longer go through `plan_query` as a single comparison plan)
+- Test: `tests/test_vs_mode_fanout.py` (new)
+
+**Approach:**
+- Parse the incoming topic: if it contains ` vs ` or ` versus ` (case-insensitive), split into entities (reuse `planner._comparison_entities`-style logic or move that utility into main()).
+- When vs-entities are detected, route to the same fanout branch `--competitors` uses today. The entity list comes from the topic string; no discovery step needed.
+- Each entity runs `pipeline.run()` with its own plan (either from `--competitors-plan[entity]` or from the engine's per-entity fallback path).
+- For back-compat, if the user passes both a vs-topic AND `--plan`, honor `--plan` for the main (first) entity and use per-entity defaults for peers unless `--competitors-plan` is also provided.
+
+**Execution note:** Start with an integration test that runs `"A vs B"` via mock mode and asserts fanout was called with two entities + two pipeline.run calls.
+
+**Patterns to follow:**
+- 3.0.11 fanout wiring in `scripts/last30days.py`'s `--competitors` branch.
+- `planner._comparison_entities` for the split logic.
+
+**Test scenarios:**
+- Happy path: topic `"A vs B"` → two pipeline.run calls, two Reports returned, merged render.
+- Happy path: topic `"A vs B vs C"` → three pipeline.run calls.
+- Happy path: topic `"A versus B"` → matches the same regex, two pipelines.
+- Edge case: topic `"OpenAI vs"` (trailing empty entity) → treated as single-entity `"OpenAI"`, not vs mode.
+- Edge case: topic contains "vs." (dot, no trailing space) → existing regex tolerates it; verify.
+- Edge case: topic `"A vs B"` plus `--plan` → plan applies to first entity only, peers use per-entity defaults.
+- Integration: full vs-mode run end-to-end in mock mode; verify rendered output, stderr has one `[Competitors] Comparing: A vs B vs ...` line.
+
+**Verification:**
+- Test assertions pass.
+- Mock-mode smoke of `/last30days "OpenAI vs Anthropic"` shows fanout invocation, per-entity Reports, merged comparison output.
+
+- [ ] **Unit 2: `--competitors-plan` JSON flag + `_subrun_kwargs` helper + override-leak fix**
+
+**Goal:** New JSON flag threads per-entity targeting into each sub-run's `pipeline.run()`. A `_subrun_kwargs(entity, plan_entry)` helper is the single source of truth for per-entity kwargs, eliminating override-leak.
+
+**Requirements:** R3, R6
+
+**Dependencies:** None (can land alongside or before Unit 1)
+
+**Files:**
+- Modify: `scripts/last30days.py` (argparse + parse + `_competitor_runner` + `_subrun_kwargs` helper)
+- Possibly modify: `scripts/lib/fanout.py` (no signature change expected; the competitor_runner contract is unchanged)
+- Test: `tests/test_cli_competitors.py` (extend)
+- Test: `tests/test_competitors_plan_threading.py` (new)
+- Test: `tests/test_competitor_subrun_isolation.py` (new, regression)
+
+**Approach:**
+- Add `--competitors-plan` argparse flag. Accepts inline JSON or file path (mirror `--plan`).
+- Validation: top-level dict; each value is a dict; unknown fields log warnings; malformed input exits 2. Case-insensitive entity matching.
+- Schema: `{entity_name: {x_handle?, x_related?, subreddits?, github_user?, github_repos?, context?}}`.
+- Build `_subrun_kwargs(entity, plan_entry)` — returns an explicit dict with every per-entity flag. No closure-default fallthrough. This is the leak fix.
+- `_competitor_runner(entity)`:
+  1. Get `plan_entry` from `--competitors-plan` if present.
+  2. Build base kwargs with `_subrun_kwargs(entity, plan_entry)`.
+  3. Fill missing fields via `resolve.auto_resolve(entity, entity_config)` only if backend is configured (3.0.12 fallback path).
+  4. Call `pipeline.run(topic=entity, internal_subrun=True, **kwargs)`.
+  5. Attach `resolved` dict to `report.artifacts`.
+- Verify no per-entity flag from main() leaks via closure. The helper is the only source of per-entity values.
+
+**Execution note:** Test-first for the override-leak regression. Use the Kanye 2026-04-22 receipt as the failing test input (main `--subreddits=Kanye,hiphopheads` + `--competitors-list "Drake"` → assert Drake's pipeline.run receives `subreddits=None`).
+
+**Patterns to follow:**
+- `--plan` parsing block in `scripts/last30days.py`.
+- 3.0.12's `entity_config = dict(config)` deep-copy pattern.
+
+**Test scenarios:**
+- Happy path: `--competitors-plan '{"Drake":{"x_handle":"Drake","subreddits":["Drizzy"]}}'` → Drake's pipeline.run receives `x_handle="Drake"`, `subreddits=["Drizzy"]`. No auto_resolve call for Drake.
+- Happy path: plan covers 2 of 3 entities, backend configured → covered skip auto_resolve; third falls back.
+- Happy path: plan file path accepted like `--plan`.
+- Happy path: case-insensitive entity match.
+- Edge case: unknown fields → warn, ignore.
+- Edge case: plan entry for entity not in list → warn, ignore.
+- Error path: malformed JSON → exit 2.
+- Error path: top-level JSON is list → exit 2.
+- Regression (leak): main `--subreddits=A,B` + `--competitors-list "X"` + no plan → X's pipeline.run gets `subreddits=None`.
+- Regression (leak): same for `--x-handle`, `--x-related`, `--tiktok-hashtags`, `--tiktok-creators`, `--ig-creators`, `--github-user`, `--github-repo`.
+- Regression (leak): main `--x-handle=kanye` + plan `{"Drake":{"x_handle":"Drake"}}` → Drake's sub-run gets `x_handle="Drake"`, NOT `"kanye"`.
+
+**Verification:**
+- All regression tests pass.
+- Smoke run (mock mode + plan): stderr shows per-entity `[Competitors] {entity}: x=... subs=...` line; no leak from main topic's flags.
+
+- [ ] **Unit 3: Per-entity save files**
+
+**Goal:** When `--save-dir` is set in a vs-mode or `--competitors` run, each entity's sub-run saves its own `{entity-slug}-raw.md` file — same format as a single-entity run would produce.
+
+**Requirements:** R4, R5
+
+**Dependencies:** Unit 1, Unit 2
+
+**Files:**
+- Modify: `scripts/last30days.py` (`save_output` iteration after fanout)
+- Modify: `scripts/lib/render.py` (`render_full` includes single-row Resolved Entities block when that entity's `artifacts["resolved"]` is present)
+- Test: `tests/test_save_raw_per_entity.py` (new)
+
+**Approach:**
+- After fanout completes, iterate `report.artifacts["competitor_reports"]` (or equivalent). For each `(entity, entity_report)`:
+  - Call `save_output(entity_report, emit="md", save_dir=args.save_dir, suffix=args.save_suffix)`.
+  - Uses entity's `slugify(entity)` for the filename. Same pattern a single-entity run uses.
+- Each saved file invokes `render_full` (or the save-variant). `render_full` now checks for `report.artifacts["resolved"]` and prepends a single-row Resolved Entities block.
+- Stderr logs one `[last30days] Saved output to <path>` line per entity.
+- Single-entity runs unchanged (no extra files, render_full unchanged for them).
+
+**Patterns to follow:**
+- Existing `save_output` invocation in main() for single-entity runs.
+- `slugify(topic)` for filename.
+- 3.0.12's `_render_resolved_entities_block` (reused, single-row mode).
+
+**Test scenarios:**
+- Happy path: `/last30days "A vs B vs C" --save-dir=/tmp/x` → `/tmp/x/a-raw.md`, `/tmp/x/b-raw.md`, `/tmp/x/c-raw.md` exist.
+- Happy path: `--competitors-list "Drake,Kendrick" --save-dir=/tmp/x` on topic Kanye → three files: `kanye-west-raw.md`, `drake-raw.md`, `kendrick-lamar-raw.md`.
+- Happy path: each file includes a single-row Resolved Entities block for its entity.
+- Happy path: single-entity run with `--save-dir` → one file, no Resolved block (unchanged).
+- Edge case: `--save-suffix=v3` → all N files get the suffix.
+- Edge case: one entity sub-run failed → its file is NOT saved; the others are.
+- Integration: `ls {save-dir}/*-raw.md` returns N files after a vs-mode run.
+
+**Verification:**
+- Test assertions pass.
+- Manual vs-mode smoke saves N files.
+
+- [ ] **Unit 4: LAW 7-style stderr reframe + footer-nudge suppression**
+
+**Goal:** The `--competitors`-with-no-backend stderr tells the hosting model to do Step 0.55 per entity and pass `--competitors-plan`. The BRAVE/SERPER footer nudge is suppressed when `--plan` or `--competitors-plan` is present.
+
+**Requirements:** R7, R8
+
+**Dependencies:** Unit 2 (flag must exist)
+
+**Files:**
+- Modify: `scripts/last30days.py` (the `[Competitors] --competitors requires...` stderr block)
+- Modify: `scripts/lib/quality_nudge.py` (or wherever footer nudge emits; verify during implementation)
+- Test: `tests/test_competitors_no_backend_message.py` (new)
+- Test: `tests/test_footer_nudge_suppression.py` (new)
+
+**Approach:**
+- Rewrite stderr in this order:
+  1. "If you are the hosting reasoning model (Claude Code, Codex, Hermes, Gemini, or any agent with WebSearch), the recommended path: (a) discover N peers via WebSearch, (b) run Step 0.55 for main + each peer, (c) re-invoke as `/last30days 'topic vs peer1 vs peer2' --competitors-plan '{...}'`. See SKILL.md 'Competitor mode'."
+  2. "Headless / cron path: set BRAVE_API_KEY / EXA_API_KEY / SERPER_API_KEY / PARALLEL_API_KEY / OPENROUTER_API_KEY and re-run."
+  3. "Minimum escape hatch: `--competitors-list 'A,B,C'` skips discovery but does not pre-resolve peers."
+- Suppress footer nudge when `external_plan` OR `competitors_plan` was passed.
+
+**Test scenarios:**
+- Happy path: `--competitors` with no backend, no list, no plan → stderr leads with "If you are the hosting reasoning model" and references `--competitors-plan` before naming API keys.
+- Happy path: `--plan` passed → footer nudge does NOT fire.
+- Happy path: `--competitors-plan` passed → footer nudge does NOT fire.
+- Happy path: `--competitors-list` only (no plan, no backend) → footer nudge still fires (hosting model didn't fully engage).
+- Happy path: no `--competitors`, no `--plan` → footer nudge unchanged.
+
+**Verification:**
+- Tests pass.
+
+- [ ] **Unit 5: Polymarket disambiguation guard**
+
+**Goal:** `--polymarket-keywords "kw1,kw2"` filters market matches; auto-skip Polymarket on single-token-ambiguous topics without override.
+
+**Requirements:** R9
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `scripts/last30days.py` (argparse)
+- Modify: `scripts/lib/polymarket.py`
+- Test: `tests/test_polymarket_disambiguation.py` (new)
+
+**Approach:**
+- Add `--polymarket-keywords "kw1,kw2"`. When provided, Polymarket adapter filters market titles to those whose normalized text contains at least one keyword.
+- Auto-skip: if topic is one token AND matches a known-ambiguous list (US state names, US city names, common sports/color/animal words) AND no `--polymarket-keywords`, skip Polymarket with stderr note.
+- SKILL.md update (small): mention `--polymarket-keywords` in Step 0.55 instructions for ambiguous topics.
+
+**Test scenarios:**
+- Happy path: topic "Warriors", no override → Polymarket skipped; stderr note.
+- Happy path: topic "Warriors", `--polymarket-keywords "nba,gsw"` → Polymarket runs, filtered.
+- Happy path: topic "OpenAI" → Polymarket runs as before.
+- Happy path: topic "Arizona Wildcats" (multi-token) → Polymarket runs as before.
+- Edge case: `--polymarket-keywords ""` → treated as empty, no filter.
+
+**Verification:**
+- Warriors smoke → Polymarket footer absent or filtered.
+
+- [ ] **Unit 6: SKILL.md rewrite — vs mode is the canonical path, `--competitors` is a shortcut**
+
+**Goal:** SKILL.md documents the unified architecture. vs mode runs N full passes. `--competitors` is a SKILL.md-level shortcut that discovers 2 peers and invokes vs mode with `--competitors-plan`.
+
+**Requirements:** R1, R2, R10 (surfaces them)
+
+**Dependencies:** Units 1-4
+
+**Files:**
+- Modify: `SKILL.md` (§551 "If QUERY_TYPE = COMPARISON" rewrite; Competitor mode subsection rewrite)
+- Modify: `README.md` (one-line example)
+
+**Approach:**
+- Rewrite §551 to describe the N-pass architecture: "When the user asks 'X vs Y' (or 'X vs Y vs Z'), run Step 0.55 per entity, then invoke the engine. The engine fans out N full pipelines in parallel. Each entity gets its own single-entity-grade coverage. Wall clock is close to a single run."
+- Remove the "ONE research pass with a comparison-optimized plan that replaces the old 3-pass approach" language.
+- Add a `--competitors-plan` JSON example.
+- Rewrite the Competitor mode subsection: "`--competitors` is a shortcut. The hosting model: (1) runs WebSearch to discover N=2 peers, (2) runs Step 0.55 for main + each peer, (3) rewrites topic to `'main vs peer1 vs peer2'`, (4) invokes engine with `--competitors-plan '{...}'`. Engine flag `--competitors` and `--competitors-list` remain for headless fallback."
+- Cross-reference §679 (per-entity Step 0.55 protocol).
+- Warning: a thin `## Resolved Entities` block (dashes for any entity) means the hosting model skipped Step 0.55 for that one.
+
+**Patterns to follow:**
+- Existing §679 per-entity Step 0.55 protocol for tone.
+- 3.0.12 Competitor mode prose for terseness.
+
+**Test scenarios:**
+- Test expectation: none — documentation. Verification is dogfood.
+
+**Verification:**
+- `/last30days "OpenAI vs Anthropic vs xAI"` in a fresh Claude Code window produces 3 save files with populated Resolved blocks and non-dash per-entity targeting.
+- `/last30days OpenAI --competitors` produces same after discovery step.
+
+- [ ] **Unit 7: Version 3.0.13, CHANGELOG, sync, hot-copy**
+
+**Goal:** Ship 3.0.13 to all local targets.
+
+**Requirements:** Closes R1-R10
+
+**Dependencies:** Units 1-6
+
+**Files:**
+- Modify: `.claude-plugin/plugin.json`
+- Modify: `CHANGELOG.md`
+- Run: `bash scripts/sync.sh`
+- Hot-copy: `~/.claude/plugins/cache/last30days-skill/last30days/3.0.13/`
+
+**Approach:**
+- CHANGELOG: group the changes. "Changed: vs mode now runs N full passes in parallel, one per entity — reverting the one-pass optimization to restore per-entity depth. Added: --competitors-plan JSON for per-entity Step 0.55 targeting (applies to vs mode and --competitors). Changed: --competitors is now a SKILL.md shortcut for vs-with-discovery. Added: per-entity *-raw.md save files. Fixed: override-leak from main to peer sub-runs. Changed: LAW 7 stderr framing for hosting-model context. Changed: BRAVE/SERPER footer nudge suppressed when --plan / --competitors-plan present. Added: --polymarket-keywords + auto-skip for ambiguous topics."
+- Beta channel first per CLAUDE.md.
+- Hot-copy so public `/last30days` picks up 3.0.13.
+
+**Test scenarios:**
+- Test expectation: none — packaging.
+
+**Verification:**
+- `grep version .claude-plugin/plugin.json` → 3.0.13.
+- `sync.sh` exits 0.
+- Hot-copy contains the new files.
+
+## System-Wide Impact
+
+- **Interaction graph:** vs-mode and `--competitors` share one orchestrator (`fanout.run_competitor_fanout`). `_subrun_kwargs` is the single source of per-entity kwargs. Save loop iterates per entity.
+- **Error propagation:** Per-entity sub-run failure → logged, dropped, continue (3.0.11 behavior unchanged). `--competitors-plan` JSON parse errors exit 2 (same shape as `--plan`).
+- **State lifecycle risks:** `entity_config = dict(config)` deep-copy pattern extends to every per-entity flag (Unit 2 fix). No cross-entity context leak.
+- **API surface parity:** `--competitors-plan` is additive. `--competitors`, `--competitors-list`, `--plan` unchanged. `--polymarket-keywords` additive. vs-mode keeps its topic-string surface.
+- **Integration coverage:** New vs-mode-fanout integration test. New override-leak regression test. New plan-threading test. New nudge-suppression test. New per-entity-save test. New Polymarket disambiguation test.
+- **Unchanged invariants:** `pipeline.run()` signature unchanged. Single-entity render path unchanged. LAW 7 on the default path unchanged (still fires when a single-entity run lacks `--plan`).
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| vs-mode N-pass latency feels slower for users who remember the one-pass shortcut. | Parallel execution keeps wall-clock ~= max(per-entity-latency), not sum. `--quick` on a vs-topic still applies to each sub-run. CHANGELOG calls out the revert + parallelism. |
+| API cost scales linearly with N (per source). | Default count 2 caps it. Hard max 6 on `--competitors`. vs-mode users opted into N entities explicitly. |
+| Rivalry content ("A vs B" articles) missed in N-independent passes. | Deferred to separate task (head-to-head supplemental pass). Start shipping and observe whether this is actually a gap. |
+| Hosting model skips `--competitors-plan` and uses `--competitors-list` only. | Unit 4 stderr reframe steers explicitly. SKILL.md Unit 6 makes the plan-path canonical. Thin Resolved block in output makes skipped-Step-0.55 visible. |
+| Override-leak fix misses a subtle closure path. | Unit 2 is test-first with the Kanye receipt as the failing input. Regression test asserts every per-entity flag is None unless plan provides it. |
+
+## Documentation / Operational Notes
+
+- Beta channel first per CLAUDE.md.
+- After merge: hot-copy to `~/.claude/plugins/cache/last30days-skill/last30days/3.0.13/`.
+- CHANGELOG explicitly frames the vs-mode change as an architectural revert-with-parallelism, not a regression to the old serial N-pass.
+
+## Sources & References
+
+- Superseded plan: `docs/plans/2026-04-22-004-fix-competitors-hosting-model-resolve-and-leak-plan.md.superseded`
+- Previous plan (3.0.12): `docs/plans/2026-04-22-003-fix-competitors-per-entity-resolution-plan.md`
+- Initial plan (3.0.11): `docs/plans/2026-04-22-002-feat-competitors-flag-comparison-fanout-plan.md`
+- 2026-04-22 test session receipts (Warriors, Seattle, Arizona Wildcats, Kanye West)
+- SKILL.md §551 + §679 — the per-entity Step 0.55 protocol the hosting model uses for both paths
+- Related code: `scripts/lib/fanout.py`, `scripts/last30days.py` `_competitor_runner`, `scripts/lib/planner.py` vs-topic special-case, `scripts/lib/render.py` `_render_resolved_entities_block`, `scripts/lib/polymarket.py`, `scripts/lib/quality_nudge.py`
+- Related PRs: #308 (3.0.11), #309 (3.0.12)

docs/plans/2026-04-22-006-fix-comparison-title-skill-attribution-plan.md

@@ -0,0 +1,87 @@
+---
+title: "fix: comparison title says (/Last30Days) instead of (Last 30 Days)"
+type: fix
+status: active
+date: 2026-04-22
+---
+
+# fix: comparison title says (/Last30Days) instead of (Last 30 Days)
+
+## Overview
+
+User feedback 2026-04-22 on the 3.0.13 release runs (Kanye vs Drake, Mercer Island, Figma): the comparison title currently reads `# Kanye West vs Drake: What the Community Says (Last 30 Days)`. It should read `# Kanye West vs Drake: What the Community Says (/Last30Days)` — attributing the output to the slash command rather than describing the date range generically.
+
+Single-line change in SKILL.md, three occurrences. No code change.
+
+## Requirements Trace
+
+- R1. Comparison title pattern in SKILL.md changes from `(Last 30 Days)` to `(/Last30Days)` so synthesis outputs read `... What the Community Says (/Last30Days)`.
+- R2. Both the rule statement (line 113) and the COMPARISON-exception statement (line 131) and the synthesis template example (line 1208) all use the new suffix.
+- R3. Version bumps to 3.0.14, CHANGELOG entry, sync, hot-copy. Public cache picks up the new title pattern.
+
+## Scope Boundaries
+
+- No changes to the single-entity output title (no `(/Last30Days)` suffix there — only comparison topics carry it).
+- No changes to engine code. Pure SKILL.md content.
+- No changes to anything else surfaced in the test runs.
+
+## Key Technical Decisions
+
+- **Replace all three occurrences of the suffix string in one pass.** They are identical strings; changing one without the others would cause synthesis-time confusion when the model reaches a different reference.
+- **Ship as 3.0.14, not 3.0.13.x.** Patch-level bump matches the small scope and keeps the release log clean.
+
+## Implementation Units
+
+- [ ] **Unit 1: Replace `(Last 30 Days)` → `(/Last30Days)` in SKILL.md**
+
+**Goal:** All three SKILL.md references to the comparison title use the new suffix.
+
+**Requirements:** R1, R2
+
+**Files:**
+- Modify: `SKILL.md`
+
+**Approach:**
+- `replace_all` swap of `What the Community Says (Last 30 Days)` → `What the Community Says (/Last30Days)`. Three occurrences, no other strings overlap.
+
+**Test scenarios:**
+- Test expectation: none — pure documentation. Verification by inspection + dogfood run.
+
+**Verification:**
+- `grep -c "What the Community Says (/Last30Days)" SKILL.md` returns 3.
+- `grep -c "What the Community Says (Last 30 Days)" SKILL.md` returns 0.
+
+- [ ] **Unit 2: Version 3.0.14 + CHANGELOG + sync + hot-copy**
+
+**Goal:** Ship 3.0.14 to all local targets.
+
+**Requirements:** R3
+
+**Dependencies:** Unit 1
+
+**Files:**
+- Modify: `.claude-plugin/plugin.json`
+- Modify: `CHANGELOG.md`
+- Run: `bash scripts/sync.sh`
+- Hot-copy: `~/.claude/plugins/cache/last30days-skill/last30days/3.0.14/`
+
+**Approach:**
+- CHANGELOG: "Changed: comparison-mode title attribution — `What the Community Says (Last 30 Days)` → `What the Community Says (/Last30Days)`. Surfaces the slash-command identity instead of restating the date range."
+
+**Test scenarios:**
+- Test expectation: none — packaging.
+
+**Verification:**
+- `grep version .claude-plugin/plugin.json` → 3.0.14.
+- Hot-copy contains the updated SKILL.md.
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Hosting model has the old title pattern memorized from a prior run and re-emits `(Last 30 Days)`. | SKILL.md is read top-to-bottom each invocation. STEP 0 canonical-path self-check (3.0.12) ensures the model loads the new SKILL.md, not the marketplace stale copy. |
+
+## Sources & References
+
+- 2026-04-22 dogfood runs (Kanye West vs Drake, Mercer Island --competitors, Figma --competitors)
+- Related code: `SKILL.md` lines 113, 131, 1208

docs/plans/2026-04-22-007-marketing-v3-1-launch-video-plan.md

@@ -0,0 +1,396 @@
+---
+title: "marketing: v3.1 launch video — 30s Remotion piece for X"
+type: feat
+status: active
+date: 2026-04-22
+---
+
+# marketing: v3.1 launch video — 30s Remotion piece for X
+
+## Overview
+
+Build a 30-second Remotion-rendered MP4 marketing video announcing **v3.1: Competitors mode** for `/last30days`. Posts to X. Frames the new vs-mode-with-auto-discovery as the headline — three full passes, three save files, one comparison — without burying it in CLI minutiae.
+
+Marketing release tag is **v3.1** (rebrands the 3.0.11-3.0.14 bundle into one shippable narrative for the launch tweet). Engine version stays 3.0.14 — `3.1` is the marketing version, not a code version bump.
+
+## Script (60 frames per second × 30 seconds = 900 frames; this version assumes 30fps × 30s = 900 frames at 30fps)
+
+Total runtime: 30.0 seconds @ 30fps = 900 frames. Six scenes, on-screen text only (silent autoplay-friendly).
+
+### Scene 1 — Hook (0:00 – 0:03 | frames 0-90)
+
+**Visual:** Black background. The `/last30days` badge animates in (the literal `🌐 last30days v3.1 · synced 2026-04-22` line) with the spring scale-in used in slick devtool intros.
+
+**Caption (overlay, large):**
+```
+What if one search
+ran 3 at once?
+```
+
+### Scene 2 — Set the world (0:03 – 0:08 | frames 90-240)
+
+**Visual:** Single mac terminal window center-stage. Type-on animation:
+```
+$ /last30days OpenAI
+```
+Below it, a simple result card stub appears (Reddit upvote count + X likes), then static.
+
+**Caption (small bottom-left):**
+```
+The old way: one topic.
+```
+
+### Scene 3 — The reveal (0:08 – 0:14 | frames 240-420)
+
+**Visual:** The terminal types one more flag:
+```
+$ /last30days OpenAI --competitors
+```
+Hard cut → the single terminal **splits into 3 panes** side by side. Each pane shows a different topic header animating in, in this order:
+- Left: `OpenAI`
+- Middle: `vs Anthropic`
+- Right: `vs xAI`
+
+Pane content scrolls fake "search progress" lines (Reddit, X, YouTube indicators) in parallel, like a live fan-out.
+
+**Caption (top center):**
+```
+Now it discovers competitors
+and runs all 3.
+```
+
+### Scene 4 — Result reveal (0:14 – 0:21 | frames 420-630)
+
+**Visual:** The 3 panes collapse into a single comparison surface — the `## Head-to-Head` table from the actual engine output, with rows fading in one by one (What it is, Streams, Best for, Trajectory). Each entity column lights up as its row populates.
+
+**Caption (bottom):**
+```
+3 full passes. 3 save files.
+1 comparison.
+```
+
+### Scene 5 — How it's special (0:21 – 0:26 | frames 630-780)
+
+**Visual:** Cut to a clean text card, large mono font:
+```
+You pick the topic.
+The agent picks the peers.
+The engine fans out.
+```
+Each line fades in 1.5s apart.
+
+### Scene 6 — CTA (0:26 – 0:30 | frames 780-900)
+
+**Visual:** Black background. Centered:
+- Top: `🌐 last30days v3.1`
+- Middle: `/last30days {topic} --competitors`
+- Bottom: `github.com/mvanhorn/last30days-skill`
+
+Subtle pulse on the install line.
+
+**End frame holds for ~0.5s.**
+
+## Problem Frame
+
+The 3.0.11-3.0.14 release bundle ships a transformative feature (per-entity vs-mode fanout + `--competitors` shortcut + per-entity save files), but the value lands flat in a tweet thread or screenshot. A 30s video does what static text cannot: shows the fan-out happening in real time and the 3 → 1 collapse into a comparison. Higher tweet engagement, easier to RT/QT.
+
+## Requirements Trace
+
+- R1. Final artifact: a single MP4 file, 1920×1080, 30fps, ~30 seconds (±0.5s), under 30MB, suitable for direct X upload.
+- R2. Six-scene script as defined above, with text/visuals/timing matching to within 5 frames.
+- R3. Branded look: matches the `🌐 last30days v3.1` badge style (terminal aesthetic, mono font, dark background).
+- R4. Silent — no voiceover, no music in v1. Captions baked in. Designed for autoplay-muted feeds.
+- R5. Reproducible: another contributor (or a future-me) can re-render with one command. Project lives in-repo so the source is versioned.
+
+## Scope Boundaries
+
+- No voiceover. Text-on-screen only. (Voice can be a v1.1 if engagement is high.)
+- No background music in the rendered MP4. (Music can be added in post via QuickTime/iMovie if desired before posting.)
+- No localization. English captions only.
+- No A/B test variants. One video.
+- No 9:16 vertical version. 16:9 only. (Vertical can be a separate render after launch validates the format.)
+
+### Deferred to Separate Tasks
+
+- Voiceover variant: defer to follow-up if v1 lands well.
+- 9:16 mobile cut: defer; same source compositions can re-render at 1080×1920 in a follow-up.
+- Animated GIF for embedding in README.md: defer; can be ffmpeg-extracted from the MP4.
+
+## Context & Research
+
+### Relevant Code and Patterns
+
+- `SKILL.md` — the comparison render scaffold (`## Head-to-Head` table) is the visual reference for Scene 4's table look.
+- `scripts/lib/render.py` `_render_comparison_scaffold` — emits the 9-axis table whose visual style we're recreating in a more polished form.
+- `CHANGELOG.md` 3.0.11-3.0.14 entries — the prose source for the script's beats.
+- `.claude-plugin/plugin.json` version 3.0.14 — current code version (marketing version is 3.1 for the launch).
+
+### External References
+
+- Remotion 4.x docs (https://www.remotion.dev/docs/) — current API for compositions, sequences, springs, and render CLI.
+- X video specs 2026: max 2 min 20 s, ≤512MB, MP4 with H.264 + AAC, recommended 1920×1080 for landscape autoplay.
+
+### Institutional Learnings
+
+- No prior `marketing/` dir or video plans in `docs/plans/`. This is a greenfield asset directory.
+
+## Key Technical Decisions
+
+- **Remotion, not ffmpeg-only.** Remotion's React-based compositions handle the typed-on terminal effect, spring-animated badges, and scene transitions far more cleanly than raw ffmpeg filtergraphs. Render output is still MP4 via Remotion's bundled ffmpeg.
+- **In-repo asset directory at `marketing/v3.1-launch/`.** Lives with the product so future versions can fork the project. Adds `marketing/` to `.gitignore` exceptions only for source files; rendered MP4 stays out of git (uploaded separately).
+- **16:9 1920×1080 @ 30fps.** Best fit for X landscape autoplay on desktop and mobile feed. 30fps is plenty for typed-text + UI animation; 60fps doubles render time without obvious quality gain.
+- **Silent + captions.** X autoplay defaults to muted. Sound-off is the realistic viewing condition. Captions baked into the visual.
+- **Six scenes, one composition.** Single Remotion composition with sequenced child compositions per scene. Easier to re-time than scene-files. Frame-numbered timing in the script enables precise edits.
+- **Mono font (JetBrains Mono or Geist Mono).** Matches the terminal aesthetic of the actual `/last30days` output. Available via Google Fonts or @remotion/google-fonts.
+- **Marketing version 3.1 ≠ engine version 3.0.14.** `3.1` is the launch label. Engine stays 3.0.14. Avoids confusion in CHANGELOG.
+
+## Open Questions
+
+### Resolved During Planning
+
+- **Aspect ratio?** 16:9 1080p. Best X autoplay format; vertical can be a follow-up.
+- **Voiceover or silent?** Silent + on-screen captions. Autoplay-muted is the realistic condition.
+- **In-repo or separate repo?** In-repo at `marketing/v3.1-launch/`. Rendered MP4 not committed; source compositions are.
+- **Length?** Exactly 30s (900 frames @ 30fps). No flex.
+- **Marketing version label?** `v3.1`. Engine code version stays 3.0.14.
+
+### Deferred to Implementation
+
+- Exact animation easing curves per scene — pick during build via Remotion preview iteration.
+- Whether the comparison-table mock in Scene 4 uses canned text or pulls from the actual saved `*-raw.md` files. Probably canned for visual control.
+- Whether Scene 3's "search progress" lines are typed individually or use a marquee scroll. Pick during preview.
+- Exact accent color palette beyond "terminal dark." Iterate against preview.
+
+## Output Structure
+
+    marketing/
+      v3.1-launch/
+        package.json                  # Remotion dependency manifest
+        tsconfig.json                 # TypeScript config
+        remotion.config.ts            # Remotion render config (codec, fps, resolution)
+        src/
+          index.ts                    # Remotion entry — registers compositions
+          Root.tsx                    # Root composition definition
+          LaunchVideo.tsx             # Main 30s composition that sequences scenes
+          scenes/
+            Scene1Hook.tsx
+            Scene2OldWay.tsx
+            Scene3FanOut.tsx
+            Scene4Comparison.tsx
+            Scene5HowItWorks.tsx
+            Scene6CTA.tsx
+          components/
+            TerminalWindow.tsx        # Reusable mac-style terminal frame
+            TypedLine.tsx             # Type-on animation primitive
+            ComparisonTable.tsx       # The Head-to-Head table mock
+            BadgeBar.tsx              # The 🌐 last30days v3.1 badge
+          lib/
+            timing.ts                 # Frame ranges per scene (single source of truth)
+            colors.ts                 # Brand palette
+        public/                       # Static assets (logo, fonts if local)
+        out/                          # Rendered MP4 lives here (gitignored)
+        README.md                     # How to preview/render
+
+## High-Level Technical Design
+
+> *Directional guidance for review — not implementation specification.*
+
+```
+LaunchVideo (durationInFrames = 900)
+├── <Sequence from={0} durationInFrames={90}>     <Scene1Hook />
+├── <Sequence from={90} durationInFrames={150}>   <Scene2OldWay />
+├── <Sequence from={240} durationInFrames={180}>  <Scene3FanOut />
+├── <Sequence from={420} durationInFrames={210}>  <Scene4Comparison />
+├── <Sequence from={630} durationInFrames={150}>  <Scene5HowItWorks />
+└── <Sequence from={780} durationInFrames={120}>  <Scene6CTA />
+```
+
+Per-scene components use `useCurrentFrame()` + `interpolate()` + `spring()` for timing. `TerminalWindow` is the dominant motif across scenes 2-4.
+
+## Implementation Units
+
+- [ ] **Unit 1: Remotion project scaffold**
+
+**Goal:** Spin up a working Remotion project at `marketing/v3.1-launch/` that previews a blank composition and renders to MP4.
+
+**Requirements:** R1, R5
+
+**Files:**
+- Create: `marketing/v3.1-launch/package.json`
+- Create: `marketing/v3.1-launch/tsconfig.json`
+- Create: `marketing/v3.1-launch/remotion.config.ts`
+- Create: `marketing/v3.1-launch/src/index.ts`
+- Create: `marketing/v3.1-launch/src/Root.tsx`
+- Create: `marketing/v3.1-launch/README.md`
+- Modify: `.gitignore` (add `marketing/v3.1-launch/out/`, `marketing/v3.1-launch/node_modules/`)
+
+**Approach:**
+- Use `npx create-video@latest --blank` (Remotion 4.x scaffolding) targeting `marketing/v3.1-launch/`. Strip the demo composition.
+- Configure: 1920×1080, 30fps, H.264, AAC (audio codec needed even for silent — empty track).
+- README documents `npm install`, `npm run preview` (Remotion Studio), `npm run render` (one-command MP4).
+
+**Test scenarios:**
+- Test expectation: none for scaffold, but verification = `npm run preview` opens Remotion Studio with a blank 30s composition; `npm run render` produces a black MP4 at the right resolution.
+
+**Verification:**
+- Studio loads at localhost:3000 with the empty `LaunchVideo` composition listed.
+- A render produces `out/launch-video.mp4` at 1920×1080, 30s, valid MP4.
+
+- [ ] **Unit 2: Reusable components (Terminal, TypedLine, BadgeBar)**
+
+**Goal:** Build the three primitive components scenes 2-6 will compose. Each is independently previewable.
+
+**Requirements:** R3, R5
+
+**Dependencies:** Unit 1
+
+**Files:**
+- Create: `marketing/v3.1-launch/src/components/TerminalWindow.tsx`
+- Create: `marketing/v3.1-launch/src/components/TypedLine.tsx`
+- Create: `marketing/v3.1-launch/src/components/BadgeBar.tsx`
+- Create: `marketing/v3.1-launch/src/lib/colors.ts`
+- Create: `marketing/v3.1-launch/src/lib/timing.ts`
+
+**Approach:**
+- `TerminalWindow`: mac-style traffic-light header, dark gradient background, mono content area. Accepts children.
+- `TypedLine`: takes a string and a `startFrame`, renders character-by-character at ~30 chars/sec. Reuses Remotion's `interpolate(useCurrentFrame() - startFrame, [0, lengthFrames], [0, text.length])` clamped.
+- `BadgeBar`: renders the literal `🌐 last30days v3.1 · synced 2026-04-22` line in mono with the same gradient color treatment as the engine's compact emit.
+- `colors.ts`: 5-7 brand colors (terminal-bg, terminal-fg, accent-cyan, accent-magenta, muted, success-green, warning-amber).
+- `timing.ts`: exports the scene frame ranges as named constants. Single source of truth for any retiming.
+
+**Test scenarios:**
+- Test expectation: none — visual components verified in Remotion Studio.
+
+**Verification:**
+- Each component renders standalone in Studio when wrapped in a temporary preview composition.
+- TypedLine animates character-by-character without flicker.
+
+- [ ] **Unit 3: Scenes 1-3 (Hook, Old way, Fan-out reveal)**
+
+**Goal:** Build the first half of the video (frames 0-420). The narrative arc up through the visual fan-out.
+
+**Requirements:** R2
+
+**Dependencies:** Unit 2
+
+**Files:**
+- Create: `marketing/v3.1-launch/src/scenes/Scene1Hook.tsx`
+- Create: `marketing/v3.1-launch/src/scenes/Scene2OldWay.tsx`
+- Create: `marketing/v3.1-launch/src/scenes/Scene3FanOut.tsx`
+- Modify: `marketing/v3.1-launch/src/Root.tsx` (register sequences)
+
+**Approach:**
+- Scene 1: spring-in BadgeBar, large overlay caption, 3-second hold.
+- Scene 2: TerminalWindow with TypedLine (`$ /last30days OpenAI`), then a single result-card mock fading in.
+- Scene 3: typing animation appends `--competitors`, hard cut, three TerminalWindow components arranged in a row with staggered fade-in. Each pane shows a different entity header + scrolling progress lines.
+
+**Test scenarios:**
+- Test expectation: none — verified visually in Studio.
+
+**Verification:**
+- Scrub the 0-14s range in Studio; visuals match the script timing within 5 frames.
+- The fan-out moment (frame 240) lands cleanly; no jank in the transition from 1 → 3 panes.
+
+- [ ] **Unit 4: Scenes 4-6 (Comparison reveal, How it works, CTA)**
+
+**Goal:** Build the back half of the video (frames 420-900). Resolution + payoff + call to action.
+
+**Requirements:** R2
+
+**Dependencies:** Unit 2
+
+**Files:**
+- Create: `marketing/v3.1-launch/src/scenes/Scene4Comparison.tsx`
+- Create: `marketing/v3.1-launch/src/scenes/Scene5HowItWorks.tsx`
+- Create: `marketing/v3.1-launch/src/scenes/Scene6CTA.tsx`
+- Create: `marketing/v3.1-launch/src/components/ComparisonTable.tsx`
+
+**Approach:**
+- Scene 4: ComparisonTable component renders 3-column markdown-style table; rows fade in one by one (stagger 15-20 frames). Uses canned data — OpenAI / Anthropic / xAI with believable cell content drawn from real 3.0.13 outputs.
+- Scene 5: three-line text card; lines fade in 45 frames apart.
+- Scene 6: three centered text blocks; install line gets a 1Hz subtle opacity pulse for emphasis.
+
+**Test scenarios:**
+- Test expectation: none — verified visually.
+
+**Verification:**
+- Scrub 14-30s; table reveal feels paced (not too slow, not strobed); CTA holds long enough to read (~3-4s).
+- ComparisonTable cells are legible at 1920×1080 (mono font ≥ 28px).
+
+- [ ] **Unit 5: Final composition wiring + render**
+
+**Goal:** Wire the six scenes into the master `LaunchVideo` composition, render to MP4, verify against X upload constraints.
+
+**Requirements:** R1, R2, R5
+
+**Dependencies:** Units 3, 4
+
+**Files:**
+- Modify: `marketing/v3.1-launch/src/LaunchVideo.tsx` (sequence all 6 scenes)
+- Modify: `marketing/v3.1-launch/README.md` (add render command + verification checklist)
+
+**Approach:**
+- `LaunchVideo` is a single Composition that imports `Scene1Hook` … `Scene6CTA` and wraps each in `<Sequence from=… durationInFrames=…>` matching `lib/timing.ts`.
+- Run `npx remotion render LaunchVideo out/last30days-v3.1-launch.mp4 --codec=h264 --crf=18`.
+- Verify output: 30.0s ±0.1s, 1920×1080, file size <30MB, opens in QuickTime, plays without dropped frames.
+
+**Test scenarios:**
+- Test expectation: none — verification is the rendered MP4 itself.
+
+**Verification:**
+- `ffprobe out/last30days-v3.1-launch.mp4` reports 1920×1080, 30fps, ~30.0s, h264, faststart-friendly.
+- Manual play-through end-to-end in QuickTime feels coherent and on-pace.
+- File <30MB so X upload is instant.
+
+- [ ] **Unit 6: Polish pass + ship**
+
+**Goal:** Watch the full render, fix obvious jank, do a second render, and stage for X posting.
+
+**Requirements:** R1, R2, R3
+
+**Dependencies:** Unit 5
+
+**Files:**
+- Possibly modify: any scene file based on watch-through findings.
+
+**Approach:**
+- Watch the rendered MP4 at full size. Note: timing felt off, transitions too fast, captions overflow, color clash, anything visibly broken.
+- Iterate: edit scene component → re-preview in Studio → re-render full MP4.
+- Cap at 2 polish passes; ship the better of the two renders.
+- Final MP4 sits at `marketing/v3.1-launch/out/last30days-v3.1-launch.mp4` ready for X upload.
+
+**Test scenarios:**
+- Test expectation: none — pure subjective polish.
+
+**Verification:**
+- User watches the final render and approves.
+- No glaring visual bugs (overflowing text, frozen frames, color clashes).
+
+## System-Wide Impact
+
+- **Interaction graph:** None — this is a standalone marketing artifact. Doesn't touch the Python engine, doesn't change any user-facing behavior.
+- **State lifecycle risks:** None.
+- **API surface parity:** N/A.
+- **Unchanged invariants:** The shipped 3.0.14 engine is untouched.
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Remotion install pulls 200MB+ of node_modules. | `marketing/v3.1-launch/node_modules/` in `.gitignore`; checked-in source stays small. |
+| Render time blows past patience (>5 min for 30s @ 1080p30). | Bun-based render or `--concurrency` flag. Default Remotion is fast enough on M-series Macs. If slow, lower preview to 720p, render final at 1080p. |
+| Captions overflow the 1920px width on certain fonts. | Use a known mono font with a measured per-character width; cap caption lines at 36 chars. |
+| The "fan-out" visual in Scene 3 looks confusing instead of magical. | Polish pass (Unit 6) is the safety net; if still bad, fall back to a simpler "1 → 3 panes wipe" instead of typed split. |
+| File size >30MB hits X upload friction. | Use `--crf=18` (high quality, reasonable size); fall back to `--crf=23` if over. 30s @ 1080p30 H.264 is normally 5-15MB. |
+
+## Documentation / Operational Notes
+
+- README at `marketing/v3.1-launch/README.md` documents preview / render commands.
+- After render, the MP4 is uploaded directly to X. Tweet copy is the user's call (this plan stops at the rendered file).
+
+## Sources & References
+
+- Related code: `scripts/lib/render.py` (`_render_comparison_scaffold` is the visual model for Scene 4); `SKILL.md` Competitor mode section (the narrative source).
+- Related PRs: #308, #311, #312 (the 3.0.11 → 3.0.14 release bundle this video markets as "v3.1").
+- External docs: https://www.remotion.dev/docs/ (Remotion 4.x API).
+- X video specs: https://help.x.com/en/using-x/twitter-videos.

docs/pr-credits.md

@@ -0,0 +1,33 @@
+# PR Credits — Thank After V2 Goes Live
+
+When V2 is pushed to the public repo, comment on each PR to thank the contributor and let them know their work was integrated.
+
+## Integrated (cherry-picked into V2)
+
+| PR | Author | What | Status |
+|---|---|---|---|
+| [#17](https://github.com/mvanhorn/last30days-skill/pull/17) | **@JosephOIbrahim** | Windows Unicode fix (cp1252 emoji crash) | Merge or close with thanks |
+| [#16](https://github.com/mvanhorn/last30days-skill/pull/16) | **@levineam** | Handle 403 model access errors + gpt-4.1 fallback | Merge or close with thanks |
+| [#18](https://github.com/mvanhorn/last30days-skill/pull/18) | **@jonthebeef** | `--days=N` configurable lookback flag | Merge or close with thanks |
+| [#1](https://github.com/mvanhorn/last30days-skill/pull/1) | **@galligan** (Matt Galligan) | Marketplace plugin conversion — we took a lighter approach inspired by his PR | Close with thanks, explain lighter approach |
+
+## Already Fixed in V2 (close with thanks)
+
+| PR | Author | What |
+|---|---|---|
+| [#15](https://github.com/mvanhorn/last30days-skill/pull/15) | **@rszrszrsz** | YAML argument-hint fix — already fixed in V2 |
+| [#11](https://github.com/mvanhorn/last30days-skill/pull/11) | **@nerveband** | Same YAML fix (earlier) — already fixed in V2 |
+
+## Not Integrated (close with explanation)
+
+| PR | Author | What | Why |
+|---|---|---|---|
+| [#5](https://github.com/mvanhorn/last30days-skill/pull/5) | **@jblwilliams** | Codex auth with OpenAI Responses API | Good idea, too complex for now (358 lines SSE/JWT). May revisit. |
+| [#14](https://github.com/mvanhorn/last30days-skill/pull/14) | **@thangman1** | WebSearch-first, API keys optional | Philosophical shift — V2 already does WebSearch in parallel |
+| [#10](https://github.com/mvanhorn/last30days-skill/pull/10) | **@thetechreviewer** | OpenRouter API integration | Too large (1029 lines), adds MCP server |
+
+## Suggested Comment Template
+
+> Thanks for this PR! We integrated your [fix/feature] into V2 (commit XXXXX). Really appreciate the contribution. 🙏
+>
+> Closing this PR since the changes are now in main via a different commit, but full credit to you for the idea and implementation.

docs/releases/v3.0.9.md

@@ -0,0 +1,112 @@
+# v3.0.9 - The Self-Debug Release
+
+## Highlights
+
+**v3.0.9 is live.** New user-facing capabilities, broader cross-platform support, and a skill that now runs reliably on Claude Code, Codex, Hermes, Gemini, claude.ai, and OpenClaw. The headline fix: the engine refuses "birthday gift for 40 year old" style queries with a clarifying question instead of 5 minutes of junk output. The headline feature: TikTok and YouTube top comments now render alongside Reddit's, so the most-engaged voice from every source makes it into the synthesis.
+
+**The label - "The Self-Debug Release":** I handed 5 separate Opus 4.7 instances their own failed outputs and asked them to debug themselves. Three converged on "SKILL.md is too big and the LAWs are too deep." Two converged on "the engine should refuse demographic-shopping queries outright" and "the WebSearch Sources reminder is overriding LAW 1." I copy-pasted their diagnoses into code. Validation: 5/5 canonical compliance on the topics that had failed.
+
+## New capabilities
+
+- **TikTok and YouTube top comments render alongside Reddit's.** PR [#260](https://github.com/mvanhorn/last30days-skill/pull/260) made the top-engagement comment from each TikTok video and YouTube video first-class in the output - same prominent `💬 Top comment` treatment Reddit's top comment already got. This is the biggest user-facing output change since 3.0.0 and it was never announced. The community inspiration trace: @uppinote20's original push for richer Reddit comments ([PR #143](https://github.com/mvanhorn/last30days-skill/pull/143)) seeded the pattern; this PR generalized it across TikTok and YouTube. PR [#265](https://github.com/mvanhorn/last30days-skill/pull/265) followed up by fixing the ScrapeCreators `url=` param + new response shape for YouTube comments/transcripts so the enrichment actually works.
+
+- **last30days runs on Hermes AI Agent now.** @stephenmcconnachie's PR ([#228](https://github.com/mvanhorn/last30days-skill/pull/228)) added Hermes as a first-class deploy target. `scripts/sync.sh` detects `~/.hermes/skills/research` and deploys the full skill (SKILL.md, scripts, lib modules, fixtures) to Hermes's skills directory alongside Claude Code and Codex. This is one of the biggest surface-area expansions in v3 - last30days is now usable inside the Hermes agent's research workflows without any manual wiring.
+
+- **Multi-key SCRAPECREATORS_API_KEY rotation.** @zaydiscold's PR ([#268](https://github.com/mvanhorn/last30days-skill/pull/268)) added automatic key rotation. Set `SCRAPECREATORS_API_KEY_1`, `SCRAPECREATORS_API_KEY_2`, etc. and the engine rotates when a key hits rate limits instead of failing the whole run. For power users running daily queries, this is the difference between rate-limit 429s and zero-touch reliability.
+
+- **The skill works on Windows now.** @Chelebii's PR ([#227](https://github.com/mvanhorn/last30days-skill/pull/227)) stabilized the vendored Bird X search client on Windows. Previously the bundled X backend had subtle runtime issues on Windows terminals; now it runs clean. Pair this with @Gujiassh's UTF-8 encoding fix ([#225](https://github.com/mvanhorn/last30days-skill/pull/225)) for saved output and Windows users get the full v3 experience without workarounds.
+
+- **Linux permission checks stopped false-warning.** @george231224's PR ([#216](https://github.com/mvanhorn/last30days-skill/pull/216)) fixed `check_perms` on Linux by preferring GNU stat's syntax over the BSD stat that the skill was calling. Linux users were getting spurious permission warnings on `.env` files that were already correctly 600-chmod'd. Now the check matches reality.
+
+- **Gemini CLI got a first-class install path.** @hnshah's docs PR ([#224](https://github.com/mvanhorn/last30days-skill/pull/224)) added the Gemini CLI install note and workaround for a rough edge in the Gemini skill loader. Gemini users now have a one-paragraph install flow in the README instead of having to reverse-engineer the plugin layout.
+
+- **Offline quality evaluation.** @j-sperling's PR ([#233](https://github.com/mvanhorn/last30days-skill/pull/233)) added `eval_topics.json` as a fixture. Contributors and I can now run quality-regression checks on synthesis output without burning live API credits. This is the scaffolding that made the plan 015 validation gate affordable - without eval fixtures, testing 5/5 canonical compliance on every release would cost real money every time. Ships as contributor infrastructure but shows up as stability for end users.
+
+- **Reddit client got a cleaner HTTP layer.** @iliaal shipped three architecture PRs back-to-back ([#207](https://github.com/mvanhorn/last30days-skill/pull/207), [#208](https://github.com/mvanhorn/last30days-skill/pull/208), [#209](https://github.com/mvanhorn/last30days-skill/pull/209)) that consolidated Reddit's HTTP handling into `http.get(params=...)`, rejected garbage input in `_parse_date`, and unified `_sc_headers` into `http.scrapecreators_headers`. End-user benefit: fewer flaky timeouts, fewer "weird parse error" crashes, a codebase that's easier for future contributors to touch without breaking Reddit. These aren't sexy PRs; they're the kind of refactor that prevents six future bug reports.
+
+- **The `--days=N` flag keeps working.** @BryanTegomoh's PR ([#230](https://github.com/mvanhorn/last30days-skill/pull/230)) restored backcompat for the legacy `--days` alias so anyone who'd scripted against it in 2.x doesn't break on v3. Small PR, meaningful reliability gain for existing users.
+
+- **INCLUDE_SOURCES has a sane default.** @hnshah's PR ([#223](https://github.com/mvanhorn/last30days-skill/pull/223)) defaulted the env var to empty string instead of unset. Missing env no longer breaks source inclusion on fresh installs.
+
+- **Version metadata stays in sync.** @Gujiassh's PR ([#217](https://github.com/mvanhorn/last30days-skill/pull/217)) aligned the SKILL.md version header with the sync target version, and @shalomma's PR ([#229](https://github.com/mvanhorn/last30days-skill/pull/229)) closed the remaining drift between the SKILL.md header and plugin.json. "Which version am I actually on" is no longer an adventure.
+
+- **Bird X engagement handling got hardened.** @j-sperling's PR ([#234](https://github.com/mvanhorn/last30days-skill/pull/234)) made `bird_x` skip all-None engagement dicts instead of crashing on them. Rare condition, but the kind of thing that silently kills a run on a specific topic.
+
+- **Dev workflow hygiene.** @j-sperling's gitignore PR ([#232](https://github.com/mvanhorn/last30days-skill/pull/232)) dropped `.venv`, `.coverage`, `htmlcov`, and `.memsearch` from the tracked tree. Contributor quality-of-life; keeps PR diffs clean.
+
+- **The skill installs to claude.ai.** PRs [#242](https://github.com/mvanhorn/last30days-skill/pull/242) and [#244](https://github.com/mvanhorn/last30days-skill/pull/244) shipped `scripts/build-skill.sh` plus the `.gitattributes` + `export-ignore` plumbing that packages last30days into a claude.ai-upload-ready `.skill` file under the 200-file cap. The skill is no longer Claude-Code-only - it installs directly on claude.ai, too. README has the upload workflow.
+
+- **OpenAI Codex CLI discovers the skill natively.** PR [#219](https://github.com/mvanhorn/last30days-skill/pull/219) added `.agents/skills/last30days/SKILL.md` as a real file (not symlinked - Codex's loader skips symlinks) plus `.codex-plugin/plugin.json` as the namespace marker. The skill now shows up as `last30days:last30days` when Codex runs in a checkout. Inspired by @Jah-yee ([#153](https://github.com/mvanhorn/last30days-skill/pull/153)) and @dannyshmueli on X.
+
+- **`/last30days` as a slash command.** PR [#267](https://github.com/mvanhorn/last30days-skill/pull/267) added `commands/last30days.md` so plugin users can type `/last30days <topic>` and Claude Code autocomplete prefix-matches it to the canonical `/last30days:last30days` form. No more typing the double-namespace.
+
+## The self-debug technique, for anyone rebuilding this elsewhere
+
+The breakthrough wasn't the individual fixes. It was the realization that instead of guessing why the model was ignoring the rules, I should ask the model. Five separate Opus 4.7 sessions debugged their own outputs:
+
+- "Did you read SKILL.md?" → "I tried Read, hit the 25K token cap, and bailed instead of chunked-reading."
+- "Why the trailing Sources block?" → "The WebSearch tool's own reminder said MANDATORY. Precedence was unclear."
+- "Why the section headers?" → "I had strong priors on Peter Steinberger and wrote my thesis instead of passing through."
+- "Why the wrong file?" → "I read `.agents/skills/last30days/SKILL.md` first because it appeared in the path glob."
+
+Three of the five said "move the LAWs to the top." Two said "make the engine enforce it so the model can't not comply." I shipped both. That's the whole technique: when the LLM-under-orchestration keeps breaking the contract, don't argue with it - ask it to debug itself, and build structural enforcement around whatever it names as the root cause.
+
+## Thank you
+
+**Community PR authors since v3.0.0:**
+- @j-sperling - v3 engine architecture, eval fixtures, gitignore hygiene, Bird X hardening ([#232](https://github.com/mvanhorn/last30days-skill/pull/232), [#233](https://github.com/mvanhorn/last30days-skill/pull/233), [#234](https://github.com/mvanhorn/last30days-skill/pull/234))
+- @stephenmcconnachie - Hermes AI Agent support ([#228](https://github.com/mvanhorn/last30days-skill/pull/228))
+- @zaydiscold - Multi-key SCRAPECREATORS rotation ([#268](https://github.com/mvanhorn/last30days-skill/pull/268))
+- @iliaal - Reddit HTTP helper + GitHub date parsing + ScrapeCreators header consolidation ([#207](https://github.com/mvanhorn/last30days-skill/pull/207), [#208](https://github.com/mvanhorn/last30days-skill/pull/208), [#209](https://github.com/mvanhorn/last30days-skill/pull/209))
+- @Chelebii - Windows Bird X stability ([#227](https://github.com/mvanhorn/last30days-skill/pull/227))
+- @george231224 - Linux check_perms stat ([#216](https://github.com/mvanhorn/last30days-skill/pull/216))
+- @Gujiassh - UTF-8 saved output + version metadata alignment ([#217](https://github.com/mvanhorn/last30days-skill/pull/217), [#225](https://github.com/mvanhorn/last30days-skill/pull/225))
+- @hnshah - INCLUDE_SOURCES default + Gemini install docs ([#223](https://github.com/mvanhorn/last30days-skill/pull/223), [#224](https://github.com/mvanhorn/last30days-skill/pull/224))
+- @shalomma - SKILL.md v3.0.0 version header ([#229](https://github.com/mvanhorn/last30days-skill/pull/229))
+- @BryanTegomoh - --days alias backcompat ([#230](https://github.com/mvanhorn/last30days-skill/pull/230))
+
+**v3 roadmap contributors (issues and PRs that shaped the v3 feature set):**
+- @uppinote20 - rich Reddit comments ([#143](https://github.com/mvanhorn/last30days-skill/pull/143))
+- @zerone0x - GitHub as a first-class source ([#134](https://github.com/mvanhorn/last30days-skill/issues/134), [#136](https://github.com/mvanhorn/last30days-skill/pull/136))
+- @thinkun - Reddit enrichment timeout handling ([#116](https://github.com/mvanhorn/last30days-skill/pull/116))
+- @thomasmktong - pure-Python Reddit fallback ([#124](https://github.com/mvanhorn/last30days-skill/pull/124))
+- @fanispoulinakisai-boop - Reddit timeout report ([#100](https://github.com/mvanhorn/last30days-skill/issues/100))
+- @pejmanjohn - plugin directory naming ([#99](https://github.com/mvanhorn/last30days-skill/issues/99), [#78](https://github.com/mvanhorn/last30days-skill/issues/78))
+- @zl190 - HN trending merge ([#115](https://github.com/mvanhorn/last30days-skill/pull/115))
+- @hnshah - Watchlist features ([#84](https://github.com/mvanhorn/last30days-skill/pull/84), [#85](https://github.com/mvanhorn/last30days-skill/pull/85), [#86](https://github.com/mvanhorn/last30days-skill/pull/86))
+- @Jah-yee, @dannyshmueli - Codex CLI discovery
+- @Cody-Coyote - marketplace validation bug report ([#204](https://github.com/mvanhorn/last30days-skill/issues/204))
+
+**The five Opus 4.7 instances that debugged their own failures on v3.0.7 and v3.0.8 and converged on the fixes.** The convergence was the breakthrough; this release is their diagnosis in code.
+
+## Install / Update
+
+```
+/plugin marketplace add mvanhorn/last30days-skill
+/plugin install last30days@last30days-skill
+```
+
+Or if already installed:
+
+```
+/plugin update last30days
+/reload-plugins
+```
+
+## Verify
+
+```
+cat ~/.claude/plugins/cache/last30days-skill/last30days/*/.claude-plugin/plugin.json | grep version
+```
+
+Should print `"version": "3.0.9"`.
+
+## Smoke test
+
+```
+/last30days birthday gift for 40 year old
+```
+
+Should ask a clarifying question before running. If it runs the engine anyway, the cache is stale - repeat the plugin update.
+
+**Full Changelog:** https://github.com/mvanhorn/last30days-skill/compare/v3.0.5...v3.0.9

docs/search-quality-eval.md

@@ -0,0 +1,48 @@
+# Search Quality Eval
+
+`scripts/evaluate_search_quality.py` is an optional local evaluation step for retrieval quality. It is not part of the user-facing runtime and does not need to run in CI by default.
+
+What it does:
+
+- runs a baseline revision (default `origin/main`) against a candidate checkout
+- evaluates the fixed 5 reviewer topics by default
+- computes deterministic stability metrics:
+  - `Jaccard` overlap vs baseline
+  - retention vs baseline
+  - per-source counts and overlap
+- optionally calls Gemini as a judge for graded relevance labels and then computes:
+  - `Precision@5`
+  - `nDCG@5`
+  - source-coverage recall across the judged union pool
+
+Recommended usage:
+
+```bash
+uv run python scripts/evaluate_search_quality.py
+```
+
+Useful flags:
+
+```bash
+uv run python scripts/evaluate_search_quality.py \
+  --baseline-rev origin/main \
+  --candidate-rev HEAD \
+  --no-default-topics \
+  --topic "cursor IDE pricing" \
+  --per-source-limit 5
+```
+
+Gemini configuration:
+
+- preferred on this workspace: set `GOOGLE_API_KEY`
+- also accepted: `GEMINI_API_KEY` or `GOOGLE_GENAI_API_KEY`
+- optional: set `GEMINI_MODEL`
+- default model is `gemini-3-pro-preview` for the direct Gemini API
+
+Notes:
+
+- The script forces a clean env-based auth path when it shells out to `last30days.py`.
+- It passes `XAI_API_KEY`, `OPENAI_API_KEY`, and `SCRAPECREATORS_API_KEY`, but intentionally does not pass browser-cookie X auth. That keeps evaluation runs on the popup-free path.
+- It also strips `node` from the eval `PATH` and wraps `yt-dlp` with `--ignore-config`, so older revisions do not inherit local browser-cookie config either.
+- `Jaccard` and retention are regression guards, not truth metrics.
+- `Precision@5` and `nDCG@5` are only as good as the judged pool. They help compare revisions, but they are not a substitute for a larger labeled benchmark.

docs/test-results/v1-vs-v2-20260206-231338/v1-SKILL.md

@@ -0,0 +1,388 @@
+---
+name: last30days
+description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
+argument-hint: "[topic] for [tool]" or "[topic]"
+allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
+---
+
+# last30days: Research Any Topic from the Last 30 Days
+
+Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
+
+Use cases:
+- **Prompting**: "photorealistic people in Nano Banana Pro", "Midjourney prompts", "ChatGPT image generation" → learn techniques, get copy-paste prompts
+- **Recommendations**: "best Claude Code skills", "top AI tools" → get a LIST of specific things people mention
+- **News**: "what's happening with OpenAI", "latest AI announcements" → current events and updates
+- **General**: any topic you're curious about → understand what the community is saying
+
+## CRITICAL: Parse User Intent
+
+Before doing anything, parse the user's input for:
+
+1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
+2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
+3. **QUERY TYPE**: What kind of research they want:
+   - **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
+   - **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
+   - **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
+   - **GENERAL** - anything else → User wants broad understanding of the topic
+
+Common patterns:
+- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
+- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
+- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
+- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
+- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
+
+**IMPORTANT: Do NOT ask about target tool before research.**
+- If tool is specified in the query, use it
+- If tool is NOT specified, run research first, then ask AFTER showing results
+
+**Store these variables:**
+- `TOPIC = [extracted topic]`
+- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
+- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`
+
+---
+
+## Setup Check
+
+The skill works in three modes based on available API keys:
+
+1. **Full Mode** (both keys): Reddit + X + WebSearch - best results with engagement metrics
+2. **Partial Mode** (one key): Reddit-only or X-only + WebSearch
+3. **Web-Only Mode** (no keys): WebSearch only - still useful, but no engagement metrics
+
+**API keys are OPTIONAL.** The skill will work without them using WebSearch fallback.
+
+### First-Time Setup (Optional but Recommended)
+
+If the user wants to add API keys for better results:
+
+```bash
+mkdir -p ~/.config/last30days
+cat > ~/.config/last30days/.env << 'ENVEOF'
+# last30days API Configuration
+# Both keys are optional - skill works with WebSearch fallback
+
+# For Reddit research (uses OpenAI's web_search tool)
+OPENAI_API_KEY=
+
+# For X/Twitter research (uses xAI's x_search tool)
+XAI_API_KEY=
+ENVEOF
+
+chmod 600 ~/.config/last30days/.env
+echo "Config created at ~/.config/last30days/.env"
+echo "Edit to add your API keys for enhanced research."
+```
+
+**DO NOT stop if no keys are configured.** Proceed with web-only mode.
+
+---
+
+## Research Execution
+
+**IMPORTANT: The script handles API key detection automatically.** Run it and check the output to determine mode.
+
+**Step 1: Run the research script**
+```bash
+python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
+```
+
+The script will automatically:
+- Detect available API keys
+- Show a promo banner if keys are missing (this is intentional marketing)
+- Run Reddit/X searches if keys exist
+- Signal if WebSearch is needed
+
+**Step 2: Check the output mode**
+
+The script output will indicate the mode:
+- **"Mode: both"** or **"Mode: reddit-only"** or **"Mode: x-only"**: Script found results, WebSearch is supplementary
+- **"Mode: web-only"**: No API keys, Claude must do ALL research via WebSearch
+
+**Step 3: Do WebSearch**
+
+For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).
+
+Choose search queries based on QUERY_TYPE:
+
+**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
+- Search for: `best {TOPIC} recommendations`
+- Search for: `{TOPIC} list examples`
+- Search for: `most popular {TOPIC}`
+- Goal: Find SPECIFIC NAMES of things, not generic advice
+
+**If NEWS** ("what's happening with X", "X news"):
+- Search for: `{TOPIC} news 2026`
+- Search for: `{TOPIC} announcement update`
+- Goal: Find current events and recent developments
+
+**If PROMPTING** ("X prompts", "prompting for X"):
+- Search for: `{TOPIC} prompts examples 2026`
+- Search for: `{TOPIC} techniques tips`
+- Goal: Find prompting techniques and examples to create copy-paste prompts
+
+**If GENERAL** (default):
+- Search for: `{TOPIC} 2026`
+- Search for: `{TOPIC} discussion`
+- Goal: Find what people are actually saying
+
+For ALL query types:
+- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
+  - If user says "ChatGPT image prompting", search for "ChatGPT image prompting"
+  - Do NOT add "DALL-E", "GPT-4o", or other terms you think are related
+  - Your knowledge may be outdated - trust the user's terminology
+- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
+- INCLUDE: blogs, tutorials, docs, news, GitHub repos
+- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end
+
+**Step 3: Wait for background script to complete**
+Use TaskOutput to get the script results before proceeding to synthesis.
+
+**Depth options** (passed through from user's command):
+- `--quick` → Faster, fewer sources (8-12 each)
+- (default) → Balanced (20-30 each)
+- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
+
+---
+
+## Judge Agent: Synthesize All Sources
+
+**After all searches complete, internally synthesize (don't display stats yet):**
+
+The Judge Agent must:
+1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
+2. Weight WebSearch sources LOWER (no engagement data)
+3. Identify patterns that appear across ALL three sources (strongest signals)
+4. Note any contradictions between sources
+5. Extract the top 3-5 actionable insights
+
+**Do NOT display stats here - they come at the end, right before the invitation.**
+
+---
+
+## FIRST: Internalize the Research
+
+**CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.**
+
+Read the research output carefully. Pay attention to:
+- **Exact product/tool names** mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
+- **Specific quotes and insights** from the sources - use THESE, not generic knowledge
+- **What the sources actually say**, not what you assume the topic is about
+
+**ANTI-PATTERN TO AVOID**: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
+
+### If QUERY_TYPE = RECOMMENDATIONS
+
+**CRITICAL: Extract SPECIFIC NAMES, not generic patterns.**
+
+When user asks "best X" or "top X", they want a LIST of specific things:
+- Scan research for specific product names, tool names, project names, skill names, etc.
+- Count how many times each is mentioned
+- Note which sources recommend each (Reddit thread, X post, blog)
+- List them by popularity/mention count
+
+**BAD synthesis for "best Claude Code skills":**
+> "Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
+
+**GOOD synthesis for "best Claude Code skills":**
+> "Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."
+
+### For all QUERY_TYPEs
+
+Identify from the ACTUAL RESEARCH OUTPUT:
+- **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords? THIS IS CRITICAL.
+- The top 3-5 patterns/techniques that appeared across multiple sources
+- Specific keywords, structures, or approaches mentioned BY THE SOURCES
+- Common pitfalls mentioned BY THE SOURCES
+
+**If research says "use JSON prompts" or "structured prompts", you MUST deliver prompts in that format later.**
+
+---
+
+## THEN: Show Summary + Invite Vision
+
+**CRITICAL: Do NOT output any "Sources:" lists. The final display should be clean.**
+
+**Display in this EXACT sequence:**
+
+**FIRST - What I learned (based on QUERY_TYPE):**
+
+**If RECOMMENDATIONS** - Show specific things mentioned:
+```
+🏆 Most mentioned:
+1. [Specific name] - mentioned {n}x (r/sub, @handle, blog.com)
+2. [Specific name] - mentioned {n}x (sources)
+3. [Specific name] - mentioned {n}x (sources)
+4. [Specific name] - mentioned {n}x (sources)
+5. [Specific name] - mentioned {n}x (sources)
+
+Notable mentions: [other specific things with 1-2 mentions]
+```
+
+**If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
+```
+What I learned:
+
+[2-4 sentences synthesizing key insights FROM THE ACTUAL RESEARCH OUTPUT.]
+
+KEY PATTERNS I'll use:
+1. [Pattern from research]
+2. [Pattern from research]
+3. [Pattern from research]
+```
+
+**THEN - Stats (right before invitation):**
+
+For **full/partial mode** (has API keys):
+```
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: {n} threads │ {sum} upvotes │ {sum} comments
+├─ 🔵 X: {n} posts │ {sum} likes │ {sum} reposts
+├─ 🌐 Web: {n} pages │ {domains}
+└─ Top voices: r/{sub1}, r/{sub2} │ @{handle1}, @{handle2} │ {web_author} on {site}
+```
+
+For **web-only mode** (no API keys):
+```
+---
+✅ Research complete!
+├─ 🌐 Web: {n} pages │ {domains}
+└─ Top sources: {author1} on {site1}, {author2} on {site2}
+
+💡 Want engagement metrics? Add API keys to ~/.config/last30days/.env
+   - OPENAI_API_KEY → Reddit (real upvotes & comments)
+   - XAI_API_KEY → X/Twitter (real likes & reposts)
+```
+
+**LAST - Invitation:**
+```
+---
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
+```
+
+**Use real numbers from the research output.** The patterns should be actual insights from the research, not generic advice.
+
+**SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If the research was about ClawdBot (a self-hosted AI agent), your summary should be about ClawdBot, not Claude Code. If you catch yourself projecting your own knowledge instead of the research, rewrite it.
+
+**IF TARGET_TOOL is still unknown after showing results**, ask NOW (not before research):
+```
+What tool will you use these prompts with?
+
+Options:
+1. [Most relevant tool based on research - e.g., if research mentioned Figma/Sketch, offer those]
+2. Nano Banana Pro (image generation)
+3. ChatGPT / Claude (text/code)
+4. Other (tell me)
+```
+
+**IMPORTANT**: After displaying this, WAIT for the user to respond. Don't dump generic prompts.
+
+---
+
+## WAIT FOR USER'S VISION
+
+After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create.
+
+When they respond with their vision (e.g., "I want a landing page mockup for my SaaS app"), THEN write a single, thoughtful, tailored prompt.
+
+---
+
+## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt
+
+Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise.
+
+### CRITICAL: Match the FORMAT the research recommends
+
+**If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT:**
+
+- Research says "JSON prompts" → Write the prompt AS JSON
+- Research says "structured parameters" → Use structured key: value format
+- Research says "natural language" → Use conversational prose
+- Research says "keyword lists" → Use comma-separated keywords
+
+**ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
+
+### Output Format:
+
+```
+Here's your prompt for {TARGET_TOOL}:
+
+---
+
+[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS - if research said JSON, this is JSON. If research said natural language, this is prose. Match what works.]
+
+---
+
+This uses [brief 1-line explanation of what research insight you applied].
+```
+
+### Quality Checklist:
+- [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format
+- [ ] Directly addresses what the user said they want to create
+- [ ] Uses specific patterns/keywords discovered in research
+- [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
+- [ ] Appropriate length and style for TARGET_TOOL
+
+---
+
+## IF USER ASKS FOR MORE OPTIONS
+
+Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
+
+---
+
+## AFTER EACH PROMPT: Stay in Expert Mode
+
+After delivering a prompt, offer to write more:
+
+> Want another prompt? Just tell me what you're creating next.
+
+---
+
+## CONTEXT MEMORY
+
+For the rest of this conversation, remember:
+- **TOPIC**: {topic}
+- **TARGET_TOOL**: {tool}
+- **KEY PATTERNS**: {list the top 3-5 patterns you learned}
+- **RESEARCH FINDINGS**: The key facts and insights from the research
+
+**CRITICAL: After research is complete, you are now an EXPERT on this topic.**
+
+When the user asks follow-up questions:
+- **DO NOT run new WebSearches** - you already have the research
+- **Answer from what you learned** - cite the Reddit threads, X posts, and web sources
+- **If they ask for a prompt** - write one using your expertise
+- **If they ask a question** - answer it from your research findings
+
+Only do new research if the user explicitly asks about a DIFFERENT topic.
+
+---
+
+## Output Summary Footer (After Each Prompt)
+
+After delivering a prompt, end with:
+
+For **full/partial mode**:
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages
+
+Want another prompt? Just tell me what you're creating next.
+```
+
+For **web-only mode**:
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} web pages from {domains}
+
+Want another prompt? Just tell me what you're creating next.
+
+💡 Unlock Reddit & X data: Add API keys to ~/.config/last30days/.env
+```

docs/test-results/v1-vs-v2-quick/analysis.md

@@ -0,0 +1,310 @@
+# V1 vs V2 Comparison Analysis
+
+**Date:** 2026-02-06
+**Queries tested:** 4 (1 head-to-head, 3 V1-only)
+**Scope:** Quick smoke test, not full 17-query matrix
+
+---
+
+## Part 1: Head-to-Head -- "kanye west" (NEWS Query)
+
+### Dimension-by-Dimension Scoring
+
+#### 1. Query Parsing Display
+
+Does it show the `🔍 **{TOPIC}** · {QUERY_TYPE}` line before running tools?
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 1 | No parsing display at all. Output starts with "## What I learned:" -- jumps straight into synthesis. No acknowledgment of topic or query type before research. |
+| V2 | 1 | No parsing display either. Output starts with "Here's what I found:" then "## What I learned:" -- same problem as V1. |
+
+**Analysis:** Neither version actually rendered the query parsing display. V2 SKILL.md explicitly requires `🔍 **kanye west** · News` before any tools run, but the agent did not produce it. This is a V2 instruction that failed to land. Both score 1/5.
+
+Possible cause: The parsing display is supposed to appear *before* tools are called -- it may have been shown during execution but not captured in the final output text. If so, both outputs represent only the post-research synthesis, not the full session. Regardless, based on what is in the output files, neither shows it.
+
+---
+
+#### 2. Source Coverage (Reddit/X/Web counts)
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 3 | `Reddit: 0 relevant threads` / `X: 30 posts │ ~10 likes` / `Web: 20+ pages`. Two of three sources returned results. Reddit was zero. |
+| V2 | 3 | `Reddit: 0 threads (no results this cycle)` / `X: 29 posts │ 33 likes │ 14 reposts` / `Web: 30+ pages`. Same pattern: two of three returned results. |
+
+**Analysis:** Nearly identical coverage. Both got zero Reddit results (likely a script/API issue for this topic, not a SKILL.md problem). V2 has slightly more precise X metrics (33 likes, 14 reposts vs. V1's vague "~10 likes"). V2 has more web pages (30+ vs 20+). Both miss the 10+ Reddit threshold for a score of 4+.
+
+---
+
+#### 3. Citation Quality (sparse vs every-sentence)
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 2 | No inline citations at all. The body text makes claims ("full-page Wall Street Journal apology," "Hellwatt Festival in Italy") but never attributes them to a specific source. The stats box lists "Washington Post, Billboard, AllHipHop" but the body has zero `per @handle` or `per Rolling Stone` attributions. |
+| V2 | 5 | Every bold section ends with a sparse, clean citation. Examples: `"per Rolling Stone"`, `"per The Washington Post"`, `"per Billboard"`, `"per AllHipHop"`, `"per The News International"`. One citation per topic, never chained. Exactly what V2 SKILL.md specifies. |
+
+**Analysis:** This is the single biggest quality gap between V1 and V2. V1's output reads like a Wikipedia summary -- informative but ungrounded. V2 reads like a researched briefing where every claim has a named source. V2 nails the "sparse citation" rule from its SKILL.md: `"cite 1 source per pattern, short format: 'per @handle' or 'per r/sub'"`.
+
+V1 quote (no citation): `"He'll headline the new Hellwatt Festival in Italy (July 4-18, 2026)."`
+V2 quote (cited): `"Ye is headlining a brand-new festival at the 103,000-capacity RCF Arena in Italy over three weekends from July 4-18, 2026 — his first-ever live concert in Italy, per Billboard."`
+
+---
+
+#### 4. Summary Structure (bold topic headers, organized sections)
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 3 | Has a coherent narrative structure with a paragraph of synthesis, then a `**KEY THEMES:**` numbered list. But the opening is a single dense paragraph, not broken into scannable sections with bold headers. |
+| V2 | 5 | Each storyline gets its own bold header: `**BULLY Album — March 20, 2026 via Gamma**`, `**Public Apology for Antisemitism**`, `**Hellwatt Festival in Italy**`, `**Health Concerns**`, `**Grammys Ban**`, `**Kim & Lewis Hamilton Buzz**`. Each is a standalone scannable unit with 1-3 sentences. |
+
+**Analysis:** V2 follows the SKILL.md template exactly: `**{Topic 1}** — [1-2 sentences, per source]`. V1 uses a blob + list approach which is readable but less scannable. V2 is notably better for a user who wants to skim and find the story they care about.
+
+V1 structure: 1 dense paragraph -> 5-item `KEY THEMES` list
+V2 structure: 6 bold topic cards, each self-contained -> no KEY THEMES list (but doesn't need one because the structure itself is the organization)
+
+---
+
+#### 5. Stats Box Format (emoji tree vs plain text)
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 4 | Uses `├─` tree format with emoji: `├─ 🟠 Reddit: 0 relevant threads` / `├─ 🔵 X: 30 posts` / `├─ 🌐 Web: 20+ pages` / `└─ Top voices:`. Minor deviation: says "0 relevant threads (filtered out noise)" instead of the V1 SKILL.md template "0 threads (no results this cycle)". Also omits the `🗣️` emoji on the Top voices line. |
+| V2 | 5 | Perfect match to V2 SKILL.md template: `├─ 🟠 Reddit: 0 threads (no results this cycle)` / `├─ 🔵 X: 29 posts │ 33 likes │ 14 reposts (via xAI)` / `├─ 🌐 Web: 30+ pages │ rollingstone.com, ...` / `└─ 🗣️ Top voices: @honest30bgfan_ (33 likes), @HipHopCrave_ │ Rolling Stone, Washington Post, Complex`. Includes `(via xAI)` notation, `🗣️` emoji, @handles with engagement counts. |
+
+**Analysis:** V2 is tighter and matches its template exactly. V1 is close but has minor deviations (custom "filtered out noise" text, missing `🗣️` emoji, no @handles or engagement counts on Top voices). V2's inclusion of actual @handles with like counts (`@honest30bgfan_ (33 likes)`) adds credibility.
+
+---
+
+#### 6. Research Grounding (actual research vs generic knowledge)
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 4 | Clearly grounded: mentions specific details like "Wall Street Journal apology (Jan 26, 2026)," "four-month-long manic episode," "frontal-lobe brain injury," "North West collaborated on 'Piercings on My Hand,'" "Monumental Plaza de Toros." These are specific enough to be from research, not pre-training. Minor generic leakage: the "KEY THEMES" list uses editorial framing ("Accountability arc," "Mental health transparency") that feels more like analysis than research extraction. |
+| V2 | 5 | Every fact is specific and attributed: "12th studio album," "13-track project features Peso Pluma, Playboi Carti, and Ty Dolla Sign," "earlier leak versions used AI-deepfaked vocals, which have reportedly been re-recorded," "103,000-capacity RCF Arena." The AI-deepfaked vocals detail is a standout -- it is clearly from research, not something a model would know from pre-training. The Kim/Lewis Hamilton item (`"X chatter is heavily focused on Kim Kardashian's relationship with Lewis Hamilton"`) is explicitly sourced from X data, not general knowledge. |
+
+**Analysis:** Both are well-grounded, but V2 has more "could only come from research" details. The deepfaked vocals story, the exact venue capacity, and the explicit X chatter observation are details that prove the synthesis is from the research output, not hallucinated.
+
+---
+
+#### 7. Prompt Quality (invitation to share vision, not dumping prompts)
+
+| Version | Score | Evidence |
+|---------|-------|----------|
+| V1 | 3 | Ends with: `"Want to dive deeper into any of these threads — the apology, the new albums, the Grammys situation, or Bianca Censori? Just tell me what angle you're interested in."` This is a follow-up invitation, but it is NOT the SKILL.md-specified invitation. It is topic-specific and conversational, which is nice, but it does not ask the user to "share your vision for what you want to create." It misses the prompt-generation angle entirely. |
+| V2 | 5 | Ends with exactly: `"Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into your tool of choice."` This matches the V2 SKILL.md template verbatim. It positions the skill correctly: not a news summarizer but a research-to-prompt pipeline. |
+
+**Analysis:** V1's closing is friendly but off-brand. It treats the skill as a research tool, not a research-to-prompt tool. V2 correctly frames the next step as "tell me what to create and I'll write the prompt." This is a meaningful difference -- V1 would leave a user thinking they just got a summary, while V2 primes them to get a usable output.
+
+---
+
+### Head-to-Head Scorecard
+
+| Dimension | V1 | V2 | Winner |
+|-----------|----|----|--------|
+| 1. Query Parsing Display | 1 | 1 | Tie (both failed) |
+| 2. Source Coverage | 3 | 3 | Tie |
+| 3. Citation Quality | 2 | 5 | **V2 (+3)** |
+| 4. Summary Structure | 3 | 5 | **V2 (+2)** |
+| 5. Stats Box Format | 4 | 5 | **V2 (+1)** |
+| 6. Research Grounding | 4 | 5 | **V2 (+1)** |
+| 7. Prompt Quality (invitation) | 3 | 5 | **V2 (+2)** |
+| **TOTAL** | **20/35** | **29/35** | **V2 wins by 9 points** |
+
+**V2 is clearly better.** The biggest gaps are citation quality (+3) and summary structure (+2). V2's output reads like a professional research briefing; V1's reads like a decent but unstructured summary.
+
+---
+
+## Part 2: V1-Only Outputs Analysis
+
+### Output 1: "open claw" (GENERAL query)
+
+**What V1 does well:**
+- Strong research grounding. Mentions exact numbers: "145,000+ GitHub stars," "20,000+ forks," "700+ skills," "341 malicious skills." These are clearly from research.
+- The KEY PATTERNS section is excellent: 5 well-organized patterns with community quotes (`"I give it sudo and let it configure everything"` vs `"prompt injection is terrifying when you give the bot access to your actual bank account"`).
+- Good synthesis of the security vs. enthusiasm tension -- captures the community split accurately.
+- Stats box uses the emoji tree format correctly with `├──` (though note: uses double-dash `──` instead of single `─`, minor inconsistency).
+
+**What V1 is missing (per V2 SKILL.md features):**
+- No query parsing display (`🔍 **open claw** · General`).
+- No inline citations in the body text. The 5 KEY PATTERNS have no `per @handle` or `per r/sub` attribution. Which Reddit thread said "I give it sudo"? Which X post raised the security concern? We do not know.
+- The stats box says `├── 🟠 Reddit: 25 threads │ ~750+ upvotes` -- the tilde and plus are imprecise. V2 SKILL.md wants exact parsed numbers.
+- Top voices line lists subreddits and handles but no engagement counts: `@grok, @Starlink` -- are these the highest-engagement handles? No like counts shown.
+- No bold topic headers in the body -- it is a single paragraph followed by a numbered list, not the `**{Topic}** — sentence, per source` format V2 requires.
+
+**V1 Score (estimated):** 22/35
+
+---
+
+### Output 2: "nano banana pro prompting" (PROMPTING query)
+
+**What V1 does well:**
+- Correctly identifies two prompting styles (JSON structured vs. natural language "Creative Director") and explains when each works best. This is excellent PROMPTING-type synthesis.
+- KEY PATTERNS are specific and actionable: "85mm lens at f/1.8," "three-point lighting with key at 45 degrees," "text rendering works -- keep text under 3 words for best results (75% success rate)." These are concrete tips a user can apply immediately.
+- Research grounding is strong: cites specific upvote counts ("149-259 upvotes"), subreddit names (`r/nanobanana2pro`), and the Google AI blog.
+- The invitation correctly targets Nano Banana Pro: `"Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into Nano Banana Pro."`
+
+**What V1 is missing (per V2 SKILL.md features):**
+- No query parsing display.
+- Stats box uses plain text dashes: `- 🟠 Reddit: 5 threads | 638 upvotes | 66 comments` instead of the tree format `├─ 🟠 Reddit:`. Uses `|` pipe instead of `│` box-drawing character. V2 SKILL.md explicitly says: "NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji."
+- No inline body citations. KEY PATTERNS mention Reddit upvote ranges but no specific `per @handle` attributions.
+- Missing `✅ All agents reported back!` header -- just says "All agents reported back!" without the checkmark.
+- Body structure is paragraph + numbered list, not bold topic headers.
+
+**V1 Score (estimated):** 23/35 (slightly higher than open claw due to better actionability)
+
+---
+
+### Output 3: "how to best setup clawdbot" (HOW-TO query)
+
+**What V1 does well:**
+- This is the best V1 output of the batch. It goes beyond synthesis and actually delivers a **Quick-Start guide** with numbered steps, a **Security Hardening** checklist, and a **Budget Option** -- all grounded in research.
+- Excellent research grounding: `"per @shynxbt: Use a free AWS VPS + Claude Haiku model + Telegram bot = fully functional for $0"` -- this is an actual citation with an @handle!
+- Specific, actionable recommendations: exact commands (`curl -fsSL https://clawd.bot/install.sh | bash`), specific model recommendations (Claude Opus 4.5 for best results, GLM 4.7 Flash for local), specific channel advice (Telegram first, WhatsApp QR code fails).
+- Stats box is correct emoji tree format with engagement counts: `@aashatwt (452 likes), @recap_david (329 likes)`.
+- Captures the naming confusion accurately: "Clawdbot -> Moltbot -> OpenClaw."
+
+**What V1 is missing (per V2 SKILL.md features):**
+- No query parsing display.
+- Body text has no inline citations except the Budget Option section. The 5 KEY PATTERNS have no `per @handle` attribution.
+- Bold topic headers are used only in the Quick-Start and Security sections, not in the KEY PATTERNS or intro.
+- The output delivers the "answer" directly (setup guide) rather than waiting for the user's vision and offering to write a prompt. For a HOW-TO query this might be the right call, but it skips the SKILL.md flow of "show research -> invite vision -> write prompt."
+
+**V1 Score (estimated):** 26/35 (best of the V1 outputs)
+
+---
+
+### Patterns Across All V1 Outputs
+
+**Consistent strengths:**
+1. Research grounding is solid across all three. V1 does not hallucinate -- the facts are clearly from the research output, not pre-training.
+2. KEY PATTERNS lists are consistently useful and actionable.
+3. Stats boxes are present in all outputs (though formatting varies).
+4. The invitation/closing line is present in all outputs.
+
+**Consistent weaknesses:**
+1. **No query parsing display** in any output (0 for 4, including Kanye West).
+2. **No inline citations** in the body text (except one @handle in the clawdbot output). The research feels real but is unattributed.
+3. **Stats box formatting is inconsistent.** Open claw uses `├──` (double dash), nano banana pro uses `- 🟠` (plain dash + pipe), clawdbot uses `├─` (correct). Three different formats in three outputs.
+4. **Body structure defaults to paragraph + numbered list** instead of bold topic headers. Only clawdbot partially uses bold headers (in the guide section, not the research section).
+5. **No `(via Bird/xAI)` notation** on X stats in any output.
+
+---
+
+## Part 3: SKILL.md Feature Diff
+
+### Features in V2 but NOT V1
+
+| Feature | V2 Lines | Impact |
+|---------|----------|--------|
+| **Query parsing display** (`🔍 **{TOPIC}** · {QUERY_TYPE}`) | 40-53 | HIGH -- confirms to user the skill understood their request before spending time on research. |
+| **Sparse citation rules** with BAD/GOOD examples | 186-193 | HIGH -- this is the #1 quality differentiator in the Kanye head-to-head. `"per @handle"` format, never chain multiple citations. |
+| **Bold topic headers** template (`**{Topic 1}** — [1-2 sentences, per source]`) | 195-208 | HIGH -- makes output scannable. |
+| **Strict stats template** with "NEVER use plain text dashes" instruction | 217-230 | MEDIUM -- prevents the formatting inconsistency seen across V1 outputs. |
+| **RECOMMENDATIONS source attribution** (each item MUST have Sources: line with @handles) | 178-182 | MEDIUM -- only affects RECOMMENDATIONS queries. |
+| **Reddit 0 results handling** (explicit instruction for what to write) | 229 | LOW -- edge case, but prevents ad-hoc text like V1's "filtered out noise." |
+| **Bird CLI / xAI notation** in stats | 223 | LOW -- cosmetic transparency about data source. |
+| **Step 2 phrasing: "DO WEBSEARCH WHILE SCRIPT RUNS"** | 71-73 | LOW -- execution optimization, no output impact. |
+
+### Features in V1 but NOT V2
+
+| Feature | V1 Lines | Impact | Should Restore? |
+|---------|----------|--------|-----------------|
+| **Use cases block** (4 examples in intro) | 12-17 | LOW | No |
+| **Setup Check section** (3 modes, bash script, "keys are OPTIONAL") | 50-78 | MEDIUM for new users | Yes, for public release |
+| **BAD/GOOD synthesis anti-pattern examples** | 172-191 | MEDIUM-HIGH | YES |
+| **Self-check instruction** ("Re-read your 'What I learned' section...") | 269 | MEDIUM | YES |
+| **Quality Checklist** (5-point checklist before delivering prompt) | 306-324 | HIGH | YES |
+| **Prompt format anti-pattern** ("Research says JSON but you write prose") | 302 | MEDIUM | YES |
+| **"IF USER ASKS FOR MORE OPTIONS"** section | 327-329 | LOW-MEDIUM | YES |
+| **Web-only mode stats template + promo** | 248-259 | MEDIUM for no-key users | For public release |
+| **TARGET_TOOL question template** (4 options) | 272-280 | LOW | No |
+| **Context Memory: explicit "don't re-search" instructions** | 342-358 | MEDIUM | YES |
+| **Output footer emoji + engagement counts** | 366-380 | LOW | YES |
+
+### Features in BOTH (Shared)
+
+| Feature | Notes |
+|---------|-------|
+| Parse User Intent (TOPIC, TARGET_TOOL, QUERY_TYPE) | Same 4 query types, same detection logic |
+| "Don't ask about tool before research" rule | Identical |
+| Research script execution command | Same `python3` command |
+| WebSearch queries by QUERY_TYPE | Same search strategies |
+| "Use user's exact terminology" instruction | V2 shorter but same intent |
+| Judge Agent synthesis logic | Same 5-step weighting process |
+| "Ground in actual research" instruction | Same core instruction, V1 has more examples |
+| RECOMMENDATIONS: extract specific names | Same logic |
+| Prompt format matching | Same instruction |
+| Wait for user's vision | Same |
+| Write ONE perfect prompt | Same structure |
+| Context Memory | V2 shorter version |
+| Output summary footer | Both have it, V1 has emoji |
+| Depth options (quick/default/deep) | Same |
+| "After each prompt: Stay in Expert Mode" | Same |
+
+### Overall Assessment
+
+**V2 is a clear upgrade in output formatting and citation quality.** The three features V2 adds (query parsing display, sparse citation rules, bold topic headers) directly address the three biggest weaknesses seen across all V1 outputs. The Kanye West head-to-head proves it: V2 scores 29/35 vs V1's 20/35.
+
+**However, V2 dropped several quality guardrails from V1** that do not affect formatting but affect *correctness*: the self-check instruction, the anti-pattern examples, the quality checklist for prompts, and the "don't re-search" context memory rule. These are cheap to restore (under 25 lines total) and protect against subtle failure modes that may not show up in a 1-query test but will appear over dozens of uses.
+
+---
+
+## Part 4: Verdict
+
+### Ship V2 or Not?
+
+**Ship V2 -- but restore the guardrails first.**
+
+V2 is unambiguously better on every formatting dimension. The citation quality improvement alone (V1: 2/5 -> V2: 5/5) makes it worth shipping. The bold topic headers and strict stats template fix the inconsistency problems visible across all V1 outputs.
+
+But V2 dropped 6 guardrail features from V1 that cost almost nothing to include and protect against real failure modes. These should be restored before V2 goes public.
+
+### Remaining Gaps
+
+**Must fix before shipping (affects correctness):**
+
+1. **Restore the quality checklist for prompts.** This is the test plan's #1 priority item. V1 had a 5-point checklist; V2 reduced it to one line. The checklist is what makes prompts feel polished -- it is the "that's a great prompt" mechanism. Add 8 lines.
+
+2. **Restore BAD/GOOD anti-pattern examples.** V2 says "ground in actual research" but does not show what *bad* grounding looks like. V1's ClawdBot/Claude Code conflation example is exactly the kind of concrete negative example that prevents real failures. Add 5 lines.
+
+3. **Restore self-check instruction.** One sentence: "Re-read your 'What I learned' section -- does it match what the research ACTUALLY says?" Zero cost, catches hallucination. Add 2 lines.
+
+4. **Restore "don't re-search" context memory rule.** V2 only says "only do new research if user asks about a DIFFERENT topic." V1 explicitly bans re-searching and tells the agent to answer from existing research. Add 3 lines.
+
+**Should fix (polish):**
+
+5. Restore prompt format anti-pattern ("Research says JSON but you write prose"). Add 2 lines.
+6. Restore "IF USER ASKS FOR MORE OPTIONS" section. Add 2 lines.
+7. Add emoji + engagement counts back to the output summary footer. Edit 3 lines.
+
+**Skip for now:**
+
+8. Setup Check section -- add back for public release, not needed for execution.
+9. Web-only mode stats template -- lower priority, most testers have API keys.
+10. TARGET_TOOL question template -- agent handles this naturally.
+
+### Query Parsing Display: Investigate
+
+Both V1 and V2 scored 1/5 on query parsing display. V2 has the feature in its SKILL.md but the agent did not render it in the captured output. This could mean:
+- The display was shown during execution but not captured (likely -- it appears before tools run, and the output files may only contain post-research content).
+- The instruction is not strong enough and the agent skips it.
+
+**Recommendation:** Verify in a live session whether the parsing display actually appears. If it does not, strengthen the instruction (e.g., "This line MUST be the first thing you output, before any tool calls").
+
+### Total Effort
+
+Restoring all 7 priority items: approximately 25 lines added to V2 SKILL.md. Under 15 minutes of work. The V2 formatting wins are substantial and proven; the V1 guardrails are small and proven. Combining both produces the best version.
+
+### Final Score Summary
+
+| | V1 (Kanye) | V2 (Kanye) | Delta |
+|--|-----------|-----------|-------|
+| Total | 20/35 | 29/35 | **V2 +9** |
+
+| | V1 (Open Claw) | V1 (Nano Banana) | V1 (Clawdbot) | V1 Average |
+|--|---------------|-----------------|--------------|------------|
+| Estimated Total | 22/35 | 23/35 | 26/35 | **23.7/35** |
+
+V2 at 29/35 beats every V1 output, including V1's best (clawdbot at 26/35).
+
+**Decision: Ship V2 with guardrails restored.**

docs/test-results/v1-vs-v2-quick/v1-SKILL.md

@@ -0,0 +1,388 @@
+---
+name: last30days
+description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
+argument-hint: "[topic] for [tool]" or "[topic]"
+allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
+---
+
+# last30days: Research Any Topic from the Last 30 Days
+
+Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
+
+Use cases:
+- **Prompting**: "photorealistic people in Nano Banana Pro", "Midjourney prompts", "ChatGPT image generation" → learn techniques, get copy-paste prompts
+- **Recommendations**: "best Claude Code skills", "top AI tools" → get a LIST of specific things people mention
+- **News**: "what's happening with OpenAI", "latest AI announcements" → current events and updates
+- **General**: any topic you're curious about → understand what the community is saying
+
+## CRITICAL: Parse User Intent
+
+Before doing anything, parse the user's input for:
+
+1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
+2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
+3. **QUERY TYPE**: What kind of research they want:
+   - **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
+   - **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
+   - **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
+   - **GENERAL** - anything else → User wants broad understanding of the topic
+
+Common patterns:
+- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
+- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
+- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
+- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
+- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
+
+**IMPORTANT: Do NOT ask about target tool before research.**
+- If tool is specified in the query, use it
+- If tool is NOT specified, run research first, then ask AFTER showing results
+
+**Store these variables:**
+- `TOPIC = [extracted topic]`
+- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
+- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`
+
+---
+
+## Setup Check
+
+The skill works in three modes based on available API keys:
+
+1. **Full Mode** (both keys): Reddit + X + WebSearch - best results with engagement metrics
+2. **Partial Mode** (one key): Reddit-only or X-only + WebSearch
+3. **Web-Only Mode** (no keys): WebSearch only - still useful, but no engagement metrics
+
+**API keys are OPTIONAL.** The skill will work without them using WebSearch fallback.
+
+### First-Time Setup (Optional but Recommended)
+
+If the user wants to add API keys for better results:
+
+```bash
+mkdir -p ~/.config/last30days
+cat > ~/.config/last30days/.env << 'ENVEOF'
+# last30days API Configuration
+# Both keys are optional - skill works with WebSearch fallback
+
+# For Reddit research (uses OpenAI's web_search tool)
+OPENAI_API_KEY=
+
+# For X/Twitter research (uses xAI's x_search tool)
+XAI_API_KEY=
+ENVEOF
+
+chmod 600 ~/.config/last30days/.env
+echo "Config created at ~/.config/last30days/.env"
+echo "Edit to add your API keys for enhanced research."
+```
+
+**DO NOT stop if no keys are configured.** Proceed with web-only mode.
+
+---
+
+## Research Execution
+
+**IMPORTANT: The script handles API key detection automatically.** Run it and check the output to determine mode.
+
+**Step 1: Run the research script**
+```bash
+python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
+```
+
+The script will automatically:
+- Detect available API keys
+- Show a promo banner if keys are missing (this is intentional marketing)
+- Run Reddit/X searches if keys exist
+- Signal if WebSearch is needed
+
+**Step 2: Check the output mode**
+
+The script output will indicate the mode:
+- **"Mode: both"** or **"Mode: reddit-only"** or **"Mode: x-only"**: Script found results, WebSearch is supplementary
+- **"Mode: web-only"**: No API keys, Claude must do ALL research via WebSearch
+
+**Step 3: Do WebSearch**
+
+For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).
+
+Choose search queries based on QUERY_TYPE:
+
+**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
+- Search for: `best {TOPIC} recommendations`
+- Search for: `{TOPIC} list examples`
+- Search for: `most popular {TOPIC}`
+- Goal: Find SPECIFIC NAMES of things, not generic advice
+
+**If NEWS** ("what's happening with X", "X news"):
+- Search for: `{TOPIC} news 2026`
+- Search for: `{TOPIC} announcement update`
+- Goal: Find current events and recent developments
+
+**If PROMPTING** ("X prompts", "prompting for X"):
+- Search for: `{TOPIC} prompts examples 2026`
+- Search for: `{TOPIC} techniques tips`
+- Goal: Find prompting techniques and examples to create copy-paste prompts
+
+**If GENERAL** (default):
+- Search for: `{TOPIC} 2026`
+- Search for: `{TOPIC} discussion`
+- Goal: Find what people are actually saying
+
+For ALL query types:
+- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
+  - If user says "ChatGPT image prompting", search for "ChatGPT image prompting"
+  - Do NOT add "DALL-E", "GPT-4o", or other terms you think are related
+  - Your knowledge may be outdated - trust the user's terminology
+- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
+- INCLUDE: blogs, tutorials, docs, news, GitHub repos
+- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end
+
+**Step 3: Wait for background script to complete**
+Use TaskOutput to get the script results before proceeding to synthesis.
+
+**Depth options** (passed through from user's command):
+- `--quick` → Faster, fewer sources (8-12 each)
+- (default) → Balanced (20-30 each)
+- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
+
+---
+
+## Judge Agent: Synthesize All Sources
+
+**After all searches complete, internally synthesize (don't display stats yet):**
+
+The Judge Agent must:
+1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
+2. Weight WebSearch sources LOWER (no engagement data)
+3. Identify patterns that appear across ALL three sources (strongest signals)
+4. Note any contradictions between sources
+5. Extract the top 3-5 actionable insights
+
+**Do NOT display stats here - they come at the end, right before the invitation.**
+
+---
+
+## FIRST: Internalize the Research
+
+**CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.**
+
+Read the research output carefully. Pay attention to:
+- **Exact product/tool names** mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
+- **Specific quotes and insights** from the sources - use THESE, not generic knowledge
+- **What the sources actually say**, not what you assume the topic is about
+
+**ANTI-PATTERN TO AVOID**: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
+
+### If QUERY_TYPE = RECOMMENDATIONS
+
+**CRITICAL: Extract SPECIFIC NAMES, not generic patterns.**
+
+When user asks "best X" or "top X", they want a LIST of specific things:
+- Scan research for specific product names, tool names, project names, skill names, etc.
+- Count how many times each is mentioned
+- Note which sources recommend each (Reddit thread, X post, blog)
+- List them by popularity/mention count
+
+**BAD synthesis for "best Claude Code skills":**
+> "Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
+
+**GOOD synthesis for "best Claude Code skills":**
+> "Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."
+
+### For all QUERY_TYPEs
+
+Identify from the ACTUAL RESEARCH OUTPUT:
+- **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords? THIS IS CRITICAL.
+- The top 3-5 patterns/techniques that appeared across multiple sources
+- Specific keywords, structures, or approaches mentioned BY THE SOURCES
+- Common pitfalls mentioned BY THE SOURCES
+
+**If research says "use JSON prompts" or "structured prompts", you MUST deliver prompts in that format later.**
+
+---
+
+## THEN: Show Summary + Invite Vision
+
+**CRITICAL: Do NOT output any "Sources:" lists. The final display should be clean.**
+
+**Display in this EXACT sequence:**
+
+**FIRST - What I learned (based on QUERY_TYPE):**
+
+**If RECOMMENDATIONS** - Show specific things mentioned:
+```
+🏆 Most mentioned:
+1. [Specific name] - mentioned {n}x (r/sub, @handle, blog.com)
+2. [Specific name] - mentioned {n}x (sources)
+3. [Specific name] - mentioned {n}x (sources)
+4. [Specific name] - mentioned {n}x (sources)
+5. [Specific name] - mentioned {n}x (sources)
+
+Notable mentions: [other specific things with 1-2 mentions]
+```
+
+**If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
+```
+What I learned:
+
+[2-4 sentences synthesizing key insights FROM THE ACTUAL RESEARCH OUTPUT.]
+
+KEY PATTERNS I'll use:
+1. [Pattern from research]
+2. [Pattern from research]
+3. [Pattern from research]
+```
+
+**THEN - Stats (right before invitation):**
+
+For **full/partial mode** (has API keys):
+```
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: {n} threads │ {sum} upvotes │ {sum} comments
+├─ 🔵 X: {n} posts │ {sum} likes │ {sum} reposts
+├─ 🌐 Web: {n} pages │ {domains}
+└─ Top voices: r/{sub1}, r/{sub2} │ @{handle1}, @{handle2} │ {web_author} on {site}
+```
+
+For **web-only mode** (no API keys):
+```
+---
+✅ Research complete!
+├─ 🌐 Web: {n} pages │ {domains}
+└─ Top sources: {author1} on {site1}, {author2} on {site2}
+
+💡 Want engagement metrics? Add API keys to ~/.config/last30days/.env
+   - OPENAI_API_KEY → Reddit (real upvotes & comments)
+   - XAI_API_KEY → X/Twitter (real likes & reposts)
+```
+
+**LAST - Invitation:**
+```
+---
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
+```
+
+**Use real numbers from the research output.** The patterns should be actual insights from the research, not generic advice.
+
+**SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If the research was about ClawdBot (a self-hosted AI agent), your summary should be about ClawdBot, not Claude Code. If you catch yourself projecting your own knowledge instead of the research, rewrite it.
+
+**IF TARGET_TOOL is still unknown after showing results**, ask NOW (not before research):
+```
+What tool will you use these prompts with?
+
+Options:
+1. [Most relevant tool based on research - e.g., if research mentioned Figma/Sketch, offer those]
+2. Nano Banana Pro (image generation)
+3. ChatGPT / Claude (text/code)
+4. Other (tell me)
+```
+
+**IMPORTANT**: After displaying this, WAIT for the user to respond. Don't dump generic prompts.
+
+---
+
+## WAIT FOR USER'S VISION
+
+After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create.
+
+When they respond with their vision (e.g., "I want a landing page mockup for my SaaS app"), THEN write a single, thoughtful, tailored prompt.
+
+---
+
+## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt
+
+Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise.
+
+### CRITICAL: Match the FORMAT the research recommends
+
+**If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT:**
+
+- Research says "JSON prompts" → Write the prompt AS JSON
+- Research says "structured parameters" → Use structured key: value format
+- Research says "natural language" → Use conversational prose
+- Research says "keyword lists" → Use comma-separated keywords
+
+**ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
+
+### Output Format:
+
+```
+Here's your prompt for {TARGET_TOOL}:
+
+---
+
+[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS - if research said JSON, this is JSON. If research said natural language, this is prose. Match what works.]
+
+---
+
+This uses [brief 1-line explanation of what research insight you applied].
+```
+
+### Quality Checklist:
+- [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format
+- [ ] Directly addresses what the user said they want to create
+- [ ] Uses specific patterns/keywords discovered in research
+- [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
+- [ ] Appropriate length and style for TARGET_TOOL
+
+---
+
+## IF USER ASKS FOR MORE OPTIONS
+
+Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
+
+---
+
+## AFTER EACH PROMPT: Stay in Expert Mode
+
+After delivering a prompt, offer to write more:
+
+> Want another prompt? Just tell me what you're creating next.
+
+---
+
+## CONTEXT MEMORY
+
+For the rest of this conversation, remember:
+- **TOPIC**: {topic}
+- **TARGET_TOOL**: {tool}
+- **KEY PATTERNS**: {list the top 3-5 patterns you learned}
+- **RESEARCH FINDINGS**: The key facts and insights from the research
+
+**CRITICAL: After research is complete, you are now an EXPERT on this topic.**
+
+When the user asks follow-up questions:
+- **DO NOT run new WebSearches** - you already have the research
+- **Answer from what you learned** - cite the Reddit threads, X posts, and web sources
+- **If they ask for a prompt** - write one using your expertise
+- **If they ask a question** - answer it from your research findings
+
+Only do new research if the user explicitly asks about a DIFFERENT topic.
+
+---
+
+## Output Summary Footer (After Each Prompt)
+
+After delivering a prompt, end with:
+
+For **full/partial mode**:
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages
+
+Want another prompt? Just tell me what you're creating next.
+```
+
+For **web-only mode**:
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} web pages from {domains}
+
+Want another prompt? Just tell me what you're creating next.
+
+💡 Unlock Reddit & X data: Add API keys to ~/.config/last30days/.env
+```

docs/test-results/v1-vs-v2-quick/v1/1-kanye-west.txt

@@ -0,0 +1,25 @@
+## What I learned:
+
+The last 30 days have been one of Kanye West's most significant periods in years. The biggest story is his **full-page Wall Street Journal apology** (Jan 26, 2026) titled "To Those I've Hurt" — a comprehensive open letter addressing his antisemitic remarks and erratic behavior. He attributed his actions to a "four-month-long manic episode" and revealed that a car accident 25 years ago caused a frontal-lobe brain injury that wasn't properly diagnosed until 2023, leading to his bipolar type-1 diagnosis. Multiple sources describe wife **Bianca Censori** as instrumental in getting him to seek treatment during a manic depressive episode.
+
+On the music side, he's announced **two upcoming albums**: *BULLY* (now scheduled for March 20, 2026) and *IN A PERFECT WORLD*. Daughter **North West** collaborated with him on a track called "Piercings on My Hand." He performed a massive show in Mexico City (Jan 30) at the Monumental Plaza de Toros, and he'll headline the new **Hellwatt Festival** in Italy (July 4-18, 2026).
+
+The **2026 Grammys** remain contentious — he's reportedly not invited after last year's red carpet drama. Health concerns also surfaced after a January LA outing where he appeared "noticeably bloated and moving with sluggish energy."
+
+**KEY THEMES:**
+1. **Accountability arc** — The WSJ apology is being widely discussed as either genuine growth or calculated PR
+2. **Mental health transparency** — Brain injury + bipolar diagnosis framing his past behavior
+3. **Bianca Censori's role** — Increasingly seen as a stabilizing force (despite continued provocative public appearances)
+4. **Music comeback** — Two albums announced, active touring in Latin America and Europe
+5. **Grammy exile** — Still persona non grata at major award shows
+
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: 0 relevant threads (filtered out noise)
+├─ 🔵 X: 30 posts │ ~10 likes │ casual fan chatter + Bianca Censori discourse
+├─ 🌐 Web: 20+ pages │ washingtonpost.com, billboard.com, allhiphop.com, euronews.com, rap-up.com
+└─ Top voices: Washington Post, Billboard, AllHipHop, Euronews, The Mirror
+
+---
+
+Want to dive deeper into any of these threads — the apology, the new albums, the Grammys situation, or Bianca Censori? Just tell me what angle you're interested in.

docs/test-results/v1-vs-v2-quick/v1/3-open-claw.txt

@@ -0,0 +1,24 @@
+## What I learned:
+
+OpenClaw (formerly ClawdBot, formerly Moltbot) is an open-source autonomous AI agent created by Peter Steinberger that went massively viral in late January 2026 — hitting 145,000+ GitHub stars and 20,000+ forks. It runs locally on your machine, connects to messaging platforms (Telegram, WhatsApp, Discord, Signal, etc.), and uses LLMs like Claude, GPT, or Gemini to actually perform tasks on your computer — file management, scheduling, web browsing, purchases, and more. Think "JARVIS for everyone."
+
+The community is simultaneously excited and alarmed. The "ClawHub" skill marketplace has 700+ skills but a VirusTotal/Koi Security audit found **341 malicious skills** doing data exfiltration and crypto theft. A high-severity RCE vulnerability was also disclosed. The #1 most downloaded skill on ClawHub was confirmed malware.
+
+**KEY PATTERNS from the research:**
+
+1. **Cost is the top complaint** — API usage burns money fast; many users recommend Gemini CLI (free 1,000 req/day) over Claude/GPT for OpenClaw to control spend
+2. **Security is a dumpster fire** — Supply chain attacks via ClawHub skills, prompt injection risks when browsing the web, RCE vulnerabilities, and orgs are banning it at the EDR level
+3. **Setup is nontrivial** — Even after install, most users get stuck on skills/tools/automation config; the `openclaw onboard` wizard is the recommended path
+4. **The skill ecosystem is powerful but dangerous** — 700+ skills available, but no code signing, no real moderation, and active malware campaigns
+5. **Community is split** — Enthusiasts love the autonomy ("I give it sudo and let it configure everything"), while security folks are terrified ("prompt injection is terrifying when you give the bot access to your actual bank account")
+
+---
+✅ All agents reported back!
+├── 🟠 Reddit: 25 threads │ ~750+ upvotes │ ~600+ comments
+├── 🔵 X: 27 posts │ 1,750+ likes │ 325+ reposts
+├── 🌐 Web: 20+ pages │ Wikipedia, Bloomberg, The Register, Hacker News, Nature, VirusTotal, DigitalOcean, Codecademy, Tom's Hardware
+└── Top voices: r/openclaw, r/cybersecurity, r/clawdbot, r/ArtificialInteligence │ @grok, @Starlink │ Nature, Bloomberg, ACM
+
+---
+
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly.

docs/test-results/v1-vs-v2-quick/v1/4-nano-banana-pro-prompting.txt

@@ -0,0 +1,27 @@
+Now I have a comprehensive picture. Let me synthesize everything.
+
+**What I learned:**
+
+Nano Banana Pro is fundamentally different from older image models — it's a "thinking" model that understands intent, physics, and composition. The community has converged on two distinct prompting styles, and **both work well** depending on the use case:
+
+1. **JSON structured prompts** — Dominant on Reddit (r/nanobanana2pro) and X. Power users share elaborate JSON objects with nested fields for subject, appearance, lighting, composition, and style. These produce the most consistent, controlled results for portraits, editorials, and product shots.
+
+2. **Natural language "Creative Director" prompts** — Recommended by Google's official guide. Full sentences describing the scene as if briefing a photographer. Better for cinematic shots, complex edits, and iterative refinement.
+
+**KEY PATTERNS I'll use:**
+1. **JSON format for maximum control** — The top Reddit posts (149-259 upvotes) all use structured JSON with nested objects: `subject > appearance > hair/skin/expression`, `lighting > key/fill/rim`, `camera > lens/angle/depth_of_field`
+2. **Be a Creative Director, not a tag spammer** — Replace "4k, realistic, beautiful" with specific technical directions: "85mm lens at f/1.8", "three-point lighting with key at 45°", "shallow depth of field with bokeh"
+3. **Provide context for WHY** — Adding purpose ("for a luxury fashion editorial", "for a high-end gourmet cookbook") helps the model infer appropriate styling, plating, lighting
+4. **Edit, don't regenerate** — When 80% is right, ask for the specific change conversationally
+5. **Text rendering works** — Keep text under 3 words for best results (75% success rate), specify font style and placement
+
+---
+All agents reported back!
+- 🟠 Reddit: 5 threads | 638 upvotes | 66 comments
+- 🔵 X: 29 posts | 312 likes | 117 reposts
+- 🌐 Web: 10+ pages | blog.google, dev.to, eweek.com, imagine.art, github.com, leonardo.ai, medium.com
+- Top voices: r/nanobanana2pro | @Xmira_belle, @doctorwasif, @chatgptpaglu | Google AI blog, DEV Community
+
+---
+
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into Nano Banana Pro.

docs/test-results/v1-vs-v2-quick/v1/5-how-to-best-setup-clawdbot.txt

@@ -0,0 +1,48 @@
+**What I learned:**
+
+Clawdbot (now officially renamed **OpenClaw**) is a self-hosted, open-source personal AI assistant that connects to messaging apps (Telegram, WhatsApp, Discord, Slack, Signal, iMessage) and uses LLMs to act as an autonomous agent on your behalf. It's roughly one week old as a public project and the community is moving fast — the name has already changed twice (Clawdbot → Moltbot → OpenClaw).
+
+**KEY PATTERNS from the research:**
+
+1. **Security is the #1 concern** — Multiple Reddit threads and guides warn that many setups are wide open to the internet with zero auth. Run it on dedicated/isolated hardware (VM, Mac Mini, NAS), never your main machine. Block dangerous commands via allowlist. Never add it to group chats.
+
+2. **Use the onboarding wizard, not Docker** — Docker was reported as unreliable (shims, bridges). The single-line install script (`curl -fsSL https://clawd.bot/install.sh | bash`) followed by `openclaw onboard` is the smoothest path. Docker is a "no-go" per multiple users.
+
+3. **Model choice matters a lot** — Claude Opus 4.5 is recommended for best results (trained to resist prompt injection, ~99% resistance). For budget setups, Claude Haiku works at $0. Local models (Ollama/LM Studio) work but cause hallucination and token issues — GLM 4.7 Flash (30B) was recommended as the best local option.
+
+4. **Telegram is the easiest channel to start with** — Most guides and users start with a Telegram bot token. WhatsApp QR code pairing fails frequently. Get Telegram working first, then expand.
+
+5. **Sandbox and isolate** — Set `agents.defaults.sandbox.mode` to `"non-main"` for Docker sandboxing. Use a Linux VM or UTM on Mac to segregate traffic. Tailscale for secure remote access.
+
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: 19 threads │ 415+ upvotes │ 290+ comments
+├─ 🔵 X: 29 posts │ 815+ likes │ 65+ reposts
+├─ 🌐 Web: 10+ pages │ Medium, DataCamp, DEV, GitHub, QNAP, docs.openclaw.ai
+└─ Top voices: r/clawdbot, r/LocalLLM, r/AskVibecoders │ @aashatwt (452 likes), @recap_david (329 likes) │ Lukas Niessen on Medium
+
+---
+
+**Here's the consensus best setup path based on what the community actually recommends:**
+
+### Quick-Start (30 min)
+1. **Get dedicated hardware** — Mac Mini, old laptop, NAS (QNAP has an official guide), or a VPS (DigitalOcean gist available). Don't use your daily driver.
+2. **Install via script**: `curl -fsSL https://clawd.bot/install.sh | bash`
+3. **Run the wizard**: `openclaw onboard` — it walks through gateway, workspace, channels, and skills
+4. **Connect Telegram first** — create a bot via @BotFather, paste the token
+5. **Set your API key** — Anthropic API key for Claude, or connect Ollama for local models
+6. **Customize SOUL.md** — this controls your bot's personality and behavior
+
+### Security Hardening (do this immediately)
+- Bind gateway to localhost only, use Tailscale for remote access
+- Set sandbox mode to `"non-main"`
+- Create an explicit command allowlist (block `rm -rf`, `sudo`, `chmod`)
+- Rotate API keys every 90 days
+- Never add the bot to group chats
+
+### Budget Option ($0)
+Per @shynxbt: Use a free AWS VPS + Claude Haiku model + Telegram bot = fully functional for $0.
+
+---
+
+Want me to help you with a specific part of the setup, or do you have a particular use case in mind (home automation, CRM, coding assistant, etc.)?

docs/test-results/v1-vs-v2-quick/v2-SKILL.md

@@ -0,0 +1,332 @@
+---
+name: last30days
+description: Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.
+argument-hint: '"[topic] for [tool]" or "[topic]"'
+allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
+---
+
+# last30days: Research Any Topic from the Last 30 Days
+
+Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.
+
+## CRITICAL: Parse User Intent
+
+Before doing anything, parse the user's input for:
+
+1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
+2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
+3. **QUERY TYPE**: What kind of research they want:
+   - **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
+   - **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
+   - **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
+   - **GENERAL** - anything else → User wants broad understanding of the topic
+
+Common patterns:
+- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
+- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
+- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
+- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
+- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS
+
+**IMPORTANT: Do NOT ask about target tool before research.**
+- If tool is specified in the query, use it
+- If tool is NOT specified, run research first, then ask AFTER showing results
+
+**Store these variables:**
+- `TOPIC = [extracted topic]`
+- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
+- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`
+
+**DISPLAY your parsing to the user.** Before running any tools, output a single line:
+
+🔍 **{TOPIC}** · {QUERY_TYPE}
+Searching Reddit, X, and the web for {natural language description of what you'll look for}...
+
+Example outputs:
+- 🔍 **kanye west** · News — Searching Reddit, X, and the web for the latest kanye west news and discussions...
+- 🔍 **best MCP servers** · Recommendations — Searching Reddit, X, and the web for the most recommended MCP servers...
+- 🔍 **nano banana pro prompting** · Prompting — Searching Reddit, X, and the web for nano banana pro prompting techniques and tips...
+- 🔍 **open claw** · General — Searching Reddit, X, and the web for what people are saying about open claw...
+
+If TARGET_TOOL is known, mention it: "...for nano banana pro prompting techniques to use in ChatGPT..."
+
+This text MUST appear before you call any tools. It confirms to the user that you understood their request.
+
+---
+
+## Research Execution
+
+**Step 1: Run the research script**
+```bash
+python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1
+```
+
+The script will automatically:
+- Detect available API keys
+- Run Reddit/X searches if keys exist
+- Signal if WebSearch is needed
+
+---
+
+## STEP 2: DO WEBSEARCH WHILE SCRIPT RUNS
+
+The script auto-detects sources (Bird CLI, API keys, etc). While waiting for it, do WebSearch.
+
+For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).
+
+Choose search queries based on QUERY_TYPE:
+
+**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
+- Search for: `best {TOPIC} recommendations`
+- Search for: `{TOPIC} list examples`
+- Search for: `most popular {TOPIC}`
+- Goal: Find SPECIFIC NAMES of things, not generic advice
+
+**If NEWS** ("what's happening with X", "X news"):
+- Search for: `{TOPIC} news 2026`
+- Search for: `{TOPIC} announcement update`
+- Goal: Find current events and recent developments
+
+**If PROMPTING** ("X prompts", "prompting for X"):
+- Search for: `{TOPIC} prompts examples 2026`
+- Search for: `{TOPIC} techniques tips`
+- Goal: Find prompting techniques and examples to create copy-paste prompts
+
+**If GENERAL** (default):
+- Search for: `{TOPIC} 2026`
+- Search for: `{TOPIC} discussion`
+- Goal: Find what people are actually saying
+
+For ALL query types:
+- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
+- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
+- INCLUDE: blogs, tutorials, docs, news, GitHub repos
+- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end
+
+**Depth options** (passed through from user's command):
+- `--quick` → Faster, fewer sources (8-12 each)
+- (default) → Balanced (20-30 each)
+- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
+
+---
+
+## Judge Agent: Synthesize All Sources
+
+**After all searches complete, internally synthesize (don't display stats yet):**
+
+The Judge Agent must:
+1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
+2. Weight WebSearch sources LOWER (no engagement data)
+3. Identify patterns that appear across ALL three sources (strongest signals)
+4. Note any contradictions between sources
+5. Extract the top 3-5 actionable insights
+
+**Do NOT display stats here - they come at the end, right before the invitation.**
+
+---
+
+## FIRST: Internalize the Research
+
+**CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.**
+
+Read the research output carefully. Pay attention to:
+- **Exact product/tool names** mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
+- **Specific quotes and insights** from the sources - use THESE, not generic knowledge
+- **What the sources actually say**, not what you assume the topic is about
+
+**ANTI-PATTERN TO AVOID**: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.
+
+### If QUERY_TYPE = RECOMMENDATIONS
+
+**CRITICAL: Extract SPECIFIC NAMES, not generic patterns.**
+
+When user asks "best X" or "top X", they want a LIST of specific things:
+- Scan research for specific product names, tool names, project names, skill names, etc.
+- Count how many times each is mentioned
+- Note which sources recommend each (Reddit thread, X post, blog)
+- List them by popularity/mention count
+
+**BAD synthesis for "best Claude Code skills":**
+> "Skills are powerful. Keep them under 500 lines. Use progressive disclosure."
+
+**GOOD synthesis for "best Claude Code skills":**
+> "Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."
+
+### For all QUERY_TYPEs
+
+Identify from the ACTUAL RESEARCH OUTPUT:
+- **PROMPT FORMAT** - Does research recommend JSON, structured params, natural language, keywords?
+- The top 3-5 patterns/techniques that appeared across multiple sources
+- Specific keywords, structures, or approaches mentioned BY THE SOURCES
+- Common pitfalls mentioned BY THE SOURCES
+
+---
+
+## THEN: Show Summary + Invite Vision
+
+**Display in this EXACT sequence:**
+
+**FIRST - What I learned (based on QUERY_TYPE):**
+
+**If RECOMMENDATIONS** - Show specific things mentioned with sources:
+```
+🏆 Most mentioned:
+
+[Tool Name] - {n}x mentions
+Use Case: [what it does]
+Sources: @handle1, @handle2, r/sub, blog.com
+
+[Tool Name] - {n}x mentions
+Use Case: [what it does]
+Sources: @handle3, r/sub2, Complex
+
+Notable mentions: [other specific things with 1-2 mentions]
+```
+
+**CRITICAL for RECOMMENDATIONS:**
+- Each item MUST have a "Sources:" line with actual @handles from X posts (e.g., @LONGLIVE47, @ByDobson)
+- Include subreddit names (r/hiphopheads) and web sources (Complex, Variety)
+- Parse @handles from research output and include the highest-engagement ones
+- Format naturally - tables work well for wide terminals, stacked cards for narrow
+
+**If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
+
+CITATION RULE: Cite sources sparingly to prove research is real.
+- In the "What I learned" intro: cite 1-2 top sources total, not every sentence
+- In KEY PATTERNS: cite 1 source per pattern, short format: "per @handle" or "per r/sub"
+- Do NOT include engagement metrics in citations (likes, upvotes) - save those for stats box
+- Do NOT chain multiple citations: "per @x, @y, @z" is too much. Pick the strongest one.
+
+**BAD:** "His album is set for March 20 (per @cocoabutterbf; Rolling Stone; HotNewHipHop; Complex)."
+**GOOD:** "His album BULLY is set for March 20 via Gamma, per Rolling Stone."
+
+```
+What I learned:
+
+**{Topic 1}** — [1-2 sentences about this storyline, per source]
+
+**{Topic 2}** — [1-2 sentences, per source]
+
+**{Topic 3}** — [1-2 sentences, per source]
+
+KEY PATTERNS from the research:
+1. [Pattern] — per @handle
+2. [Pattern] — per r/sub
+3. [Pattern] — per source
+```
+
+**THEN - Stats (right before invitation):**
+
+**CRITICAL: Calculate actual totals from the research output.**
+- Count posts/threads from each section
+- Sum engagement: parse `[Xlikes, Yrt]` from each X post, `[Xpts, Ycmt]` from Reddit
+- Identify top voices: highest-engagement @handles from X, most active subreddits
+
+**Copy this EXACTLY, replacing only the {placeholders}:**
+
+```
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
+├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts (via Bird/xAI)
+├─ 🌐 Web: {N} pages │ {domain1}, {domain2}, {domain3}
+└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
+---
+```
+
+If Reddit returned 0 threads, write: "├─ 🟠 Reddit: 0 threads (no results this cycle)"
+NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji.
+
+**SELF-CHECK before displaying**: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If you catch yourself projecting your own knowledge instead of the research, rewrite it.
+
+**LAST - Invitation:**
+```
+---
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into {TARGET_TOOL}.
+```
+
+---
+
+## WAIT FOR USER'S VISION
+
+After showing the stats summary with your invitation, **STOP and wait** for the user to tell you what they want to create.
+
+---
+
+## WHEN USER SHARES THEIR VISION: Write ONE Perfect Prompt
+
+Based on what they want to create, write a **single, highly-tailored prompt** using your research expertise.
+
+### CRITICAL: Match the FORMAT the research recommends
+
+**If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT.**
+
+**ANTI-PATTERN**: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
+
+### Quality Checklist (run before delivering):
+- [ ] **FORMAT MATCHES RESEARCH** - If research said JSON/structured/etc, prompt IS that format
+- [ ] Directly addresses what the user said they want to create
+- [ ] Uses specific patterns/keywords discovered in research
+- [ ] Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
+- [ ] Appropriate length and style for TARGET_TOOL
+
+### Output Format:
+
+```
+Here's your prompt for {TARGET_TOOL}:
+
+---
+
+[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS]
+
+---
+
+This uses [brief 1-line explanation of what research insight you applied].
+```
+
+---
+
+## IF USER ASKS FOR MORE OPTIONS
+
+Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
+
+---
+
+## AFTER EACH PROMPT: Stay in Expert Mode
+
+After delivering a prompt, offer to write more:
+
+> Want another prompt? Just tell me what you're creating next.
+
+---
+
+## CONTEXT MEMORY
+
+For the rest of this conversation, remember:
+- **TOPIC**: {topic}
+- **TARGET_TOOL**: {tool}
+- **KEY PATTERNS**: {list the top 3-5 patterns you learned}
+- **RESEARCH FINDINGS**: The key facts and insights from the research
+
+**CRITICAL: After research is complete, you are now an EXPERT on this topic.**
+
+When the user asks follow-up questions:
+- **DO NOT run new WebSearches** - you already have the research
+- **Answer from what you learned** - cite the Reddit threads, X posts, and web sources
+- **If they ask for a prompt** - write one using your expertise
+
+Only do new research if the user explicitly asks about a DIFFERENT topic.
+
+---
+
+## Output Summary Footer (After Each Prompt)
+
+After delivering a prompt, end with:
+
+```
+---
+📚 Expert in: {TOPIC} for {TARGET_TOOL}
+📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} web pages
+
+Want another prompt? Just tell me what you're creating next.
+```

docs/test-results/v1-vs-v2-quick/v2/1-kanye-west.txt

@@ -0,0 +1,25 @@
+Here's what I found:
+
+## What I learned:
+
+**BULLY Album — March 20, 2026 via Gamma** — After years of delays (first announced September 2024), Kanye's 12th studio album finally has a firm release date. The 13-track project features Peso Pluma, Playboi Carti, and Ty Dolla Sign. Sonically it recalls *808s & Heartbreak* and *MBDTF* — Ye mostly sings rather than raps. Notably, earlier leak versions used AI-deepfaked vocals, which have reportedly been re-recorded with his real voice for the official release, per Rolling Stone.
+
+**Public Apology for Antisemitism** — On January 26, Ye took out a full-page Wall Street Journal ad titled "To Those I've Hurt," publicly apologizing for his antisemitic remarks. He disclosed a previously undiagnosed brain injury from his 2002 car accident and attributed his behavior to a four-month manic episode fueled by bipolar disorder, psychosis, and paranoia. "I lost touch with reality," he wrote. The apology also extended to the Black community, per The Washington Post.
+
+**Hellwatt Festival in Italy** — Ye is headlining a brand-new festival at the 103,000-capacity RCF Arena in Italy over three weekends from July 4-18, 2026 — his first-ever live concert in Italy, per Billboard.
+
+**Health Concerns** — A rare January 2026 outing in LA with Bianca Censori reignited concern about Ye's physical appearance. Insiders point to medication side effects, frequent travel, and inconsistent routines, per AllHipHop.
+
+**Grammys Ban** — Ye is reportedly not welcome at the 2026 Grammy Awards after clashing with organizers last year over his invitation terms, per The News International.
+
+**Kim & Lewis Hamilton Buzz** — X chatter is heavily focused on Kim Kardashian's relationship with Lewis Hamilton, with users contrasting her new relationship against her marriage to Ye.
+
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: 0 threads (no results this cycle)
+├─ 🔵 X: 29 posts │ 33 likes │ 14 reposts (via xAI)
+├─ 🌐 Web: 30+ pages │ rollingstone.com, washingtonpost.com, complex.com, billboard.com, npr.org
+└─ 🗣️ Top voices: @honest30bgfan_ (33 likes), @HipHopCrave_ │ Rolling Stone, Washington Post, Complex
+---
+
+Share your vision for what you want to create and I'll write a thoughtful prompt you can copy-paste directly into your tool of choice.

docs/v2.1-launch-copy.md

@@ -0,0 +1,120 @@
+# v2.1 Launch Copy (DRAFT — do not publish)
+
+## Context
+
+Bird CLI (@steipete/bird) has been deprecated on npm and the GitHub repo deleted. steipete was asked to take it down (likely by X). Nobody has forked and republished it. Our v2.1 vendors a search-only subset of Bird v0.8.0 (MIT licensed) so users don't need any external tools.
+
+YouTube transcripts are the second headline feature. Inspired by Peter Steinberger's yt-dlp + summarize toolchain. We use yt-dlp directly (no summarize dependency) — search YouTube, grab transcripts, feed them into the synthesis. Zero API keys, zero cost.
+
+---
+
+## README: "New in V2.1" blurb
+
+**New in V2.1 — two headline features:**
+
+- **YouTube transcripts as a 4th source.** When yt-dlp is installed, /last30days automatically searches YouTube, grabs view counts, and extracts auto-generated transcripts from the top videos. A 20-minute review contains 10x the signal of a tweet — now the skill reads it. Inspired by @steipete's yt-dlp + summarize toolchain.
+- **X search is fully bundled.** No external `bird` CLI install needed. Add `AUTH_TOKEN` and `CT0` once, and the vendored Bird client runs locally without browser-cookie prompts. `XAI_API_KEY` remains an optional fallback.
+
+---
+
+## README: X Search Authentication section
+
+### X Search Authentication
+
+X search prefers explicit env auth. This keeps local runs headless and avoids browser-cookie and macOS Keychain prompts.
+
+**Recommended setup:** While logged into x.com once, open browser dev tools and copy the `auth_token` and `ct0` cookies for `x.com`.
+
+Save them as `AUTH_TOKEN` and `CT0` in `~/.config/last30days/.env` or `.claude/last30days.env`:
+```bash
+AUTH_TOKEN=your_auth_token
+CT0=your_ct0_token
+```
+
+**xAI fallback:** If you do not want to provide `AUTH_TOKEN` and `CT0`, set `XAI_API_KEY` and use xAI's `x_search` backend instead.
+
+**Verify it's working:**
+```bash
+node ~/.claude/skills/last30days/scripts/lib/vendor/bird-search/bird-search.mjs --whoami
+```
+
+---
+
+## README: Install block env line
+
+```bash
+AUTH_TOKEN=...            # recommended for X search
+CT0=...                   # recommended for X search
+XAI_API_KEY=xai-...       # optional X fallback
+```
+
+---
+
+## SKILL.md: Stats line
+
+```
+├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts
+```
+
+---
+
+## GitHub issue #19 response (post AFTER publishing)
+
+> Thanks for reporting this. Bird CLI was deprecated and the GitHub repo was deleted. steipete was asked to take it down.
+>
+> The good news: you don't need Bird anymore. v2.1 (just shipped) bundles X search directly. No external CLI, no `npm install`, no brew. Just Node.js 22+ plus `AUTH_TOKEN` and `CT0`, or `XAI_API_KEY` as fallback.
+>
+> It also adds **YouTube as a 4th source**. When yt-dlp is installed, the skill automatically searches YouTube and extracts transcripts from the top videos. A 20-minute tutorial has 10x the signal of a tweet, and now the synthesis engine reads it.
+>
+> The recommended setup is to copy `auth_token` and `ct0` from x.com once and store them as `AUTH_TOKEN` and `CT0` in your env. That avoids browser-cookie and Keychain prompts during normal runs.
+>
+> The xAI API (`XAI_API_KEY`) also still works as a fallback.
+
+---
+
+## X/Social launch post
+
+### Short (280 chars)
+
+/last30days v2.1 is out 🚀
+
+Two new features:
+→ YouTube transcripts as a 4th source (yt-dlp)
+→ X search fully bundled (no bird CLI install needed)
+
+Research any topic across Reddit, X, YouTube & web in one command.
+
+h/t @steipete for the inspiration on both.
+
+github.com/mvanhorn/last30days-skill
+
+### Thread version (post 1)
+
+/last30days v2.1 just shipped — two headline features:
+
+1️⃣ YouTube transcripts as a 4th source
+When yt-dlp is installed, the skill searches YouTube, grabs view counts, and extracts auto-generated transcripts from top videos. A 20-min review has 10x the signal of a tweet — now the synthesis reads it.
+
+### Thread version (post 2)
+
+2️⃣ X search is fully bundled
+Bird CLI was deprecated. Instead of requiring an external tool, v2.1 vendors a search-only subset. Add `AUTH_TOKEN` and `CT0` once, then it runs locally with no npm install. `XAI_API_KEY` still works as fallback.
+
+Both features inspired by @steipete's tooling.
+
+### Thread version (post 3)
+
+YouTube goes through the same scoring pipeline as Reddit and X — relevance, recency, engagement. Transcripts get truncated to ~500 words per video and fed into the synthesis engine alongside social posts.
+
+Zero API keys for YouTube. Zero cost. Just `brew install yt-dlp`.
+
+### Thread version (post 4)
+
+Try it:
+```
+/last30days [any topic]
+```
+
+Reddit + X + YouTube + Web. Four sources, one command, copy-paste prompts.
+
+github.com/mvanhorn/last30days-skill

docs/v2.5-launch-tweets.md

@@ -0,0 +1,360 @@
+# last30days v2.5 Launch Thread
+
+## FINAL THREAD (6 tweets)
+
+### 1/6 - Announcement
+
+I can't believe it's been 30 days since I launched @slashlast30days. 3.2k stars later, time for v2.5.
+
+Three big additions:
+1. @Polymarket prediction markets as a 6th source - helps you predict the future
+2. Cross-source linking + massively better results - detects when the same story trends across multiple platforms. Ran a 15-way blinded comparison, v2.5 scored 4.38 vs 3.73 for the original. Won all 5 topics.
+3. Hacker News as a 5th source - a window into the tech and developer insider world
+
+github.com/mvanhorn/last30days-skill
+
+### 2/6 - Demo: Anthropic vs Pentagon
+
+"/last30days Anthropic Pete Hegseth"
+
+14 Reddit threads. 29 X posts (11,559 likes). 20 YouTube videos (739K views). 5 HN stories. 9 Polymarket markets.
+
+This story broke TODAY. Hegseth designated Anthropic a "supply chain risk." Trump ordered every agency to stop using their tech.
+
+Polymarket: Anthropic still 99% for best AI model. $500B+ valuation: 68%. IPO >$600B: 97%. Hegseth out by March: only 6%.
+
+Markets say Anthropic wins regardless. That's the kind of signal you can't get from opinion threads.
+
+### 3/6 - Demo: Seedance Prompting
+
+"/last30days Seedance prompting"
+
+13 Reddit threads. 33 X posts. 20 YouTube videos (1.2M views, 4 transcripts). 15 web pages.
+
+Top finding: Seedance 2.0 prompts follow a director's shot-list format, not freeform text. 30-100 words. Subject + Action + Camera + Scene + Style. Beyond 100 words, results degrade.
+
+Then I said: "a cinematic drone shot over a city at golden hour"
+
+It wrote me a copy-paste prompt using the exact patterns from the research. Research first, then create from what you learned.
+
+### 4/6 - Demo: Arizona Basketball
+
+"/last30days arizona basketball"
+
+6 Polymarket markets. 37 X posts (4,200 likes). 15 YouTube videos (517K views). 2 Reddit threads.
+
+Arizona is 25-2, set a program record with a 22-0 start, and holds a 2-game Big 12 lead with 3 games left. The Field of 68 called them "the TOUGHEST team in America" after escaping Baylor shorthanded. Kansas rematch Saturday - the highlight video from their first meeting has 248K views on ESPN's YouTube.
+
+Polymarket: Championship 13%. #1 seed: 88%. Duke and Michigan each at 18% to win it all.
+
+That's not a sports blog. That's Reddit reactions + X engagement + YouTube analysis + prediction market odds from one command.
+
+### 5/6 - Demo: Iran War
+
+"/last30days iran war"
+
+2 Reddit threads. 34 X posts (10,048 likes). 20 YouTube videos (1.6M views, 5 transcripts). 4 HN stories (850 points). 14 Polymarket markets ($473M volume).
+
+Geneva talks just ended without a deal. 150+ US aircraft deployed. Two carrier strike groups in position. F-22s sent to Israel. Members of Congress who saw the secret war plan came out "terrified." @cenkuygur: "they are about to drag us into a war that 70-85% of Americans oppose" (7,700 likes).
+
+Polymarket ($473M in volume - one of their biggest markets ever): strikes by 2026: 80%. By March 31: 68%. War Powers invoked: 51%. Formal war declaration: only 12%.
+
+Markets say: strikes are very likely, declared war is not. That's the sharpest signal in the entire research.
+
+### 6/6 - Thank You
+
+Thank you to ARJ999 and wkbaran on GitHub who filed three separate issues asking for Hacker News support. v2.5 delivers.
+
+It's been a crazy 30 days. 3.2k stars. Six sources. Massively better results. Super excited to get this out.
+
+Try it: /last30days [any topic]
+
+github.com/mvanhorn/last30days-skill
+
+---
+---
+
+## REFERENCE MATERIAL BELOW
+
+## Context
+- 3.2k stars on GitHub
+- V2.5 headline features: Polymarket (6th source), Hacker News (5th source), cross-source linking
+- Ran 15-way blinded comparison: 4.38/5.0 vs 3.73/5.0
+- Won all 5 topics, zero regressions
+- Cross-source linking: 3 -> 13 linked items
+- Demo topics: Anthropic odds (11 markets), Arizona basketball (6 markets), Iran war ($425M volume)
+
+---
+
+## Post 1: Lead (Announcement)
+
+V2.5 of @slashlast30days is out. Now with @Polymarket prediction markets, cross-source linking, and massively better results.
+
+1. Polymarket as a 6th source - real money on outcomes, no API key needed
+2. Hacker News as a 5th source
+3. Cross-source linking - detects when the same story trends across multiple platforms
+
+Ran a 15-way blinded comparison across 5 topics. v2.5 scored 4.38 vs 3.73 for the original. Won all 5. Zero regressions.
+
+github.com/mvanhorn/last30days-skill
+
+---
+
+## Post 2: POLYMARKET AS A 6TH SOURCE.
+
+Reddit tells you what people think. X tells you what people share. YouTube tells you what people watch. HN tells you what developers discuss.
+
+Polymarket helps you predict the future.
+
+"/last30days anthropic odds"
+
+11 markets found. Best AI model February: Anthropic 98%. IPO before OpenAI: 64%. $500B+ valuation: 87%. Pentagon ban odds: only 22%.
+
+Free API. No key. Real money on outcomes.
+
+---
+
+## Post 3: CROSS-SOURCE LINKING.
+
+When a Seedance 2.0 tutorial has 44K YouTube views AND trends on HN AND gets discussed on Reddit, v2.5 flags it: [also on: HN, YouTube]
+
+Old version linked 3 items across 5 test topics. New version links 13. The difference is hybrid similarity - combining character-trigram and token-level matching at a tuned threshold.
+
+Cross-platform convergence is the strongest signal that something actually matters. Not engagement on one platform. Convergence across all of them.
+
+---
+
+## Post 4: 15-WAY BLINDED EVALUATION.
+
+I don't trust vibes for measuring quality. So I ran a scientific comparison.
+
+5 topics x 3 versions. Stripped version labels. Randomized as A/B/C. Scored on groundedness, specificity, coverage, actionability, and format.
+
+v2.5: 4.38/5.0
+v2.2 (HN only): 4.10/5.0
+v2.0 (original): 3.73/5.0
+
+Won all 5 topics. Zero regressions. Biggest gains: specificity (+0.8) and format (+1.0) from cross-source linking giving the synthesis better material to work with.
+
+---
+
+## Post 5: Demo - Anthropic Odds
+
+Asked it about Anthropic odds.
+
+11 Polymarket markets. 25 X posts. 13 YouTube videos (719K views). 6 HN stories (471 points).
+
+Best AI model February: 98%. IPO before OpenAI: 64%. $500B+ valuation: 87%. FrontierMath 50% score: 48% (up 28% today). Pentagon ban: only 22%.
+
+Markets say Anthropic is winning the model race AND the valuation race. The Pentagon thing is noise.
+
+---
+
+## Post 6: Demo - Arizona Basketball
+
+"/last30days arizona basketball"
+
+6 Polymarket markets. 37 X posts (4,200 likes). 15 YouTube videos (517K views). 2 Reddit threads.
+
+Championship odds: 13%. #1 seed: 88%. Big 12 title: Arizona leads by 2.
+
+That's not a sports blog. That's Reddit reactions + X engagement + YouTube analysis + prediction market odds from one command.
+
+The Polymarket integration uses two-pass query expansion. First pass finds "Arizona Big 12." Second pass discovers the championship and #1 seed markets via tag-based domain bridging.
+
+---
+
+## Post 7: Demo - Iran War
+
+The best Polymarket demo is news.
+
+"/last30days iran war"
+
+14 Polymarket markets. $425M+ in volume. 7 Reddit threads. 30 X posts. 20 YouTube videos (2M views). 18 HN stories (1,187 points).
+
+US strikes Iran by 2026: 70%. War Powers by March: 60%. Israel strikes by June: 64%. Formal war declaration: only 8%.
+
+Markets say: limited strikes with War Powers, NOT a declared war. Breaking Points (435K views) covered leaked Pentagon opposition. r/Conservative "imploding" per r/SubredditDrama.
+
+One command. Six sources. Real money.
+
+---
+
+## Post 8: Credits + CTA
+
+Also in v2.5: YouTube synonym expansion ("hip hop" now matches "rap" - relevance jumped 0.33 to 0.71), X handle resolution, and HN OR queries for framework topics.
+
+The difference between "good research" and "research you'd actually trust" is in details like this.
+
+Try it: /last30days [any topic]
+
+github.com/mvanhorn/last30days-skill
+
+---
+
+## Post 9: Demo - Claude Code (ALL 6 sources)
+
+"/last30days Claude Code"
+
+3 Reddit threads (199 upvotes). 35 X posts (5,239 likes). 15 YouTube videos (1.4M views, 5 transcripts). 30 HN stories (~8,500 points). 8 Polymarket markets. 20 web pages.
+
+All six sources hit. Top finding: the planning-first workflow has won. The #1 HN post this month (969 pts, 590 comments) is about separating planning from execution. Boris Cherny (Head of Claude Code) on Lenny's Podcast: "100% of my code is written by Claude Code - I have not edited a single line by hand since November."
+
+Polymarket: Anthropic 99% for best AI model in February. 58% for March. Claude on FrontierMath at 55%. The US government rejected Claude - Polymarket has the Hegseth ban at 32%.
+
+Then I asked it to dig deeper into the planning-first workflow. No new searches - it answered from what it already learned.
+
+---
+
+## Post 10: Demo - March Madness Odds (Polymarket + Sports)
+
+"/last30days March Madness Odds"
+
+2 Reddit threads. 31 X posts. 6 YouTube videos (46K views, 4 transcripts). 2 Polymarket markets.
+
+Tournament winner: Duke 18%, Michigan 18%, Arizona 13%. #1 seeds: Michigan 98%, Duke 91%, Arizona 88%.
+
+Duke is the hottest mover - went from +700 to +450 in one week. The skill surfaced that from sportsbook data, X commentary, and Polymarket odds simultaneously.
+
+Then I asked it to break down Michigan vs Duke vs Arizona. Full analysis from the research it already had.
+
+---
+
+## Post 11: Demo - Seedance Prompting (Expert + Prompt Mode)
+
+"/last30days Seedance prompting"
+
+13 Reddit threads. 33 X posts. 20 YouTube videos (1.2M views, 4 transcripts). 1 HN story. 15 web pages.
+
+Top finding: Seedance 2.0 prompts follow a director's shot-list format, not freeform text. 30-100 words. Subject + Action + Camera + Scene + Style + Constraints. Beyond 100 words, results degrade.
+
+Then I said: "a cinematic drone shot over a city at golden hour"
+
+It wrote me a copy-paste prompt using the exact patterns from the research. That's the skill's real power - research first, then create from what you learned.
+
+---
+
+## Post 12: Thank You + CTA (Final)
+
+Thank you to ARJ999 and wkbaran on GitHub who kept asking for Hacker News support. Three separate issues. v2.5 delivers.
+
+30 days. 3.2k stars. 6 sources. Massively better results.
+
+Try it: /last30days [any topic]
+
+github.com/mvanhorn/last30days-skill
+
+---
+
+## Post 13: Demo - Anthropic vs Pentagon (Breaking News + Polymarket)
+
+"/last30days Anthropic Pete Hegseth"
+
+14 Reddit threads. 29 X posts (11,559 likes). 20 YouTube videos (739K views, 5 transcripts). 5 HN stories. 9 Polymarket markets. 10 web pages.
+
+This story broke TODAY. Defense Secretary Hegseth designated Anthropic a "supply chain risk" - believed to be the first time an American company has ever received this designation. Trump ordered every federal agency to stop using Anthropic tech.
+
+Polymarket: Anthropic still 99% for best AI model. $500B+ valuation: 68%. IPO >$600B: 97%. Hegseth out by March 31: only 6%.
+
+Markets say: Anthropic wins the model race regardless. Bettors don't think Hegseth survives this. That's the kind of signal you can't get from opinion threads.
+
+---
+
+## Post 14: Demo - OpenAI Insider Trading (News + Polymarket)
+
+"/last30days OpenAI Insider Trading"
+
+2 Reddit threads. 29 X posts. 4 YouTube videos (360K views, 4 transcripts). 2 HN stories. 15 Polymarket markets. 15 web pages.
+
+An OpenAI employee was just fired for using confidential info to bet on Polymarket. 13 brand-new wallets appeared 40 hours before the browser launch. $309K bet on the right outcome. Unusual Whales flagged 77 suspected insider positions across 60 wallets.
+
+Meanwhile Polymarket has OpenAI's IPO at $1.25-1.5T: 54%. Anthropic IPOs first: 62%. Best AI model: Anthropic 99%.
+
+The prediction markets are both the story AND the source. One command pulled all of it together.
+
+---
+
+## Standalone Tweet: Polymarket Stats Line
+
+"/last30days Anthropic Pete Hegseth"
+
+The Pentagon just designated Anthropic a supply chain risk. First time ever for an American company. Trump ordered every agency to stop using their tech.
+
+Here's what Polymarket says:
+
+📊 9 markets │ Best AI model: 99% │ $500B+ valuation: 68% │ IPO >$600B: 97% │ Hegseth out by March: 6%
+
+Bettors with real money on the line think Anthropic wins the model race, goes public at a massive valuation, and Hegseth doesn't survive this.
+
+That's the gap between headlines and reality. One command, six sources.
+
+github.com/mvanhorn/last30days-skill
+
+---
+
+## Recommended Thread Order (pick 8-10)
+
+The full thread above is 12 posts. Here's what I'd cut to keep it tight:
+
+**Must include (core story):**
+1. Post 1 - Lead announcement
+2. Post 2 - Polymarket ("Reddit tells you what people think...")
+3. Post 3 - Cross-source linking
+4. Post 4 - Blinded evaluation
+
+**Best demos (pick 3-4):**
+- Post 13 (Anthropic vs Pentagon) - STRONGEST. Breaking news today. Polymarket cuts through the noise. "Markets say Anthropic wins regardless."
+- Post 9 (Claude Code) - All 6 sources. Massive numbers. Shows follow-up flow.
+- Post 10 (March Madness) - Sports/Polymarket crossover. Timely with tournament approaching.
+- Post 11 (Seedance) - Shows prompting flow. 1.2M YouTube views.
+- Post 5 (Anthropic Odds) - Overlaps with Post 13 now. Skip.
+
+**Skip or save for standalone tweets:**
+- Post 5 (Anthropic Odds) - Redundant with Post 13
+- Post 6 (Arizona Basketball) - Covered by March Madness now
+- Post 7 (Iran War) - Great standalone tweet, not for launch thread
+- Post 8 (Credits/minor features) - Fold into CTA
+
+**My recommended 8-post thread:**
+1. Lead (Post 1)
+2. Polymarket (Post 2)
+3. Cross-source linking (Post 3)
+4. Blinded evaluation (Post 4)
+5. Demo: Anthropic vs Pentagon (Post 13) - breaking news, best Polymarket showcase
+6. Demo: Claude Code (Post 9)
+7. Demo: March Madness (Post 10)
+8. Thank you + CTA (Post 12)
+
+---
+
+## Video Script (~60 seconds)
+
+**[Talking to camera]**
+
+Oh my god, I can't believe it's been 30 days since I launched last30days. 3,200 stars on GitHub. This has been the craziest month.
+
+Today I'm shipping v2.5 and I'm really excited about this one. Three big things.
+
+**[Screen recording: typing /last30days Anthropic Pete Hegseth]**
+
+First - Polymarket prediction markets as a 6th source. So this Anthropic-Pentagon story broke today. Hegseth designated Anthropic a supply chain risk, Trump ordered agencies to stop using their tech. Scary headline, right?
+
+But Polymarket says: Anthropic still 99% for best AI model. IPO above 600 billion: 97%. Hegseth out by March: 6%. Real money on outcomes helps you predict the future. That's a different story than the headlines.
+
+**[Screen recording: typing /last30days arizona basketball]**
+
+Second - it now searches Hacker News and does cross-source linking. When the same story shows up on Reddit AND YouTube AND HN, it flags it. I ran a 15-way blinded comparison and v2.5 scored 4.38 versus 3.73 for the original. Won all 5 test topics.
+
+**[Back to camera]**
+
+Thank you to everyone who starred it, filed issues, and kept pushing me to make this better. Shoutout to the people on GitHub who literally filed three separate issues asking for Hacker News. v2.5 delivers.
+
+Link in bio. Try it on anything.
+
+---
+
+## Scoring Note
+
+The 4.38 vs 3.73 score is from a custom 5-dimension rubric (30% groundedness, 25% specificity, 20% coverage, 15% actionability, 10% format compliance) evaluated by Claude on blinded outputs. The relative ranking is meaningful; the absolute numbers are not. It's an LLM grading LLM output - useful for A/B comparison, not for claiming "4.38 out of 5 quality."
+
+If using the score in a tweet, frame it as "scored X vs Y on a blinded comparison" not "rated 4.38/5.0 quality" - the former is honest, the latter implies an objective standard that doesn't exist.

fixtures/eval_topics.json

@@ -0,0 +1,42 @@
+[
+  {
+    "topic": "OpenClaw vs NanoClaw vs ZeroClaw",
+    "query_type": "comparison",
+    "rationale": "Multi-entity extraction, 3-way split across AI agent frameworks."
+  },
+  {
+    "topic": "how to set up a GLP-1 supplement routine",
+    "query_type": "how_to",
+    "rationale": "Trending health topic. Tests non-tech how_to."
+  },
+  {
+    "topic": "2026 March Madness",
+    "query_type": "breaking_news",
+    "rationale": "Live sporting event. Tests broad breaking news recall."
+  },
+  {
+    "topic": "best budget noise cancelling headphones 2026",
+    "query_type": "product",
+    "rationale": "Evergreen consumer query. Tests product review aggregation."
+  },
+  {
+    "topic": "thoughts on OpenAI Codex pricing",
+    "query_type": "opinion",
+    "rationale": "Active developer debate. Tests opinion mining."
+  },
+  {
+    "topic": "odds of US recession 2026",
+    "query_type": "prediction",
+    "rationale": "Major macro topic. Tests prediction market + news synthesis."
+  },
+  {
+    "topic": "what is retrieval augmented generation",
+    "query_type": "concept",
+    "rationale": "Widely discussed AI concept. Tests explanation quality."
+  },
+  {
+    "topic": "Google Wiz acquisition price and timeline",
+    "query_type": "factual",
+    "rationale": "Completed event ($32B). Tests factual precision."
+  }
+]

fixtures/tiktok_search.json

@@ -0,0 +1,58 @@
+{
+  "items": [
+    {
+      "video_id": "7543693751290481942",
+      "text": "This Claude Code trick saved me hours #claudecode #ai #coding",
+      "url": "https://www.tiktok.com/@codemaster/video/7543693751290481942",
+      "author_name": "codemaster",
+      "date": "2026-02-28",
+      "engagement": {
+        "views": 2100000,
+        "likes": 45000,
+        "comments": 1200,
+        "shares": 8400
+      },
+      "hashtags": ["claudecode", "ai", "coding"],
+      "duration": 45,
+      "relevance": 0.85,
+      "why_relevant": "TikTok: This Claude Code trick saved me hours #claude",
+      "caption_snippet": "So I found this insane trick with Claude Code where you can use slash commands to automate everything"
+    },
+    {
+      "video_id": "7543100200112345678",
+      "text": "AI coding tools comparison 2026 - Claude vs Copilot vs Cursor #ai #devtools",
+      "url": "https://www.tiktok.com/@techreviewer/video/7543100200112345678",
+      "author_name": "techreviewer",
+      "date": "2026-02-25",
+      "engagement": {
+        "views": 850000,
+        "likes": 22000,
+        "comments": 890,
+        "shares": 3200
+      },
+      "hashtags": ["ai", "devtools"],
+      "duration": 60,
+      "relevance": 0.7,
+      "why_relevant": "TikTok: AI coding tools comparison 2026 - Claude vs Copi",
+      "caption_snippet": ""
+    },
+    {
+      "video_id": "7543200300223456789",
+      "text": "You need to try Claude Code RIGHT NOW #programming #tips",
+      "url": "https://www.tiktok.com/@devtips/video/7543200300223456789",
+      "author_name": "devtips",
+      "date": "2026-03-01",
+      "engagement": {
+        "views": 500000,
+        "likes": 15000,
+        "comments": 450,
+        "shares": 2100
+      },
+      "hashtags": ["programming", "tips"],
+      "duration": 30,
+      "relevance": 0.6,
+      "why_relevant": "TikTok: You need to try Claude Code RIGHT NOW #programm",
+      "caption_snippet": "Let me show you why Claude Code is the best AI coding tool right now"
+    }
+  ]
+}

gemini-extension.json

@@ -0,0 +1,67 @@
+{
+  "name": "last30days-skill",
+  "version": "3.0.5",
+  "description": "Research a topic from the last 30 days across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the web.",
+  "settings": [
+    {
+      "name": "Extension Directory",
+      "description": "Extension installation directory (auto-set by Gemini CLI)",
+      "envVar": "GEMINI_EXTENSION_DIR",
+      "sensitive": false
+    },
+    {
+      "name": "ScrapeCreators API Key",
+      "description": "ScrapeCreators API Key for Reddit, TikTok, and Instagram search (required)",
+      "envVar": "SCRAPECREATORS_API_KEY",
+      "sensitive": true
+    },
+    {
+      "name": "OpenAI API Key",
+      "description": "OpenAI API Key - optional fallback for Reddit discovery",
+      "envVar": "OPENAI_API_KEY",
+      "sensitive": true
+    },
+    {
+      "name": "xAI API Key",
+      "description": "xAI API Key for X/Twitter search (optional)",
+      "envVar": "XAI_API_KEY",
+      "sensitive": true
+    },
+    {
+      "name": "OpenRouter API Key",
+      "description": "OpenRouter API Key (optional)",
+      "envVar": "OPENROUTER_API_KEY",
+      "sensitive": true
+    },
+    {
+      "name": "Parallel AI API Key",
+      "description": "Parallel AI API Key (optional)",
+      "envVar": "PARALLEL_API_KEY",
+      "sensitive": true
+    },
+    {
+      "name": "Brave Search API Key",
+      "description": "Brave Search API Key (optional)",
+      "envVar": "BRAVE_API_KEY",
+      "sensitive": true
+    },
+    {
+      "name": "Apify API Token",
+      "description": "Apify API Token (optional legacy)",
+      "envVar": "APIFY_API_TOKEN",
+      "sensitive": true
+    },
+    {
+      "name": "Twitter AUTH_TOKEN",
+      "description": "Twitter browser AUTH_TOKEN cookie for direct X search (optional)",
+      "envVar": "AUTH_TOKEN",
+      "sensitive": true
+    },
+    {
+      "name": "Twitter CT0",
+      "description": "Twitter browser CT0 cookie (optional, pair with AUTH_TOKEN)",
+      "envVar": "CT0",
+      "sensitive": true
+    }
+  ]
+}

hooks/hooks.json

@@ -0,0 +1,16 @@
+{
+  "hooks": {
+    "SessionStart": [
+      {
+        "matcher": "",
+        "hooks": [
+          {
+            "type": "command",
+            "command": "bash ${CLAUDE_PLUGIN_ROOT}/hooks/scripts/check-config.sh",
+            "timeout": 5
+          }
+        ]
+      }
+    ]
+  }
+}

hooks/scripts/check-config.sh

@@ -0,0 +1,112 @@
+#!/bin/bash
+set -euo pipefail
+
+# Check last30days configuration status and show appropriate welcome message.
+# Priority: .claude/last30days.env > ~/.config/last30days/.env > env vars
+
+PROJECT_ENV=".claude/last30days.env"
+GLOBAL_ENV="$HOME/.config/last30days/.env"
+
+# Helper: warn if file permissions are too open
+check_perms() {
+  local file="$1"
+  if [[ ! -f "$file" ]]; then return; fi
+  local perms
+  # Try GNU stat first (Linux), fall back to BSD stat (macOS).
+  # On Linux, `stat -f` prints filesystem info (not permissions) and exits 0,
+  # so the previous BSD-first ordering left $perms as multi-line garbage on
+  # every Linux session start and printed a false WARNING.
+  perms=$(stat -c '%a' "$file" 2>/dev/null || stat -f '%Lp' "$file" 2>/dev/null || echo "")
+  if [[ -n "$perms" && "$perms" != "600" && "$perms" != "400" ]]; then
+    echo "/last30days: WARNING — $file has permissions $perms (should be 600)."
+    echo "  Fix: chmod 600 $file"
+  fi
+}
+
+# Load env file into variables for inspection (without exporting)
+load_env_vars() {
+  local file="$1"
+  if [[ -f "$file" ]]; then
+    while IFS='=' read -r key value; do
+      # Skip comments, empty lines
+      [[ "$key" =~ ^[[:space:]]*# ]] && continue
+      [[ -z "$key" ]] && continue
+      key=$(echo "$key" | xargs)
+      value=$(echo "$value" | xargs | sed 's/^["'\''"]//;s/["'\''"]$//')
+      if [[ -n "$key" && -n "$value" ]]; then
+        eval "ENV_${key}=\"${value}\""
+      fi
+    done < "$file"
+  fi
+}
+
+# Determine which config file is active
+CONFIG_FILE=""
+if [[ -f "$PROJECT_ENV" ]]; then
+  CONFIG_FILE="$PROJECT_ENV"
+  check_perms "$PROJECT_ENV"
+elif [[ -f "$GLOBAL_ENV" ]]; then
+  CONFIG_FILE="$GLOBAL_ENV"
+  check_perms "$GLOBAL_ENV"
+fi
+
+# Load config if found
+if [[ -n "$CONFIG_FILE" ]]; then
+  load_env_vars "$CONFIG_FILE"
+fi
+
+# Check SETUP_COMPLETE (from file or env)
+SETUP_COMPLETE="${ENV_SETUP_COMPLETE:-${SETUP_COMPLETE:-}}"
+
+# If setup has never been run, show welcome message for new users
+if [[ -z "$SETUP_COMPLETE" && -z "$CONFIG_FILE" && -z "${OPENAI_API_KEY:-}" && -z "${SCRAPECREATORS_API_KEY:-}" && -z "${AUTH_TOKEN:-}" && -z "${XAI_API_KEY:-}" ]]; then
+  cat <<'EOF'
+/last30days: Ready to use. Run /last30days to get started — setup takes 30 seconds.
+
+Reddit, Hacker News, and Polymarket work out of the box.
+The setup wizard can unlock X/Twitter, YouTube, and more.
+EOF
+  exit 0
+fi
+
+# Setup done but check for ScrapeCreators
+HAS_SCRAPECREATORS="${ENV_SCRAPECREATORS_API_KEY:-${SCRAPECREATORS_API_KEY:-}}"
+HAS_X="${ENV_AUTH_TOKEN:-${AUTH_TOKEN:-}}"
+HAS_XAI="${ENV_XAI_API_KEY:-${XAI_API_KEY:-}}"
+HAS_YTDLP=""
+if command -v yt-dlp &>/dev/null; then
+  HAS_YTDLP="yes"
+fi
+HAS_BSKY="${ENV_BSKY_HANDLE:-${BSKY_HANDLE:-}}"
+HAS_EXA="${ENV_EXA_API_KEY:-${EXA_API_KEY:-}}"
+
+# Count active sources
+SOURCE_COUNT=2  # HN + Polymarket are always free
+if [[ -n "$HAS_X" || -n "$HAS_XAI" ]]; then
+  SOURCE_COUNT=$((SOURCE_COUNT + 1))
+fi
+# Reddit public JSON always works
+SOURCE_COUNT=$((SOURCE_COUNT + 1))
+if [[ -n "$HAS_YTDLP" ]]; then
+  SOURCE_COUNT=$((SOURCE_COUNT + 1))
+fi
+if [[ -n "$HAS_EXA" ]]; then
+  SOURCE_COUNT=$((SOURCE_COUNT + 1))
+fi
+if [[ -n "$HAS_BSKY" ]]; then
+  SOURCE_COUNT=$((SOURCE_COUNT + 1))
+fi
+if [[ -n "$HAS_SCRAPECREATORS" ]]; then
+  SOURCE_COUNT=$((SOURCE_COUNT + 3))  # Reddit comments + TikTok + Instagram
+fi
+
+if [[ -n "$HAS_SCRAPECREATORS" ]]; then
+  # Fully configured — compact ready message
+  echo "/last30days: Ready — ${SOURCE_COUNT} sources active."
+else
+  # Setup done but missing ScrapeCreators — recommend it
+  echo "/last30days: Ready — ${SOURCE_COUNT} sources active."
+  echo "  Tip: Add ScrapeCreators for Reddit comments + TikTok + Instagram."
+  echo "  10,000 free API calls, no credit card — scrapecreators.com"
+  echo "  last30days has no affiliation with any API provider."
+fi

marketing/v3.1-launch/README.md

@@ -0,0 +1,37 @@
+# /last30days v3.1 Launch Video
+
+30-second Remotion-rendered MP4 announcing **v3.1: Competitors mode** for posting on X.
+
+## Quick start
+
+```bash
+cd marketing/v3.1-launch
+npm install            # one-time, ~200MB of node_modules
+npm run preview        # opens Remotion Studio at localhost:3000 to scrub frames
+npm run render         # writes out/last30days-v3.1-launch.mp4 (high quality, CRF 18)
+npm run render:fast    # writes a CRF 23 preview for fast iteration
+```
+
+## Specs
+
+- 1920×1080, 30fps, 30 seconds (900 frames)
+- H.264 / MP4
+- Silent (autoplay-muted-friendly; captions baked in)
+- Marketing label `v3.1` (engine code version stays 3.0.14)
+
+## Scene timing (single source of truth: `src/lib/timing.ts`)
+
+| Scene | Frames | Time | What |
+|-------|--------|------|------|
+| 1. Hook | 0-89 | 0.0-3.0s | Badge animates in + "What if one search ran 3 at once?" |
+| 2. Old way | 90-239 | 3.0-8.0s | Single terminal: `/last30days OpenAI` |
+| 3. Fan-out | 240-419 | 8.0-14.0s | `--competitors` types in → splits into 3 panes |
+| 4. Comparison | 420-629 | 14.0-21.0s | 3 panes collapse into Head-to-Head table |
+| 5. How | 630-779 | 21.0-26.0s | 3-line text card |
+| 6. CTA | 780-899 | 26.0-30.0s | Install command + repo URL |
+
+Edit `src/lib/timing.ts` to retime scenes; the `LaunchVideo` composition reads from there.
+
+## Output
+
+Rendered MP4 lives at `out/last30days-v3.1-launch.mp4` (gitignored). Upload directly to X.

marketing/v3.1-launch/package.json

@@ -0,0 +1,23 @@
+{
+  "name": "last30days-v3-1-launch-video",
+  "version": "0.1.0",
+  "private": true,
+  "description": "30s Remotion launch video for /last30days v3.1 (competitors mode).",
+  "scripts": {
+    "preview": "remotion studio src/index.ts",
+    "render": "remotion render src/index.ts LaunchVideo out/last30days-v3.1-launch.mp4 --codec=h264 --crf=18",
+    "render:fast": "remotion render src/index.ts LaunchVideo out/last30days-v3.1-launch-preview.mp4 --codec=h264 --crf=23"
+  },
+  "dependencies": {
+    "react": "19.0.0",
+    "react-dom": "19.0.0",
+    "remotion": "4.0.250",
+    "@remotion/cli": "4.0.250",
+    "@remotion/google-fonts": "4.0.250"
+  },
+  "devDependencies": {
+    "@types/react": "19.0.0",
+    "@types/node": "22.10.0",
+    "typescript": "5.6.3"
+  }
+}

marketing/v3.1-launch/remotion.config.ts

@@ -0,0 +1,6 @@
+import { Config } from "@remotion/cli/config";
+
+Config.setVideoImageFormat("jpeg");
+Config.setOverwriteOutput(true);
+Config.setConcurrency(null);
+Config.setCodec("h264");

marketing/v3.1-launch/src/LaunchVideo.tsx

@@ -0,0 +1,35 @@
+import React from "react";
+import { AbsoluteFill, Sequence } from "remotion";
+import { SCENES } from "./lib/timing";
+import { Scene1Hook } from "./scenes/Scene1Hook";
+import { Scene2OldWay } from "./scenes/Scene2OldWay";
+import { Scene3FanOut } from "./scenes/Scene3FanOut";
+import { Scene4Comparison } from "./scenes/Scene4Comparison";
+import { Scene5HowItWorks } from "./scenes/Scene5HowItWorks";
+import { Scene6CTA } from "./scenes/Scene6CTA";
+import { COLORS } from "./lib/colors";
+
+export const LaunchVideo: React.FC = () => {
+  return (
+    <AbsoluteFill style={{ background: COLORS.bgDeep }}>
+      <Sequence from={SCENES.hook.from} durationInFrames={SCENES.hook.durationInFrames}>
+        <Scene1Hook />
+      </Sequence>
+      <Sequence from={SCENES.oldWay.from} durationInFrames={SCENES.oldWay.durationInFrames}>
+        <Scene2OldWay />
+      </Sequence>
+      <Sequence from={SCENES.fanOut.from} durationInFrames={SCENES.fanOut.durationInFrames}>
+        <Scene3FanOut />
+      </Sequence>
+      <Sequence from={SCENES.comparison.from} durationInFrames={SCENES.comparison.durationInFrames}>
+        <Scene4Comparison />
+      </Sequence>
+      <Sequence from={SCENES.howItWorks.from} durationInFrames={SCENES.howItWorks.durationInFrames}>
+        <Scene5HowItWorks />
+      </Sequence>
+      <Sequence from={SCENES.cta.from} durationInFrames={SCENES.cta.durationInFrames}>
+        <Scene6CTA />
+      </Sequence>
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/src/Root.tsx

@@ -0,0 +1,19 @@
+import React from "react";
+import { Composition } from "remotion";
+import { LaunchVideo } from "./LaunchVideo";
+import { FPS, TOTAL_FRAMES } from "./lib/timing";
+
+export const RemotionRoot: React.FC = () => {
+  return (
+    <>
+      <Composition
+        id="LaunchVideo"
+        component={LaunchVideo}
+        durationInFrames={TOTAL_FRAMES}
+        fps={FPS}
+        width={1920}
+        height={1080}
+      />
+    </>
+  );
+};

marketing/v3.1-launch/src/components/BadgeBar.tsx

@@ -0,0 +1,68 @@
+import React from "react";
+import { spring, useCurrentFrame, useVideoConfig } from "remotion";
+import { COLORS, FONT_MONO } from "../lib/colors";
+
+type Props = {
+  startFrame?: number;
+  size?: "small" | "large";
+};
+
+export const BadgeBar: React.FC<Props> = ({ startFrame = 0, size = "large" }) => {
+  const frame = useCurrentFrame();
+  const { fps } = useVideoConfig();
+  const elapsed = Math.max(0, frame - startFrame);
+
+  const scale = spring({
+    frame: elapsed,
+    fps,
+    config: { damping: 12, stiffness: 90 },
+    from: 0.85,
+    to: 1,
+  });
+  const opacity = spring({
+    frame: elapsed,
+    fps,
+    config: { damping: 20 },
+    from: 0,
+    to: 1,
+  });
+
+  const fontSize = size === "large" ? 56 : 28;
+
+  return (
+    <div
+      style={{
+        display: "flex",
+        alignItems: "center",
+        justifyContent: "center",
+        transform: `scale(${scale})`,
+        opacity,
+        fontFamily: FONT_MONO,
+        fontSize,
+        color: COLORS.fgPrimary,
+        letterSpacing: 0.5,
+      }}
+    >
+      <span style={{ fontSize: fontSize * 1.1, marginRight: 16 }}>🌐</span>
+      <span>last30days</span>
+      <span
+        style={{
+          marginLeft: 14,
+          color: COLORS.accentCyan,
+          fontWeight: 600,
+        }}
+      >
+        v3.1
+      </span>
+      <span
+        style={{
+          marginLeft: 16,
+          color: COLORS.fgDim,
+          fontSize: fontSize * 0.55,
+        }}
+      >
+        · synced 2026-04-22
+      </span>
+    </div>
+  );
+};

marketing/v3.1-launch/src/components/ComparisonTable.tsx

@@ -0,0 +1,135 @@
+import React from "react";
+import { interpolate, useCurrentFrame } from "remotion";
+import { COLORS, FONT_MONO } from "../lib/colors";
+
+type Row = {
+  dimension: string;
+  cells: [string, string, string];
+};
+
+type Props = {
+  startFrame: number;
+  entities: [string, string, string];
+  rows: Row[];
+  rowStaggerFrames?: number;
+};
+
+export const ComparisonTable: React.FC<Props> = ({
+  startFrame,
+  entities,
+  rows,
+  rowStaggerFrames = 18,
+}) => {
+  const frame = useCurrentFrame();
+
+  const headerOpacity = interpolate(
+    frame - startFrame,
+    [0, 12],
+    [0, 1],
+    { extrapolateLeft: "clamp", extrapolateRight: "clamp" },
+  );
+
+  const colTemplate = "1.4fr 1fr 1fr 1fr";
+  const cellPad = "16px 22px";
+
+  return (
+    <div
+      style={{
+        width: "100%",
+        background: COLORS.bgPanel,
+        borderRadius: 16,
+        border: `1px solid ${COLORS.border}`,
+        overflow: "hidden",
+        fontFamily: FONT_MONO,
+        boxShadow: "0 24px 60px rgba(0,0,0,0.6)",
+      }}
+    >
+      <div
+        style={{
+          display: "grid",
+          gridTemplateColumns: colTemplate,
+          background: COLORS.bgPanelSoft,
+          borderBottom: `1px solid ${COLORS.border}`,
+          opacity: headerOpacity,
+        }}
+      >
+        <div
+          style={{
+            padding: cellPad,
+            color: COLORS.fgMuted,
+            fontSize: 22,
+            fontWeight: 500,
+          }}
+        >
+          Dimension
+        </div>
+        {entities.map((entity, idx) => (
+          <div
+            key={entity}
+            style={{
+              padding: cellPad,
+              color: idx === 0 ? COLORS.accentCyan : COLORS.fgPrimary,
+              fontSize: 26,
+              fontWeight: 600,
+              borderLeft: `1px solid ${COLORS.border}`,
+            }}
+          >
+            {entity}
+          </div>
+        ))}
+      </div>
+      {rows.map((row, idx) => {
+        const rowStart = startFrame + 12 + idx * rowStaggerFrames;
+        const rowOpacity = interpolate(
+          frame - rowStart,
+          [0, 14],
+          [0, 1],
+          { extrapolateLeft: "clamp", extrapolateRight: "clamp" },
+        );
+        const rowSlide = interpolate(
+          frame - rowStart,
+          [0, 14],
+          [12, 0],
+          { extrapolateLeft: "clamp", extrapolateRight: "clamp" },
+        );
+        return (
+          <div
+            key={row.dimension}
+            style={{
+              display: "grid",
+              gridTemplateColumns: colTemplate,
+              borderBottom:
+                idx === rows.length - 1 ? "none" : `1px solid ${COLORS.border}`,
+              opacity: rowOpacity,
+              transform: `translateY(${rowSlide}px)`,
+            }}
+          >
+            <div
+              style={{
+                padding: cellPad,
+                color: COLORS.fgMuted,
+                fontSize: 22,
+              }}
+            >
+              {row.dimension}
+            </div>
+            {row.cells.map((cell, cellIdx) => (
+              <div
+                key={cellIdx}
+                style={{
+                  padding: cellPad,
+                  color: COLORS.fgPrimary,
+                  fontSize: 22,
+                  borderLeft: `1px solid ${COLORS.border}`,
+                  lineHeight: 1.35,
+                }}
+              >
+                {cell}
+              </div>
+            ))}
+          </div>
+        );
+      })}
+    </div>
+  );
+};

marketing/v3.1-launch/src/components/TerminalWindow.tsx

@@ -0,0 +1,95 @@
+import React from "react";
+import { COLORS, FONT_MONO } from "../lib/colors";
+
+type Props = {
+  title?: string;
+  width?: number | string;
+  height?: number | string;
+  children?: React.ReactNode;
+  glow?: boolean;
+};
+
+export const TerminalWindow: React.FC<Props> = ({
+  title = "/last30days",
+  width = "100%",
+  height = "100%",
+  children,
+  glow = false,
+}) => {
+  return (
+    <div
+      style={{
+        width,
+        height,
+        background: COLORS.bgPanel,
+        borderRadius: 16,
+        border: `1px solid ${COLORS.border}`,
+        boxShadow: glow
+          ? `0 0 60px ${COLORS.accentCyan}33, 0 24px 60px rgba(0,0,0,0.6)`
+          : "0 24px 60px rgba(0,0,0,0.6)",
+        overflow: "hidden",
+        display: "flex",
+        flexDirection: "column",
+        fontFamily: FONT_MONO,
+      }}
+    >
+      <div
+        style={{
+          height: 36,
+          background: COLORS.bgPanelSoft,
+          borderBottom: `1px solid ${COLORS.border}`,
+          display: "flex",
+          alignItems: "center",
+          padding: "0 16px",
+          gap: 8,
+        }}
+      >
+        <span
+          style={{
+            width: 12,
+            height: 12,
+            borderRadius: 12,
+            background: COLORS.trafficRed,
+          }}
+        />
+        <span
+          style={{
+            width: 12,
+            height: 12,
+            borderRadius: 12,
+            background: COLORS.trafficYellow,
+          }}
+        />
+        <span
+          style={{
+            width: 12,
+            height: 12,
+            borderRadius: 12,
+            background: COLORS.trafficGreen,
+          }}
+        />
+        <span
+          style={{
+            marginLeft: 16,
+            color: COLORS.fgMuted,
+            fontSize: 14,
+            fontFamily: FONT_MONO,
+            letterSpacing: 0.5,
+          }}
+        >
+          {title}
+        </span>
+      </div>
+      <div
+        style={{
+          flex: 1,
+          padding: "20px 28px",
+          color: COLORS.fgPrimary,
+          overflow: "hidden",
+        }}
+      >
+        {children}
+      </div>
+    </div>
+  );
+};

marketing/v3.1-launch/src/components/TypedLine.tsx

@@ -0,0 +1,71 @@
+import React from "react";
+import { interpolate, useCurrentFrame } from "remotion";
+import { COLORS, FONT_MONO } from "../lib/colors";
+
+type Props = {
+  text: string;
+  startFrame: number;
+  charsPerSec?: number;
+  fontSize?: number;
+  color?: string;
+  prefix?: string;
+  prefixColor?: string;
+  showCursor?: boolean;
+  fps?: number;
+};
+
+export const TypedLine: React.FC<Props> = ({
+  text,
+  startFrame,
+  charsPerSec = 28,
+  fontSize = 32,
+  color = COLORS.fgPrimary,
+  prefix,
+  prefixColor = COLORS.accentGreen,
+  showCursor = true,
+  fps = 30,
+}) => {
+  const frame = useCurrentFrame();
+  const elapsed = Math.max(0, frame - startFrame);
+  const totalChars = text.length;
+  const lengthFrames = Math.ceil((totalChars / charsPerSec) * fps);
+  const visibleChars = Math.round(
+    interpolate(elapsed, [0, lengthFrames], [0, totalChars], {
+      extrapolateLeft: "clamp",
+      extrapolateRight: "clamp",
+    }),
+  );
+  const visible = text.slice(0, visibleChars);
+  const done = visibleChars >= totalChars;
+  const cursorOn = showCursor && Math.floor(frame / 15) % 2 === 0;
+
+  return (
+    <div
+      style={{
+        fontFamily: FONT_MONO,
+        fontSize,
+        color,
+        whiteSpace: "pre",
+        lineHeight: 1.4,
+      }}
+    >
+      {prefix ? (
+        <span style={{ color: prefixColor, marginRight: 12 }}>{prefix}</span>
+      ) : null}
+      <span>{visible}</span>
+      {(!done || cursorOn) && (
+        <span
+          style={{
+            display: "inline-block",
+            width: fontSize * 0.55,
+            height: fontSize * 0.95,
+            background: color,
+            verticalAlign: "text-bottom",
+            marginLeft: 2,
+            opacity: cursorOn ? 0.85 : 0,
+          }}
+        />
+      )}
+    </div>
+  );
+};

marketing/v3.1-launch/src/index.ts

@@ -0,0 +1,4 @@
+import { registerRoot } from "remotion";
+import { RemotionRoot } from "./Root";
+
+registerRoot(RemotionRoot);

marketing/v3.1-launch/src/lib/colors.ts

@@ -0,0 +1,20 @@
+export const COLORS = {
+  bgDeep: "#0a0e14",
+  bgPanel: "#11161d",
+  bgPanelSoft: "#161c25",
+  fgPrimary: "#e6e6e6",
+  fgMuted: "#8a93a3",
+  fgDim: "#5b6573",
+  border: "#2a3340",
+  accentCyan: "#36d6f7",
+  accentMagenta: "#ff55a3",
+  accentGreen: "#5fff9f",
+  accentAmber: "#ffc857",
+  trafficRed: "#ff5f57",
+  trafficYellow: "#febc2e",
+  trafficGreen: "#28c840",
+} as const;
+
+export const FONT_MONO = '"JetBrains Mono", "SF Mono", "Menlo", monospace';
+export const FONT_SANS =
+  '"Inter", "SF Pro Display", -apple-system, BlinkMacSystemFont, sans-serif';

marketing/v3.1-launch/src/lib/timing.ts

@@ -0,0 +1,13 @@
+// Single source of truth for scene frame ranges.
+// 30fps × 30s = 900 frames total.
+export const FPS = 30;
+export const TOTAL_FRAMES = 900;
+
+export const SCENES = {
+  hook: { from: 0, durationInFrames: 90 },
+  oldWay: { from: 90, durationInFrames: 150 },
+  fanOut: { from: 240, durationInFrames: 180 },
+  comparison: { from: 420, durationInFrames: 210 },
+  howItWorks: { from: 630, durationInFrames: 150 },
+  cta: { from: 780, durationInFrames: 120 },
+} as const;

marketing/v3.1-launch/src/scenes/Scene1Hook.tsx

@@ -0,0 +1,61 @@
+import React from "react";
+import { AbsoluteFill, interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion";
+import { BadgeBar } from "../components/BadgeBar";
+import { COLORS, FONT_SANS } from "../lib/colors";
+
+export const Scene1Hook: React.FC = () => {
+  const frame = useCurrentFrame();
+  const { fps } = useVideoConfig();
+
+  const captionOpacity = interpolate(frame, [20, 35, 75, 90], [0, 1, 1, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+  const captionLift = spring({
+    frame: frame - 20,
+    fps,
+    config: { damping: 15, stiffness: 70 },
+    from: 16,
+    to: 0,
+  });
+
+  const badgeFadeOut = interpolate(frame, [70, 90], [1, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  return (
+    <AbsoluteFill
+      style={{
+        background: `radial-gradient(circle at 50% 40%, ${COLORS.bgPanelSoft} 0%, ${COLORS.bgDeep} 60%)`,
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+        justifyContent: "center",
+        gap: 64,
+      }}
+    >
+      <div style={{ opacity: badgeFadeOut }}>
+        <BadgeBar />
+      </div>
+      <div
+        style={{
+          opacity: captionOpacity,
+          transform: `translateY(${captionLift}px)`,
+          fontFamily: FONT_SANS,
+          fontSize: 96,
+          fontWeight: 600,
+          color: COLORS.fgPrimary,
+          textAlign: "center",
+          letterSpacing: -1.5,
+          lineHeight: 1.1,
+          maxWidth: 1400,
+        }}
+      >
+        What if one search
+        <br />
+        ran <span style={{ color: COLORS.accentCyan }}>3 at once?</span>
+      </div>
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/src/scenes/Scene2OldWay.tsx

@@ -0,0 +1,100 @@
+import React from "react";
+import { AbsoluteFill, interpolate, useCurrentFrame } from "remotion";
+import { TerminalWindow } from "../components/TerminalWindow";
+import { TypedLine } from "../components/TypedLine";
+import { COLORS, FONT_MONO, FONT_SANS } from "../lib/colors";
+
+export const Scene2OldWay: React.FC = () => {
+  const frame = useCurrentFrame();
+
+  const enter = interpolate(frame, [0, 20], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+  const slide = interpolate(frame, [0, 20], [40, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  // Result card fades in after type completes (~70 frames)
+  const resultFade = interpolate(frame, [70, 95], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  // Caption appears late
+  const captionFade = interpolate(frame, [110, 130, 150], [0, 1, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  return (
+    <AbsoluteFill
+      style={{
+        background: COLORS.bgDeep,
+        padding: 80,
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+        justifyContent: "center",
+      }}
+    >
+      <div
+        style={{
+          width: 1400,
+          height: 520,
+          opacity: enter,
+          transform: `translateY(${slide}px)`,
+        }}
+      >
+        <TerminalWindow title="bash">
+          <TypedLine
+            text="/last30days OpenAI"
+            startFrame={20}
+            prefix="$"
+            fontSize={42}
+          />
+          <div
+            style={{
+              opacity: resultFade,
+              marginTop: 36,
+              padding: "20px 24px",
+              background: COLORS.bgPanelSoft,
+              borderRadius: 12,
+              border: `1px solid ${COLORS.border}`,
+              fontFamily: FONT_MONO,
+              fontSize: 26,
+              color: COLORS.fgPrimary,
+              lineHeight: 1.6,
+            }}
+          >
+            <div style={{ color: COLORS.accentGreen }}>
+              ✅ All agents reported back!
+            </div>
+            <div style={{ color: COLORS.fgMuted, marginTop: 6 }}>
+              ├─ 🟠 Reddit: 14 threads
+            </div>
+            <div style={{ color: COLORS.fgMuted }}>
+              ├─ 🔵 X: 22 posts
+            </div>
+            <div style={{ color: COLORS.fgMuted }}>
+              └─ 🟡 HN: 1 story
+            </div>
+          </div>
+        </TerminalWindow>
+      </div>
+
+      <div
+        style={{
+          opacity: captionFade,
+          marginTop: 60,
+          fontFamily: FONT_SANS,
+          fontSize: 38,
+          color: COLORS.fgMuted,
+        }}
+      >
+        The old way: <span style={{ color: COLORS.fgPrimary }}>one topic.</span>
+      </div>
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/src/scenes/Scene3FanOut.tsx

@@ -0,0 +1,221 @@
+import React from "react";
+import { AbsoluteFill, interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion";
+import { TerminalWindow } from "../components/TerminalWindow";
+import { TypedLine } from "../components/TypedLine";
+import { COLORS, FONT_MONO, FONT_SANS } from "../lib/colors";
+
+const PROGRESS_LINES = [
+  { source: "Reddit", color: "#ff6a3d" },
+  { source: "X", color: "#36d6f7" },
+  { source: "YouTube", color: "#ff5757" },
+  { source: "TikTok", color: "#5fff9f" },
+  { source: "Instagram", color: "#ff55a3" },
+];
+
+const ENTITIES: { label: string; tag: string; accent: string }[] = [
+  { label: "OpenAI", tag: "$ /last30days OpenAI", accent: COLORS.accentCyan },
+  { label: "Anthropic", tag: "$ /last30days Anthropic", accent: COLORS.accentMagenta },
+  { label: "xAI", tag: "$ /last30days xAI", accent: COLORS.accentAmber },
+];
+
+const FanPane: React.FC<{
+  label: string;
+  tag: string;
+  accent: string;
+  panelStart: number;
+}> = ({ label, tag, accent, panelStart }) => {
+  const frame = useCurrentFrame();
+  const { fps } = useVideoConfig();
+  const localFrame = Math.max(0, frame - panelStart);
+
+  const enter = spring({
+    frame: localFrame,
+    fps,
+    config: { damping: 18, stiffness: 80 },
+    from: 0,
+    to: 1,
+  });
+  const slide = interpolate(localFrame, [0, 20], [40, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  return (
+    <div
+      style={{
+        flex: 1,
+        opacity: enter,
+        transform: `translateY(${slide}px)`,
+        height: 480,
+      }}
+    >
+      <TerminalWindow title={label} glow>
+        <div
+          style={{
+            color: accent,
+            fontSize: 18,
+            fontFamily: FONT_MONO,
+            marginBottom: 14,
+          }}
+        >
+          {tag}
+        </div>
+        <div
+          style={{
+            fontSize: 22,
+            color: COLORS.accentGreen,
+            fontFamily: FONT_MONO,
+            marginBottom: 12,
+          }}
+        >
+          [Competitors] running...
+        </div>
+        {PROGRESS_LINES.map((line, idx) => {
+          const lineStart = panelStart + 16 + idx * 6;
+          const lineFade = interpolate(
+            frame - lineStart,
+            [0, 8],
+            [0, 1],
+            { extrapolateLeft: "clamp", extrapolateRight: "clamp" },
+          );
+          // pulse the in-progress dot
+          const dotOn = Math.floor((frame - lineStart) / 6) % 2 === 0;
+          return (
+            <div
+              key={line.source}
+              style={{
+                opacity: lineFade,
+                fontFamily: FONT_MONO,
+                fontSize: 20,
+                color: COLORS.fgMuted,
+                display: "flex",
+                alignItems: "center",
+                marginBottom: 6,
+              }}
+            >
+              <span
+                style={{
+                  display: "inline-block",
+                  width: 10,
+                  height: 10,
+                  borderRadius: 10,
+                  background: dotOn ? line.color : COLORS.bgPanelSoft,
+                  marginRight: 12,
+                  boxShadow: dotOn ? `0 0 10px ${line.color}` : "none",
+                }}
+              />
+              <span style={{ color: line.color, marginRight: 8 }}>
+                ►
+              </span>
+              <span>{line.source}</span>
+              <span style={{ marginLeft: "auto", color: COLORS.fgDim }}>
+                {dotOn ? "..." : "·"}
+              </span>
+            </div>
+          );
+        })}
+      </TerminalWindow>
+    </div>
+  );
+};
+
+export const Scene3FanOut: React.FC = () => {
+  const frame = useCurrentFrame();
+
+  // Phase 1 (0-30 frames): single terminal types --competitors flag
+  // Phase 2 (30+): split into 3 panes
+  const splitProgress = interpolate(frame, [30, 50], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  const singleOpacity = interpolate(frame, [0, 8, 30, 45], [0, 1, 1, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  // Caption fades in shortly after panes settle so it has time to read.
+  const captionFade = interpolate(frame, [60, 80], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  return (
+    <AbsoluteFill
+      style={{
+        background: COLORS.bgDeep,
+        padding: 60,
+        display: "flex",
+        flexDirection: "column",
+      }}
+    >
+      <div
+        style={{
+          opacity: captionFade,
+          textAlign: "center",
+          fontFamily: FONT_SANS,
+          fontSize: 42,
+          color: COLORS.fgPrimary,
+          marginBottom: 32,
+        }}
+      >
+        Now it discovers competitors
+        <br />
+        <span style={{ color: COLORS.accentCyan }}>and runs all 3.</span>
+      </div>
+
+      <div
+        style={{
+          flex: 1,
+          position: "relative",
+        }}
+      >
+        {/* Single terminal during phase 1, fades out as panes appear */}
+        <div
+          style={{
+            position: "absolute",
+            inset: 0,
+            display: "flex",
+            alignItems: "center",
+            justifyContent: "center",
+            opacity: singleOpacity,
+          }}
+        >
+          <div style={{ width: 1200, height: 380 }}>
+            <TerminalWindow title="bash">
+              <TypedLine
+                text="/last30days OpenAI --competitors"
+                startFrame={0}
+                prefix="$"
+                fontSize={42}
+              />
+            </TerminalWindow>
+          </div>
+        </div>
+
+        {/* Three panes fade in starting frame ~30 */}
+        <div
+          style={{
+            position: "absolute",
+            inset: 0,
+            display: "flex",
+            alignItems: "center",
+            justifyContent: "center",
+            gap: 24,
+            opacity: splitProgress,
+          }}
+        >
+          {ENTITIES.map((entity, idx) => (
+            <FanPane
+              key={entity.label}
+              label={entity.label}
+              tag={entity.tag}
+              accent={entity.accent}
+              panelStart={45 + idx * 8}
+            />
+          ))}
+        </div>
+      </div>
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/src/scenes/Scene4Comparison.tsx

@@ -0,0 +1,105 @@
+import React from "react";
+import { AbsoluteFill, interpolate, useCurrentFrame } from "remotion";
+import { ComparisonTable } from "../components/ComparisonTable";
+import { COLORS, FONT_SANS } from "../lib/colors";
+
+const ROWS = [
+  {
+    dimension: "What it is",
+    cells: [
+      "GPT-5 leader, Plus + API",
+      "Claude 4, safety-first",
+      "Grok, X-native, fast",
+    ] as [string, string, string],
+  },
+  {
+    dimension: "30-day momentum",
+    cells: [
+      "GPT-5 launch wave",
+      "Claude 4.7 1M context",
+      "Grok 5 reveal",
+    ] as [string, string, string],
+  },
+  {
+    dimension: "Community vibe",
+    cells: [
+      "Defensive but deep",
+      "Quiet, devs-only",
+      "Loud, meme-rich",
+    ] as [string, string, string],
+  },
+  {
+    dimension: "Best for",
+    cells: [
+      "Mainstream + tools",
+      "Long-context coding",
+      "Live X intel",
+    ] as [string, string, string],
+  },
+];
+
+export const Scene4Comparison: React.FC = () => {
+  const frame = useCurrentFrame();
+
+  const captionFade = interpolate(frame, [0, 12, 180, 210], [0, 1, 1, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+  const captionLift = interpolate(frame, [0, 14], [16, 0], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  const tableFade = interpolate(frame, [16, 32], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  return (
+    <AbsoluteFill
+      style={{
+        background: COLORS.bgDeep,
+        padding: "48px 80px",
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+      }}
+    >
+      <div
+        style={{
+          opacity: captionFade,
+          transform: `translateY(${captionLift}px)`,
+          fontFamily: FONT_SANS,
+          fontSize: 38,
+          color: COLORS.fgMuted,
+          textAlign: "center",
+          marginBottom: 36,
+        }}
+      >
+        <span style={{ color: COLORS.accentCyan, fontWeight: 600 }}>
+          3 full passes.
+        </span>
+        <span style={{ marginLeft: 18, color: COLORS.accentMagenta, fontWeight: 600 }}>
+          3 save files.
+        </span>
+        <span style={{ marginLeft: 18, color: COLORS.fgPrimary, fontWeight: 600 }}>
+          1 comparison.
+        </span>
+      </div>
+      <div
+        style={{
+          width: "100%",
+          maxWidth: 1640,
+          opacity: tableFade,
+        }}
+      >
+        <ComparisonTable
+          startFrame={20}
+          entities={["OpenAI", "Anthropic", "xAI"]}
+          rows={ROWS}
+          rowStaggerFrames={22}
+        />
+      </div>
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/src/scenes/Scene5HowItWorks.tsx

@@ -0,0 +1,54 @@
+import React from "react";
+import { AbsoluteFill, interpolate, useCurrentFrame } from "remotion";
+import { COLORS, FONT_SANS } from "../lib/colors";
+
+const LINES = [
+  { text: "You pick the topic.", color: COLORS.fgPrimary },
+  { text: "The agent picks the peers.", color: COLORS.accentCyan },
+  { text: "The engine fans out.", color: COLORS.accentMagenta },
+];
+
+export const Scene5HowItWorks: React.FC = () => {
+  const frame = useCurrentFrame();
+
+  return (
+    <AbsoluteFill
+      style={{
+        background: `radial-gradient(circle at 50% 60%, ${COLORS.bgPanelSoft} 0%, ${COLORS.bgDeep} 70%)`,
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+        justifyContent: "center",
+        gap: 48,
+      }}
+    >
+      {LINES.map((line, idx) => {
+        const start = 10 + idx * 28;
+        const fade = interpolate(frame, [start, start + 14], [0, 1], {
+          extrapolateLeft: "clamp",
+          extrapolateRight: "clamp",
+        });
+        const slide = interpolate(frame, [start, start + 18], [24, 0], {
+          extrapolateLeft: "clamp",
+          extrapolateRight: "clamp",
+        });
+        return (
+          <div
+            key={line.text}
+            style={{
+              opacity: fade,
+              transform: `translateY(${slide}px)`,
+              fontFamily: FONT_SANS,
+              fontSize: 78,
+              fontWeight: 600,
+              color: line.color,
+              letterSpacing: -1,
+            }}
+          >
+            {line.text}
+          </div>
+        );
+      })}
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/src/scenes/Scene6CTA.tsx

@@ -0,0 +1,73 @@
+import React from "react";
+import { AbsoluteFill, interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion";
+import { BadgeBar } from "../components/BadgeBar";
+import { COLORS, FONT_MONO, FONT_SANS } from "../lib/colors";
+
+export const Scene6CTA: React.FC = () => {
+  const frame = useCurrentFrame();
+  const { fps } = useVideoConfig();
+
+  const installFade = interpolate(frame, [20, 40], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+  // Stronger 1Hz pulse on the install line for end-of-video emphasis
+  const pulse = 0.8 + 0.2 * Math.sin((frame / fps) * 2 * Math.PI);
+  const glowPulse = 0.4 + 0.4 * Math.sin((frame / fps) * 2 * Math.PI);
+
+  const repoFade = interpolate(frame, [50, 70], [0, 1], {
+    extrapolateLeft: "clamp",
+    extrapolateRight: "clamp",
+  });
+
+  const enterScale = spring({
+    frame,
+    fps,
+    config: { damping: 18, stiffness: 90 },
+    from: 0.95,
+    to: 1,
+  });
+
+  return (
+    <AbsoluteFill
+      style={{
+        background: `radial-gradient(circle at 50% 50%, ${COLORS.bgPanelSoft} 0%, ${COLORS.bgDeep} 70%)`,
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+        justifyContent: "center",
+        gap: 56,
+        transform: `scale(${enterScale})`,
+      }}
+    >
+      <BadgeBar />
+      <div
+        style={{
+          opacity: installFade * pulse,
+          padding: "18px 36px",
+          background: COLORS.bgPanel,
+          border: `1px solid ${COLORS.accentCyan}`,
+          borderRadius: 14,
+          boxShadow: `0 0 ${40 + glowPulse * 60}px ${COLORS.accentCyan}${Math.round(40 + glowPulse * 80).toString(16)}`,
+          fontFamily: FONT_MONO,
+          fontSize: 44,
+          color: COLORS.fgPrimary,
+        }}
+      >
+        <span style={{ color: COLORS.accentGreen, marginRight: 18 }}>$</span>
+        /last30days <span style={{ color: COLORS.fgMuted }}>{"{topic}"}</span>{" "}
+        <span style={{ color: COLORS.accentCyan }}>--competitors</span>
+      </div>
+      <div
+        style={{
+          opacity: repoFade,
+          fontFamily: FONT_SANS,
+          fontSize: 28,
+          color: COLORS.fgMuted,
+        }}
+      >
+        github.com/mvanhorn/last30days-skill
+      </div>
+    </AbsoluteFill>
+  );
+};

marketing/v3.1-launch/tsconfig.json

@@ -0,0 +1,19 @@
+{
+  "compilerOptions": {
+    "target": "ES2022",
+    "module": "ESNext",
+    "moduleResolution": "bundler",
+    "jsx": "react-jsx",
+    "strict": true,
+    "skipLibCheck": true,
+    "esModuleInterop": true,
+    "allowSyntheticDefaultImports": true,
+    "forceConsistentCasingInFileNames": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "lib": ["ES2022", "DOM", "DOM.Iterable"],
+    "types": ["node"]
+  },
+  "include": ["src/**/*"]
+}

plans/feat-add-websearch-source.md

@@ -1,395 +0,0 @@
-# feat: Add WebSearch as Third Source (Zero-Config Fallback)
-
-## Overview
-
-Add Claude's built-in WebSearch tool as a third research source for `/last30days`. This enables the skill to work **out of the box with zero API keys** while preserving the primacy of Reddit/X as the "voice of real humans with popularity signals."
-
-**Key principle**: WebSearch is supplementary, not primary. Real human voices on Reddit/X with engagement metrics (upvotes, likes, comments) are more valuable than general web content.
-
-## Problem Statement
-
-Currently `/last30days` requires at least one API key (OpenAI or xAI) to function. Users without API keys get an error. Additionally, web search could fill gaps where Reddit/X coverage is thin.
-
-**User requirements**:
-- Work out of the box (no API key needed)
-- Must NOT overpower Reddit/X results
-- Needs proper weighting
-- Validate with before/after testing
-
-## Proposed Solution
-
-### Weighting Strategy: "Engagement-Adjusted Scoring"
-
-**Current formula** (same for Reddit/X):
-```
-score = 0.45*relevance + 0.25*recency + 0.30*engagement - penalties
-```
-
-**Problem**: WebSearch has NO engagement metrics. Giving it `DEFAULT_ENGAGEMENT=35` with `-10 penalty` = 25 base, which still competes unfairly.
-
-**Solution**: Source-specific scoring with **engagement substitution**:
-
-| Source | Relevance | Recency | Engagement | Source Penalty |
-|--------|-----------|---------|------------|----------------|
-| Reddit | 45% | 25% | 30% (real metrics) | 0 |
-| X | 45% | 25% | 30% (real metrics) | 0 |
-| WebSearch | 55% | 35% | 0% (no data) | -15 points |
-
-**Rationale**:
-- WebSearch items compete on relevance + recency only (reweighted to 100%)
-- `-15 point source penalty` ensures WebSearch ranks below comparable Reddit/X items
-- High-quality WebSearch can still surface (score 60-70) but won't dominate (Reddit/X score 70-85)
-
-### Mode Behavior
-
-| API Keys Available | Default Behavior | `--include-web` |
-|--------------------|------------------|-----------------|
-| None | **WebSearch only** | n/a |
-| OpenAI only | Reddit only | Reddit + WebSearch |
-| xAI only | X only | X + WebSearch |
-| Both | Reddit + X | Reddit + X + WebSearch |
-
-**CLI flag**: `--include-web` (default: false when other sources available)
-
-## Technical Approach
-
-### Architecture
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                     last30days.py orchestrator                   │
-├─────────────────────────────────────────────────────────────────┤
-│  run_research()                                                  │
-│  ├── if sources includes "reddit": openai_reddit.search_reddit()│
-│  ├── if sources includes "x": xai_x.search_x()                  │
-│  └── if sources includes "web": websearch.search_web() ← NEW    │
-└─────────────────────────────────────────────────────────────────┘
-                              │
-                              ▼
-┌─────────────────────────────────────────────────────────────────┐
-│                     Processing Pipeline                          │
-├─────────────────────────────────────────────────────────────────┤
-│  normalize_websearch_items() → WebSearchItem schema ← NEW        │
-│  score_websearch_items() → engagement-free scoring ← NEW         │
-│  dedupe_websearch() → deduplication ← NEW                        │
-│  render_websearch_section() → output formatting ← NEW            │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-### Implementation Phases
-
-#### Phase 1: Schema & Core Infrastructure
-
-**Files to create/modify:**
-
-```python
-# scripts/lib/websearch.py (NEW)
-"""Claude WebSearch API client for general web discovery."""
-
-WEBSEARCH_PROMPT = """Search the web for content about: {topic}
-
-CRITICAL: Only include results from the last 30 days (after {from_date}).
-
-Find {min_items}-{max_items} high-quality, relevant web pages. Prefer:
-- Blog posts, tutorials, documentation
-- News articles, announcements
-- Authoritative sources (official docs, reputable publications)
-
-AVOID:
-- Reddit (covered separately)
-- X/Twitter (covered separately)
-- YouTube without transcripts
-- Forum threads without clear answers
-
-Return ONLY valid JSON:
-{{
-  "items": [
-    {{
-      "title": "Page title",
-      "url": "https://...",
-      "source_domain": "example.com",
-      "snippet": "Brief excerpt (100-200 chars)",
-      "date": "YYYY-MM-DD or null",
-      "why_relevant": "Brief explanation",
-      "relevance": 0.85
-    }}
-  ]
-}}
-"""
-
-def search_web(topic: str, from_date: str, to_date: str, depth: str = "default") -> dict:
-    """Search web using Claude's built-in WebSearch tool.
-
-    NOTE: This runs INSIDE Claude Code, so we use the WebSearch tool directly.
-    No API key needed - uses Claude's session.
-    """
-    # Implementation uses Claude's web_search_20250305 tool
-    pass
-
-def parse_websearch_response(response: dict) -> list[dict]:
-    """Parse WebSearch results into normalized format."""
-    pass
-```
-
-```python
-# scripts/lib/schema.py - ADD WebSearchItem
-
-@dataclass
-class WebSearchItem:
-    """Normalized web search item."""
-    id: str
-    title: str
-    url: str
-    source_domain: str  # e.g., "medium.com", "github.com"
-    snippet: str
-    date: Optional[str] = None
-    date_confidence: str = "low"
-    relevance: float = 0.5
-    why_relevant: str = ""
-    subs: SubScores = field(default_factory=SubScores)
-    score: int = 0
-
-    def to_dict(self) -> Dict[str, Any]:
-        return {
-            'id': self.id,
-            'title': self.title,
-            'url': self.url,
-            'source_domain': self.source_domain,
-            'snippet': self.snippet,
-            'date': self.date,
-            'date_confidence': self.date_confidence,
-            'relevance': self.relevance,
-            'why_relevant': self.why_relevant,
-            'subs': self.subs.to_dict(),
-            'score': self.score,
-        }
-```
-
-#### Phase 2: Scoring System Updates
-
-```python
-# scripts/lib/score.py - ADD websearch scoring
-
-# New constants
-WEBSEARCH_SOURCE_PENALTY = 15  # Points deducted for lacking engagement
-
-# Reweighted for no engagement
-WEBSEARCH_WEIGHT_RELEVANCE = 0.55
-WEBSEARCH_WEIGHT_RECENCY = 0.45
-
-def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebSearchItem]:
-    """Score WebSearch items WITHOUT engagement metrics.
-
-    Uses reweighted formula: 55% relevance + 45% recency - 15pt source penalty
-    """
-    for item in items:
-        rel_score = int(item.relevance * 100)
-        rec_score = dates.recency_score(item.date)
-
-        item.subs = schema.SubScores(
-            relevance=rel_score,
-            recency=rec_score,
-            engagement=0,  # Explicitly zero - no engagement data
-        )
-
-        overall = (
-            WEBSEARCH_WEIGHT_RELEVANCE * rel_score +
-            WEBSEARCH_WEIGHT_RECENCY * rec_score
-        )
-
-        # Apply source penalty (WebSearch < Reddit/X)
-        overall -= WEBSEARCH_SOURCE_PENALTY
-
-        # Apply date confidence penalty (same as other sources)
-        if item.date_confidence == "low":
-            overall -= 10
-        elif item.date_confidence == "med":
-            overall -= 5
-
-        item.score = max(0, min(100, int(overall)))
-
-    return items
-```
-
-#### Phase 3: Orchestrator Integration
-
-```python
-# scripts/last30days.py - UPDATE run_research()
-
-def run_research(...) -> tuple:
-    """Run the research pipeline.
-
-    Returns: (reddit_items, x_items, web_items, raw_openai, raw_xai,
-              raw_websearch, reddit_error, x_error, web_error)
-    """
-    # ... existing Reddit/X code ...
-
-    # WebSearch (new)
-    web_items = []
-    raw_websearch = None
-    web_error = None
-
-    if sources in ("all", "web", "reddit-web", "x-web"):
-        if progress:
-            progress.start_web()
-
-        try:
-            raw_websearch = websearch.search_web(topic, from_date, to_date, depth)
-            web_items = websearch.parse_websearch_response(raw_websearch)
-        except Exception as e:
-            web_error = f"{type(e).__name__}: {e}"
-
-        if progress:
-            progress.end_web(len(web_items))
-
-    return (reddit_items, x_items, web_items, raw_openai, raw_xai,
-            raw_websearch, reddit_error, x_error, web_error)
-```
-
-#### Phase 4: CLI & Environment Updates
-
-```python
-# scripts/last30days.py - ADD CLI flag
-
-parser.add_argument(
-    "--include-web",
-    action="store_true",
-    help="Include general web search alongside Reddit/X (lower weighted)",
-)
-
-# scripts/lib/env.py - UPDATE get_available_sources()
-
-def get_available_sources(config: dict) -> str:
-    """Determine available sources. WebSearch always available (no API key)."""
-    has_openai = bool(config.get('OPENAI_API_KEY'))
-    has_xai = bool(config.get('XAI_API_KEY'))
-
-    if has_openai and has_xai:
-        return 'both'  # WebSearch available but not default
-    elif has_openai:
-        return 'reddit'
-    elif has_xai:
-        return 'x'
-    else:
-        return 'web'  # Fallback: WebSearch only (no keys needed)
-```
-
-## Acceptance Criteria
-
-### Functional Requirements
-
-- [x] Skill works with zero API keys (WebSearch-only mode)
-- [x] `--include-web` flag adds WebSearch to Reddit/X searches
-- [x] WebSearch items have lower average scores than Reddit/X items with similar relevance
-- [x] WebSearch results exclude Reddit/X URLs (handled separately)
-- [x] Date filtering uses natural language ("last 30 days") in prompt
-- [x] Output clearly labels source type: `[WEB]`, `[Reddit]`, `[X]`
-
-### Non-Functional Requirements
-
-- [x] WebSearch adds <10s latency to total research time (0s - deferred to Claude)
-- [x] Graceful degradation if WebSearch fails
-- [ ] Cache includes WebSearch results appropriately
-
-### Quality Gates
-
-- [x] Before/after testing shows WebSearch doesn't dominate rankings (via -15pt penalty)
-- [x] Test: 10 Reddit + 10 X + 10 WebSearch → WebSearch avg score 15-20pts lower (scoring formula verified)
-- [x] Test: WebSearch-only mode produces useful results for common topics
-
-## Testing Plan
-
-### Before/After Comparison Script
-
-```python
-# tests/test_websearch_weighting.py
-
-"""
-Test harness to validate WebSearch doesn't overpower Reddit/X.
-
-Run same queries with:
-1. Reddit + X only (baseline)
-2. Reddit + X + WebSearch (comparison)
-
-Verify: WebSearch items rank lower on average.
-"""
-
-TEST_QUERIES = [
-    "best practices for react server components",
-    "AI coding assistants comparison",
-    "typescript 5.5 new features",
-]
-
-def test_websearch_weighting():
-    for query in TEST_QUERIES:
-        # Run without WebSearch
-        baseline = run_research(query, sources="both")
-        baseline_scores = [item.score for item in baseline.reddit + baseline.x]
-
-        # Run with WebSearch
-        with_web = run_research(query, sources="both", include_web=True)
-        web_scores = [item.score for item in with_web.web]
-        reddit_x_scores = [item.score for item in with_web.reddit + with_web.x]
-
-        # Assertions
-        avg_reddit_x = sum(reddit_x_scores) / len(reddit_x_scores)
-        avg_web = sum(web_scores) / len(web_scores) if web_scores else 0
-
-        assert avg_web < avg_reddit_x - 10, \
-            f"WebSearch avg ({avg_web}) too close to Reddit/X avg ({avg_reddit_x})"
-
-        # Check top 5 aren't all WebSearch
-        top_5 = sorted(with_web.reddit + with_web.x + with_web.web,
-                       key=lambda x: -x.score)[:5]
-        web_in_top_5 = sum(1 for item in top_5 if isinstance(item, WebSearchItem))
-        assert web_in_top_5 <= 2, f"Too many WebSearch items in top 5: {web_in_top_5}"
-```
-
-### Manual Test Scenarios
-
-| Scenario | Expected Outcome |
-|----------|------------------|
-| No API keys, run `/last30days AI tools` | WebSearch-only results, useful output |
-| Both keys + `--include-web`, run `/last30days react` | Mix of all 3 sources, Reddit/X dominate top 10 |
-| Niche topic (no Reddit/X coverage) | WebSearch fills gap, becomes primary |
-| Popular topic (lots of Reddit/X) | WebSearch present but lower-ranked |
-
-## Dependencies & Prerequisites
-
-- Claude Code's WebSearch tool (`web_search_20250305`) - already available
-- No new API keys required
-- Existing test infrastructure in `tests/`
-
-## Risk Analysis & Mitigation
-
-| Risk | Likelihood | Impact | Mitigation |
-|------|------------|--------|------------|
-| WebSearch returns stale content | Medium | Medium | Enforce date in prompt, apply low-confidence penalty |
-| WebSearch dominates rankings | Low | High | Source penalty (-15pts), testing validates |
-| WebSearch adds spam/low-quality | Medium | Medium | Exclude social media domains, domain filtering |
-| Date parsing unreliable | High | Medium | Accept "low" confidence as normal for WebSearch |
-
-## Future Considerations
-
-1. **Domain authority scoring**: Could proxy engagement with domain reputation
-2. **User-configurable weights**: Let users adjust WebSearch penalty
-3. **Domain whitelist/blacklist**: Filter WebSearch to trusted sources
-4. **Parallel execution**: Run all 3 sources concurrently for speed
-
-## References
-
-### Internal References
-- Scoring algorithm: `scripts/lib/score.py:8-15`
-- Source detection: `scripts/lib/env.py:57-72`
-- Schema patterns: `scripts/lib/schema.py:76-138`
-- Orchestrator: `scripts/last30days.py:54-164`
-
-### External References
-- Claude WebSearch docs: https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool
-- WebSearch pricing: $10/1K searches + token costs
-- Date filtering limitation: No explicit date params, use natural language
-
-### Research Findings
-- Reddit upvotes are ~12% of ranking value in SEO (strong signal)
-- E-E-A-T framework: Engagement metrics = trust signal
-- MSA2C2 approach: Dynamic weight learning for multi-source aggregation

pyproject.toml

@@ -0,0 +1,40 @@
+[project]
+name = "last30days-skill"
+version = "3.0.0"
+description = "Multi-source last-30-days research skill"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+  "requests>=2.32,<3",
+]
+
+[dependency-groups]
+dev = [
+  "pytest>=9,<10",
+  "pytest-cov>=7,<8",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+addopts = [
+  "-q",
+  "--tb=short",
+]
+
+[tool.coverage.run]
+branch = true
+source = ["scripts", "tests"]
+omit = [
+  "scripts/lib/vendor/*",
+  "dist/*",
+]
+
+[tool.coverage.report]
+skip_empty = true
+show_missing = true
+omit = [
+  "scripts/lib/vendor/*",
+  "dist/*",
+]
+

release-notes.md

@@ -0,0 +1,86 @@
+The AI world reinvents itself every month. This skill keeps you current.
+
+`/last30days` researches your topic across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and 5+ more sources from the last 30 days, finds what the community is actually upvoting, sharing, betting on, and saying on camera, and writes you a grounded narrative with real citations.
+
+## v3 is the intelligent search release
+
+v3 is a ground-up engine rewrite by [@j-sperling](https://github.com/j-sperling). The old engine searched keywords. The new engine understands your topic first, then searches the right people and communities.
+
+Type "OpenClaw" and v3 resolves @steipete, r/openclaw, r/ClaudeCode, and the right YouTube channels and TikTok hashtags before a single API call fires. Type "Peter Steinberger" and it resolves his X handle and GitHub profile, switches to person mode, and shows what he shipped this month at 85% merge rate across 22 PRs. None of that was on Google.
+
+## Headline features
+
+### Intelligent pre-research
+
+The killer feature. A new Python pre-research brain resolves X handles, GitHub repos, subreddits, TikTok hashtags, and YouTube channels before searching. Bidirectional: person to company, product to founder, name to GitHub profile. The right subreddits, the right handles, the right hashtags, all resolved before a single API call.
+
+### Best Takes
+
+A second LLM judge scores every result for humor, wit, and virality alongside relevance. Every brief now ends with a Best Takes section surfacing the cleverest one-liners and most viral quotes. The Reddit and X people are funny, and the old engine buried their best stuff.
+
+### Cross-source cluster merging
+
+When the same story hits Reddit, X, and YouTube, v3 merges them into one cluster instead of three duplicates. Entity-based overlap detection catches matches even when the titles use different words.
+
+### Single-pass comparisons
+
+"X vs Y" used to run three serial passes (12+ minutes). v3 runs one pass with entity-aware subqueries for both sides at once. Same depth, 3 minutes.
+
+### GitHub person-mode and project-mode
+
+When the topic is a person, the engine switches from keyword search to author-scoped queries. PR velocity, top repos by stars, release notes for what shipped this month, woven into the narrative alongside X posts and Reddit threads.
+
+When the topic is a project, it pulls live star counts, READMEs, releases, and top issues from the GitHub API. No stale blog posts.
+
+### ELI5 mode
+
+Say "eli5 on" after any research run. The synthesis rewrites in plain language. No jargon. Same data, same sources, same citations, just clearer. Say "eli5 off" to go back.
+
+### 13+ sources
+
+v3 adds Threads, Pinterest, Perplexity, Bluesky, and Parallel AI grounding to the existing Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and Web lineup. Perplexity Deep Research (`--deep-research`) gives you 50+ citation reports for serious investigation.
+
+### Per-author cap and entity disambiguation
+
+Max 3 items per author prevents single-voice dominance. Synthesis trusts resolved handles over fuzzy keyword matches.
+
+## Install
+
+Claude Code:
+
+```
+/plugin marketplace add mvanhorn/last30days-skill
+```
+
+OpenClaw:
+
+```
+clawhub install last30days-official
+```
+
+OpenAI Codex CLI: run `codex` from a checkout of this repo and v3's skill at `.agents/skills/last30days/SKILL.md` will be discovered automatically. Or copy `SKILL.md` to `~/.agents/skills/last30days/SKILL.md` for a global install.
+
+Zero config. Reddit, Hacker News, Polymarket, and GitHub work immediately. Run it once and the setup wizard unlocks X, YouTube, TikTok, and more in 30 seconds.
+
+## v3 Community
+
+v3 was shaped by community contributors whose PRs and issues inspired core features. Their code wasn't merged directly (v3 was a ground-up rewrite), but their ideas drove what shipped.
+
+Thanks to @uppinote20, @zerone0x, @thinkun, @thomasmktong, @fanispoulinakisai-boop, @pejmanjohn, @zl190, and @hnshah. See [CONTRIBUTORS.md](CONTRIBUTORS.md) for the full list.
+
+Contributors who shaped the release itself:
+
+- @Jah-yee (#153) surfaced the need for a real Codex CLI integration, which shipped in #219
+- @Cody-Coyote (#204) reported the marketplace validation bug that needed fixing before v3 could ship cleanly
+- @dannyshmueli pushed for v3 and Codex family support publicly on X
+
+Full Added / Changed / Fixed detail lives in [CHANGELOG.md](CHANGELOG.md) under `[3.0.0]`.
+
+## Earlier contributors
+
+From the v1 and v2 lineage:
+
+- [@galligan](https://github.com/galligan) for marketplace plugin inspiration
+- [@hutchins](https://x.com/hutchins) for pushing the YouTube feature
+
+30 days of research. 30 seconds of work. Thirteen sources. Zero stale prompts.

scripts/briefing.py

@@ -0,0 +1,264 @@
+#!/usr/bin/env python3
+"""Morning briefing generator for last30days.
+
+Synthesizes accumulated findings into formatted briefings.
+The Python script collects the data; the agent (via SKILL.md) does the
+beautiful synthesis. This script provides the structured data.
+
+Usage:
+    python3 briefing.py generate              # Daily briefing data
+    python3 briefing.py generate --weekly     # Weekly digest data
+    python3 briefing.py show [--date DATE]    # Show saved briefing
+"""
+
+import argparse
+import json
+import sys
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).parent.resolve()
+sys.path.insert(0, str(SCRIPT_DIR))
+
+import store
+
+BRIEFS_DIR = Path.home() / ".local" / "share" / "last30days" / "briefs"
+
+
+def _parse_sqlite_utc_timestamp(value: str) -> datetime:
+    return datetime.strptime(value, "%Y-%m-%d %H:%M:%S").replace(tzinfo=timezone.utc)
+
+
+def generate_daily(since: str = None) -> dict:
+    """Generate daily briefing data.
+
+    Returns structured data for the agent to synthesize into a beautiful briefing.
+    """
+    store.init_db()
+    topics = store.list_topics()
+
+    if not topics:
+        return {
+            "status": "no_topics",
+            "message": "No watchlist topics yet. Add one with: last30days watch add \"your topic\"",
+        }
+
+    enabled = [t for t in topics if t["enabled"]]
+    if not enabled:
+        return {
+            "status": "no_enabled",
+            "message": "All topics are paused. Enable a topic to generate briefings.",
+        }
+
+    # Default: findings since yesterday
+    if not since:
+        since = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+
+    briefing_topics = []
+    total_new = 0
+
+    for topic in enabled:
+        findings = store.get_new_findings(topic["id"], since)
+        last_run = topic.get("last_run")
+        last_status = topic.get("last_status", "unknown")
+
+        # Calculate staleness
+        stale = False
+        hours_ago = None
+        if last_run:
+            try:
+                run_dt = _parse_sqlite_utc_timestamp(last_run)
+                hours_ago = (datetime.now(timezone.utc) - run_dt).total_seconds() / 3600
+                stale = hours_ago > 36  # Stale if > 36 hours
+            except (ValueError, TypeError):
+                stale = True
+
+        topic_data = {
+            "name": topic["name"],
+            "findings": findings,
+            "new_count": len(findings),
+            "last_run": last_run,
+            "last_status": last_status,
+            "stale": stale,
+            "hours_ago": round(hours_ago, 1) if hours_ago else None,
+        }
+
+        # Extract top finding by engagement
+        if findings:
+            top = max(findings, key=lambda f: f.get("engagement_score", 0))
+            topic_data["top_finding"] = {
+                "title": top.get("source_title", ""),
+                "source": top.get("source", ""),
+                "author": top.get("author", ""),
+                "engagement": top.get("engagement_score", 0),
+                "content": top.get("content", "")[:300],
+            }
+
+        briefing_topics.append(topic_data)
+        total_new += len(findings)
+
+    # Cost info
+    daily_cost = store.get_daily_cost()
+    budget = float(store.get_setting("daily_budget", "5.00"))
+
+    # Find the single top finding across all topics (for TL;DR)
+    all_findings = []
+    for t in briefing_topics:
+        for f in t["findings"]:
+            f["_topic"] = t["name"]
+            all_findings.append(f)
+
+    top_overall = None
+    if all_findings:
+        top_overall = max(all_findings, key=lambda f: f.get("engagement_score", 0))
+
+    result = {
+        "status": "ok",
+        "date": datetime.now().strftime("%Y-%m-%d"),
+        "since": since,
+        "topics": briefing_topics,
+        "total_new": total_new,
+        "total_topics": len(briefing_topics),
+        "top_finding": {
+            "title": top_overall.get("source_title", ""),
+            "topic": top_overall.get("_topic", ""),
+            "engagement": top_overall.get("engagement_score", 0),
+        } if top_overall else None,
+        "cost": {
+            "daily": daily_cost,
+            "budget": budget,
+        },
+        "failed_topics": [
+            t["name"] for t in briefing_topics if t["last_status"] == "failed"
+        ],
+    }
+
+    # Save briefing data
+    _save_briefing(result)
+
+    return result
+
+
+def generate_weekly() -> dict:
+    """Generate weekly digest data with trend analysis."""
+    store.init_db()
+
+    week_ago = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
+    two_weeks_ago = (datetime.now() - timedelta(days=14)).strftime("%Y-%m-%d")
+
+    topics = store.list_topics()
+    if not topics:
+        return {"status": "no_topics", "message": "No watchlist topics."}
+
+    weekly_topics = []
+
+    for topic in topics:
+        if not topic["enabled"]:
+            continue
+
+        # This week's findings
+        this_week = store.get_new_findings(topic["id"], week_ago)
+
+        # Last week's findings (for comparison)
+        conn = store._connect()
+        try:
+            last_week_rows = conn.execute(
+                """SELECT * FROM findings
+                   WHERE topic_id = ? AND first_seen >= ? AND first_seen < ? AND dismissed = 0
+                   ORDER BY engagement_score DESC""",
+                (topic["id"], two_weeks_ago, week_ago),
+            ).fetchall()
+            last_week = [dict(r) for r in last_week_rows]
+        finally:
+            conn.close()
+
+        this_engagement = sum(f.get("engagement_score", 0) for f in this_week)
+        last_engagement = sum(f.get("engagement_score", 0) for f in last_week)
+
+        # Trend calculation
+        if last_engagement > 0:
+            engagement_change = ((this_engagement - last_engagement) / last_engagement) * 100
+        else:
+            engagement_change = 100 if this_engagement > 0 else 0
+
+        weekly_topics.append({
+            "name": topic["name"],
+            "this_week_count": len(this_week),
+            "last_week_count": len(last_week),
+            "this_week_engagement": this_engagement,
+            "last_week_engagement": last_engagement,
+            "engagement_change_pct": round(engagement_change, 1),
+            "top_findings": this_week[:5],  # Top 5 by engagement (already sorted)
+        })
+
+    result = {
+        "status": "ok",
+        "type": "weekly",
+        "week_of": week_ago,
+        "topics": weekly_topics,
+    }
+
+    _save_briefing(result, suffix="-weekly")
+
+    return result
+
+
+def show_briefing(date: str = None) -> dict:
+    """Load a saved briefing by date."""
+    if not date:
+        date = datetime.now().strftime("%Y-%m-%d")
+
+    path = BRIEFS_DIR / f"{date}.json"
+    if not path.exists():
+        # Try weekly
+        path = BRIEFS_DIR / f"{date}-weekly.json"
+
+    if not path.exists():
+        return {"status": "not_found", "message": f"No briefing found for {date}."}
+
+    with open(path, encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _save_briefing(data: dict, suffix: str = ""):
+    """Save briefing data to local archive."""
+    BRIEFS_DIR.mkdir(parents=True, exist_ok=True)
+    date = datetime.now().strftime("%Y-%m-%d")
+    path = BRIEFS_DIR / f"{date}{suffix}.json"
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(data, f, indent=2, default=str)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate last30days briefings")
+    sub = parser.add_subparsers(dest="command")
+
+    # generate
+    g = sub.add_parser("generate", help="Generate a briefing")
+    g.add_argument("--weekly", action="store_true", help="Weekly digest")
+    g.add_argument("--since", help="Findings since date (YYYY-MM-DD)")
+
+    # show
+    s = sub.add_parser("show", help="Show a saved briefing")
+    s.add_argument("--date", help="Date (YYYY-MM-DD, default: today)")
+
+    args = parser.parse_args()
+
+    if args.command == "generate":
+        if args.weekly:
+            result = generate_weekly()
+        else:
+            result = generate_daily(since=args.since)
+        print(json.dumps(result, indent=2, default=str))
+
+    elif args.command == "show":
+        result = show_briefing(date=args.date)
+        print(json.dumps(result, indent=2, default=str))
+
+    else:
+        parser.print_help()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

scripts/build-skill.sh

@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+# build-skill.sh - package this repo as a claude.ai-upload-ready .skill file
+# Usage: bash scripts/build-skill.sh  (run from repo root)
+#
+# Produces dist/last30days.skill, a zip with a single top-level `last30days/`
+# directory containing SKILL.md and the scripts/ runtime. See
+# docs/plans/2026-04-14-001-fix-skill-upload-200-file-limit-plan.md.
+set -euo pipefail
+
+REPO_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+cd "$REPO_ROOT"
+
+if ! git diff --quiet || ! git diff --cached --quiet; then
+  echo "error: working tree is dirty; commit or stash before building" >&2
+  exit 1
+fi
+
+mkdir -p dist
+OUT="dist/last30days.skill"
+git archive --format=zip --prefix=last30days/ --output="$OUT" HEAD
+
+# claude.ai's .skill bundle only needs the root SKILL.md + scripts/ runtime.
+# Claude Code needs skills/ and .claude-plugin/ in the git archive
+# (that's why they're NOT in .gitattributes export-ignore), but the .skill
+# bundle must strip them to keep a single canonical SKILL.md and stay under
+# the 200-file cap.
+zip -d "$OUT" "last30days/skills/*" "last30days/.claude-plugin/*" > /dev/null 2>&1 || true
+
+COUNT=$(unzip -l "$OUT" | tail -1 | awk '{print $2}')
+SIZE=$(du -h "$OUT" | cut -f1)
+
+if [ "$COUNT" -gt 200 ]; then
+  echo "error: $COUNT files in zip, claude.ai's cap is 200" >&2
+  echo "       check .gitattributes export-ignore entries and this script's zip -d excludes" >&2
+  exit 1
+fi
+
+SKILL_MD_COUNT=$(unzip -l "$OUT" | grep -c "SKILL.md" || true)
+if [ "$SKILL_MD_COUNT" -ne 1 ]; then
+  echo "error: expected exactly one SKILL.md, found $SKILL_MD_COUNT" >&2
+  exit 1
+fi
+
+echo "built $OUT ($COUNT files, $SIZE)"
+echo "upload via the claude.ai skill UI"

scripts/compare.sh

@@ -0,0 +1,61 @@
+#!/bin/bash
+# A/B test runner: public release vs private beta
+# Usage: bash scripts/compare.sh "Kanye West"
+#
+# Runs /last30days (public release) and /last30days-beta (private beta)
+# sequentially with a 30s gap, saves raw results with distinct suffixes,
+# prints file paths for comparison.
+
+set -e
+
+if [ $# -eq 0 ]; then
+  echo "Usage: bash scripts/compare.sh <topic>"
+  echo "  Example: bash scripts/compare.sh Kevin Rose"
+  exit 1
+fi
+TOPIC="$*"
+SLUG=$(echo "$TOPIC" | tr '[:upper:]' '[:lower:]' | sed 's/[^a-z0-9]/-/g' | sed 's/--*/-/g' | sed 's/^-//' | sed 's/-$//')
+LAST30DAYS_MEMORY_DIR="${LAST30DAYS_MEMORY_DIR:-$HOME/Documents/Last30Days}"
+DIR="$LAST30DAYS_MEMORY_DIR"
+DATE=$(date +%Y-%m-%d)
+
+echo "=============================================="
+echo " A/B Test: $TOPIC"
+echo " Date: $DATE"
+echo "=============================================="
+echo ""
+
+# Run 1: public release
+echo "[1/2] Running /last30days (public release)..."
+echo "  This takes 2-4 minutes..."
+claude -p --dangerously-skip-permissions "/last30days $TOPIC" > /dev/null 2>&1 || true
+RELEASE_FILE="$DIR/${SLUG}-raw.md"
+[ -f "$RELEASE_FILE" ] && echo "  Done: $RELEASE_FILE" || echo "  FAILED: no output file"
+echo ""
+
+echo "  Waiting 30s for API rate limits..."
+sleep 30
+
+# Run 2: private beta
+echo "[2/2] Running /last30days-beta (private beta)..."
+echo "  This takes 2-4 minutes..."
+claude -p --dangerously-skip-permissions "/last30days-beta $TOPIC" > /dev/null 2>&1 || true
+BETA_FILE="$DIR/${SLUG}-raw-beta.md"
+[ -f "$BETA_FILE" ] && echo "  Done: $BETA_FILE" || echo "  FAILED: no output file"
+echo ""
+
+echo "=============================================="
+echo " Both complete. Raw files:"
+echo "=============================================="
+echo ""
+ls -la "$DIR/${SLUG}-raw"*.md 2>/dev/null || echo "  (no files found - check if skills saved correctly)"
+echo ""
+echo "To compare, run in Claude Code:"
+echo "  Read and compare these raw research files, produce a detailed report:"
+echo "  $RELEASE_FILE"
+echo "  $BETA_FILE"
+echo ""
+echo "Beta output should start with a line like:"
+echo "  🧪 last30days-beta · branch <name> · synced $DATE"
+echo "If that line is missing, the beta badge regressed. See docs/plans/2026-04-17-005-*-plan.md."
+echo ""

scripts/evaluate_search_quality.py

@@ -0,0 +1,549 @@
+#!/usr/bin/env python3
+"""Compare two last30days revisions on the v3 ranked candidate output."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import subprocess
+import sys
+import tempfile
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+from urllib.error import HTTPError, URLError
+from urllib.request import Request, urlopen
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+from lib import env as envlib
+from lib import schema
+
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+EVAL_TOPICS_FILE = REPO_ROOT / "fixtures" / "eval_topics.json"
+
+
+def _load_default_topics() -> list[tuple[str, str]]:
+    if EVAL_TOPICS_FILE.exists():
+        rows = json.loads(EVAL_TOPICS_FILE.read_text())
+        return [(row["topic"], row["query_type"]) for row in rows]
+    return [
+        ("nano banana pro prompting", "product"),
+        ("codex vs claude code", "comparison"),
+        ("openclaw vs nanoclaw vs ironclaw", "comparison"),
+        ("anthropic odds", "prediction"),
+        ("kanye west", "breaking_news"),
+        ("remotion animations for Claude Code", "how_to"),
+    ]
+
+
+DEFAULT_TOPICS = _load_default_topics()
+DEFAULT_SEARCH = ""
+DEFAULT_JUDGE_MODEL = "gemini-3.1-flash-lite-preview"
+GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={api_key}"
+
+
+def stable_item_key(item: dict[str, Any]) -> str:
+    return str(item.get("candidate_id") or item.get("url") or item.get("title") or "")
+
+
+def row_sources(row: dict[str, Any]) -> list[str]:
+    candidate = schema.candidate_from_dict(row)
+    return schema.candidate_sources(candidate)
+
+
+def row_best_date(row: dict[str, Any]) -> str | None:
+    candidate = schema.candidate_from_dict(row)
+    return schema.candidate_best_published_at(candidate)
+
+
+V2_SOURCE_KEYS = [
+    ("reddit", "title"),
+    ("x", "text"),
+    ("youtube", "title"),
+    ("tiktok", "text"),
+    ("instagram", "text"),
+    ("hackernews", "title"),
+    ("bluesky", "text"),
+    ("truthsocial", "text"),
+    ("polymarket", "question"),
+    ("web", "title"),
+]
+
+
+def build_ranked_items(report: dict[str, Any], limit: int) -> list[dict[str, Any]]:
+    # v3 format: ranked_candidates list
+    if report.get("ranked_candidates"):
+        ranked = []
+        for row in report["ranked_candidates"][:limit]:
+            candidate_sources = row_sources(row)
+            ranked.append({
+                "key": stable_item_key(row),
+                "source": ", ".join(candidate_sources),
+                "sources": candidate_sources,
+                "url": str(row.get("url") or ""),
+                "text": str(row.get("title") or ""),
+                "date": row_best_date(row),
+                "score": float(row.get("final_score") or 0.0),
+            })
+        return ranked
+
+    # v2 format: per-source lists (reddit, x, youtube, etc.)
+    all_items = []
+    for source_key, text_field in V2_SOURCE_KEYS:
+        for item in report.get(source_key) or []:
+            if not isinstance(item, dict):
+                continue
+            all_items.append({
+                "key": str(item.get("url") or item.get("id") or item.get(text_field) or ""),
+                "source": source_key,
+                "sources": [source_key],
+                "url": str(item.get("url") or ""),
+                "text": str(item.get(text_field) or item.get("title") or ""),
+                "date": item.get("date"),
+                "score": float(item.get("score") or 0.0),
+            })
+    all_items.sort(key=lambda x: x["score"], reverse=True)
+    return all_items[:limit]
+
+
+def source_sets(report: dict[str, Any], limit: int) -> dict[str, set[str]]:
+    grouped: dict[str, set[str]] = {}
+    for item in build_ranked_items(report, limit):
+        for source in item["sources"]:
+            grouped.setdefault(source, set()).add(item["key"])
+    return grouped
+
+
+def jaccard(left: set[str], right: set[str]) -> float:
+    if not left and not right:
+        return 1.0
+    union = left | right
+    if not union:
+        return 1.0
+    return len(left & right) / len(union)
+
+
+def retention(left: set[str], right: set[str]) -> float:
+    if not left:
+        return 1.0
+    return len(left & right) / len(left)
+
+
+def precision_at_k(ranking: list[dict[str, Any]], judgments: dict[str, int], k: int) -> float:
+    top = ranking[:k]
+    if not top:
+        return 0.0
+    return sum(1 for item in top if judgments.get(item["key"], 0) >= 2) / len(top)
+
+
+def ndcg_at_k(ranking: list[dict[str, Any]], judgments: dict[str, int], k: int, judged_pool: list[dict[str, Any]]) -> float:
+    top = ranking[:k]
+    if not top:
+        return 0.0
+
+    def dcg(grades: list[int]) -> float:
+        total = 0.0
+        for index, grade in enumerate(grades, start=1):
+            total += (2**grade - 1) / math.log2(index + 1)
+        return total
+
+    actual = [judgments.get(item["key"], 0) for item in top]
+    ideal = sorted((judgments.get(item["key"], 0) for item in judged_pool), reverse=True)[: len(top)]
+    ideal_score = dcg(ideal)
+    if ideal_score == 0:
+        return 0.0
+    return dcg(actual) / ideal_score
+
+
+def source_coverage_recall(ranking: list[dict[str, Any]], judged_pool: list[dict[str, Any]], judgments: dict[str, int]) -> float:
+    good_sources = {
+        source
+        for item in judged_pool
+        if judgments.get(item["key"], 0) >= 2
+        for source in item["sources"]
+    }
+    if not good_sources:
+        return 1.0
+    hit_sources = {
+        source
+        for item in ranking
+        if judgments.get(item["key"], 0) >= 2
+        for source in item["sources"]
+    }
+    return len(hit_sources & good_sources) / len(good_sources)
+
+
+def resolve_google_judge_api_key(config: dict[str, Any]) -> str | None:
+    return (
+        os.environ.get("GOOGLE_API_KEY")
+        or config.get("GOOGLE_API_KEY")
+        or os.environ.get("GEMINI_API_KEY")
+        or config.get("GEMINI_API_KEY")
+        or os.environ.get("GOOGLE_GENAI_API_KEY")
+        or config.get("GOOGLE_GENAI_API_KEY")
+    )
+
+
+def extract_gemini_text(payload: dict[str, Any]) -> str:
+    for candidate in payload.get("candidates") or []:
+        content = candidate.get("content") or {}
+        for part in content.get("parts") or []:
+            if part.get("text"):
+                return part["text"]
+    raise ValueError("Gemini response did not contain text.")
+
+
+def call_gemini_judge(api_key: str, model: str, prompt: str) -> dict[str, Any]:
+    body = {
+        "contents": [{"parts": [{"text": prompt}]}],
+        "generationConfig": {"temperature": 0, "responseMimeType": "application/json"},
+    }
+    request = Request(
+        GEMINI_API_URL.format(model=model, api_key=api_key),
+        data=json.dumps(body).encode("utf-8"),
+        headers={"Content-Type": "application/json"},
+        method="POST",
+    )
+    try:
+        with urlopen(request, timeout=120) as response:
+            payload = json.loads(response.read().decode("utf-8"))
+    except HTTPError as exc:
+        detail = exc.read().decode("utf-8", errors="replace")
+        raise RuntimeError(f"Gemini HTTP {exc.code}: {detail}") from exc
+    except URLError as exc:
+        raise RuntimeError(f"Gemini request failed: {exc}") from exc
+    return json.loads(extract_gemini_text(payload))
+
+
+def build_judge_prompt(topic: str, query_type: str, items: list[dict[str, Any]]) -> str:
+    item_lines = []
+    for item in items:
+        item_lines.append(
+            "\n".join([
+                f"- id: {item['key']}",
+                f"  source: {item['source']}",
+                f"  title: {item['text'][:220]}",
+                f"  url: {item['url']}",
+                f"  date: {item.get('date') or 'unknown'}",
+            ])
+        )
+    return f"""
+Judge search-result relevance for a last-30-days research tool.
+
+Topic: {topic}
+Query type: {query_type}
+
+Score each item on this 0-3 scale:
+- 0 = off-topic or clearly bad
+- 1 = weak or tangential
+- 2 = relevant and useful
+- 3 = highly relevant, one of the best results
+
+Return JSON only:
+{{
+  "judgments": [
+    {{"id": "ITEM_ID", "grade": 0}}
+  ]
+}}
+
+Items:
+{chr(10).join(item_lines)}
+""".strip()
+
+
+def get_judgments(
+    *,
+    output_dir: Path,
+    slug: str,
+    topic: str,
+    query_type: str,
+    items: list[dict[str, Any]],
+    judge_model: str,
+    gemini_api_key: str | None,
+) -> dict[str, int]:
+    cache_file = output_dir / "judgments" / f"{slug}.json"
+    cache_file.parent.mkdir(parents=True, exist_ok=True)
+    if cache_file.exists():
+        payload = json.loads(cache_file.read_text())
+        return {row["id"]: int(row["grade"]) for row in payload.get("judgments") or []}
+    if not gemini_api_key or not items:
+        return {}
+    payload = call_gemini_judge(gemini_api_key, judge_model, build_judge_prompt(topic, query_type, items))
+    cache_file.write_text(json.dumps(payload, indent=2))
+    return {row["id"]: int(row["grade"]) for row in payload.get("judgments") or []}
+
+
+def create_eval_env() -> dict[str, str]:
+    config = envlib.get_config()
+    passthrough = {
+        "PATH": os.environ.get("PATH", ""),
+        "LANG": os.environ.get("LANG", "en_US.UTF-8"),
+        "LC_ALL": os.environ.get("LC_ALL", ""),
+        "TMPDIR": os.environ.get("TMPDIR", ""),
+        "PYTHONUTF8": "1",
+        "LAST30DAYS_CONFIG_DIR": "",
+    }
+    for key in (
+        "GOOGLE_API_KEY",
+        "GEMINI_API_KEY",
+        "GOOGLE_GENAI_API_KEY",
+        "OPENAI_API_KEY",
+        "XAI_API_KEY",
+        "SCRAPECREATORS_API_KEY",
+        "BSKY_HANDLE",
+        "BSKY_APP_PASSWORD",
+        "TRUTHSOCIAL_TOKEN",
+        "AUTH_TOKEN",
+        "CT0",
+    ):
+        value = os.environ.get(key) or config.get(key)
+        if value:
+            passthrough[key] = value
+    return passthrough
+
+
+def run_last30days(repo_dir: Path, topic: str, *, search: str, timeout_seconds: int, quick: bool, mock: bool, env: dict[str, str]) -> dict[str, Any]:
+    cmd = [sys.executable, "scripts/last30days.py", topic, "--emit=json"]
+    if search:
+        cmd.extend(["--search", search])
+    if quick:
+        cmd.append("--quick")
+    if mock:
+        cmd.append("--mock")
+    result = subprocess.run(
+        cmd,
+        cwd=repo_dir,
+        env=env,
+        capture_output=True,
+        text=True,
+        timeout=timeout_seconds,
+        check=False,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"{repo_dir.name} failed for '{topic}' with exit {result.returncode}\n{result.stderr.strip()}")
+    return json.loads(result.stdout)
+
+
+def create_worktree(rev: str) -> Path:
+    worktree_dir = Path(tempfile.mkdtemp(prefix="last30days-eval-"))
+    subprocess.run(
+        ["git", "worktree", "add", "--detach", str(worktree_dir), rev],
+        cwd=REPO_ROOT,
+        check=True,
+        capture_output=True,
+        text=True,
+    )
+    return worktree_dir
+
+
+def resolve_repo_dir(label: str) -> tuple[Path, bool]:
+    """Resolve a benchmark label into a repo directory and whether it is temporary."""
+    if label == "WORKTREE":
+        return REPO_ROOT, False
+    return create_worktree(label), True
+
+
+def remove_worktree(path: Path) -> None:
+    subprocess.run(
+        ["git", "worktree", "remove", "--force", str(path)],
+        cwd=REPO_ROOT,
+        check=False,
+        capture_output=True,
+        text=True,
+    )
+    try:
+        os.rmdir(path)
+    except OSError:
+        pass
+
+
+def summarize_topic(topic: str, query_type: str, baseline_report: dict[str, Any], candidate_report: dict[str, Any], judgments: dict[str, int], judged_pool: list[dict[str, Any]], limit: int) -> dict[str, Any]:
+    baseline_ranked = build_ranked_items(baseline_report, limit)
+    candidate_ranked = build_ranked_items(candidate_report, limit)
+    baseline_sets = source_sets(baseline_report, limit)
+    candidate_sets = source_sets(candidate_report, limit)
+    overall_left = set().union(*baseline_sets.values()) if baseline_sets else set()
+    overall_right = set().union(*candidate_sets.values()) if candidate_sets else set()
+    sources = sorted(set(baseline_sets) | set(candidate_sets))
+    return {
+        "topic": topic,
+        "query_type": query_type,
+        "baseline": {
+            "precision_at_5": precision_at_k(baseline_ranked, judgments, 5),
+            "ndcg_at_5": ndcg_at_k(baseline_ranked, judgments, 5, judged_pool),
+            "source_coverage_recall": source_coverage_recall(baseline_ranked, judged_pool, judgments),
+        },
+        "candidate": {
+            "precision_at_5": precision_at_k(candidate_ranked, judgments, 5),
+            "ndcg_at_5": ndcg_at_k(candidate_ranked, judgments, 5, judged_pool),
+            "source_coverage_recall": source_coverage_recall(candidate_ranked, judged_pool, judgments),
+        },
+        "stability": {
+            "overall_jaccard": jaccard(overall_left, overall_right),
+            "overall_retention_vs_baseline": retention(overall_left, overall_right),
+            "per_source": {
+                source: {
+                    "baseline_count": len(baseline_sets.get(source, set())),
+                    "candidate_count": len(candidate_sets.get(source, set())),
+                    "jaccard": jaccard(baseline_sets.get(source, set()), candidate_sets.get(source, set())),
+                    "retention_vs_baseline": retention(baseline_sets.get(source, set()), candidate_sets.get(source, set())),
+                }
+                for source in sources
+            },
+        },
+    }
+
+
+def write_summary(output_dir: Path, baseline_label: str, candidate_label: str, summaries: list[dict[str, Any]]) -> None:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    payload = {
+        "generated_at": datetime.now().isoformat(timespec="seconds"),
+        "baseline": baseline_label,
+        "candidate": candidate_label,
+        "topics": summaries,
+    }
+    (output_dir / "metrics.json").write_text(json.dumps(payload, indent=2))
+
+    lines = [
+        "# Search Quality Evaluation",
+        "",
+        f"- Baseline: `{baseline_label}`",
+        f"- Candidate: `{candidate_label}`",
+        f"- Generated: {payload['generated_at']}",
+        "",
+        "| Topic | Base P@5 | Cand P@5 | Base nDCG@5 | Cand nDCG@5 | Jaccard | Retention |",
+        "|---|---:|---:|---:|---:|---:|---:|",
+    ]
+    for row in summaries:
+        lines.append(
+            "| {topic} | {bp:.2f} | {cp:.2f} | {bn:.2f} | {cn:.2f} | {jac:.2f} | {ret:.2f} |".format(
+                topic=row["topic"],
+                bp=row["baseline"]["precision_at_5"],
+                cp=row["candidate"]["precision_at_5"],
+                bn=row["baseline"]["ndcg_at_5"],
+                cn=row["candidate"]["ndcg_at_5"],
+                jac=row["stability"]["overall_jaccard"],
+                ret=row["stability"]["overall_retention_vs_baseline"],
+            )
+        )
+    (output_dir / "summary.md").write_text("\n".join(lines) + "\n")
+
+
+def write_failure_summary(
+    output_dir: Path,
+    baseline_label: str,
+    candidate_label: str,
+    summaries: list[dict[str, Any]],
+    failures: list[dict[str, Any]],
+) -> None:
+    write_summary(output_dir, baseline_label, candidate_label, summaries)
+    metrics_path = output_dir / "metrics.json"
+    payload = json.loads(metrics_path.read_text()) if metrics_path.exists() else {
+        "generated_at": datetime.now().isoformat(timespec="seconds"),
+        "baseline": baseline_label,
+        "candidate": candidate_label,
+        "topics": [],
+    }
+    payload["failures"] = failures
+    metrics_path.write_text(json.dumps(payload, indent=2))
+
+    summary_path = output_dir / "summary.md"
+    lines = summary_path.read_text().splitlines() if summary_path.exists() else ["# Search Quality Evaluation", ""]
+    if failures:
+        lines.extend([
+            "",
+            "## Failures",
+            "",
+        ])
+        for failure in failures:
+            lines.append(f"- `{failure['topic']}`: {failure['error']}")
+    summary_path.write_text("\n".join(lines).rstrip() + "\n")
+
+
+def parse_topics_file(path: Path) -> list[tuple[str, str]]:
+    rows = json.loads(path.read_text())
+    return [(str(row["topic"]), str(row.get("query_type") or "general")) for row in rows]
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Compare two last30days revisions on ranked candidate quality")
+    parser.add_argument("--baseline", default="HEAD~1")
+    parser.add_argument("--candidate", default="WORKTREE")
+    parser.add_argument("--search", default=DEFAULT_SEARCH)
+    parser.add_argument("--output-dir", default="tmp/search-quality")
+    parser.add_argument("--judge-model", default=DEFAULT_JUDGE_MODEL)
+    parser.add_argument("--timeout", type=int, default=240)
+    parser.add_argument("--limit", type=int, default=20)
+    parser.add_argument("--mock", action="store_true")
+    parser.add_argument("--quick", action="store_true")
+    parser.add_argument("--topics-file")
+    return parser
+
+
+def main() -> int:
+    args = build_parser().parse_args()
+    topics = parse_topics_file(Path(args.topics_file)) if args.topics_file else DEFAULT_TOPICS
+    output_dir = Path(args.output_dir).resolve()
+    config = envlib.get_config()
+    gemini_api_key = resolve_google_judge_api_key(config)
+    run_env = create_eval_env()
+
+    baseline_dir, baseline_temp = resolve_repo_dir(args.baseline)
+    candidate_dir, candidate_temp = resolve_repo_dir(args.candidate)
+    try:
+        summaries = []
+        failures = []
+        for topic, query_type in topics:
+            try:
+                baseline_report = run_last30days(
+                    baseline_dir,
+                    topic,
+                    search=args.search,
+                    timeout_seconds=args.timeout,
+                    quick=args.quick,
+                    mock=args.mock,
+                    env=run_env,
+                )
+                candidate_report = run_last30days(
+                    candidate_dir,
+                    topic,
+                    search=args.search,
+                    timeout_seconds=args.timeout,
+                    quick=args.quick,
+                    mock=args.mock,
+                    env=run_env,
+                )
+                judged_pool_map = {
+                    item["key"]: item
+                    for item in build_ranked_items(baseline_report, args.limit) + build_ranked_items(candidate_report, args.limit)
+                }
+                judged_pool = list(judged_pool_map.values())
+                judgments = get_judgments(
+                    output_dir=output_dir,
+                    slug="".join(char.lower() if char.isalnum() else "-" for char in topic).strip("-"),
+                    topic=topic,
+                    query_type=query_type,
+                    items=judged_pool,
+                    judge_model=args.judge_model,
+                    gemini_api_key=gemini_api_key,
+                )
+                summaries.append(summarize_topic(topic, query_type, baseline_report, candidate_report, judgments, judged_pool, args.limit))
+            except Exception as exc:
+                failures.append({"topic": topic, "query_type": query_type, "error": str(exc)})
+        write_failure_summary(output_dir, args.baseline, args.candidate, summaries, failures)
+    finally:
+        if baseline_temp:
+            remove_worktree(baseline_dir)
+        if candidate_temp:
+            remove_worktree(candidate_dir)
+    result = {"output_dir": str(output_dir), "topics": len(topics), "failures": len(failures)}
+    print(json.dumps(result, indent=2))
+    return 1 if failures else 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/lib/bird_x.py

@@ -0,0 +1,474 @@
+"""Bird X search client for the v3.0.0 last30days pipeline.
+
+Uses a vendored subset of @steipete/bird v0.8.0 (MIT License) to search X
+via Twitter's GraphQL API. No external `bird` CLI binary needed - just Node.js.
+See scripts/lib/vendor/bird-search/package.json for authoritative version.
+"""
+
+import json
+import os
+import signal
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+from . import http, log
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Tuple
+
+from .relevance import token_overlap_relevance as _compute_relevance
+
+
+def _first_of(*values):
+    """Return first value that is not None."""
+    for v in values:
+        if v is not None:
+            return v
+    return None
+
+# Path to the vendored bird-search wrapper
+_BIRD_SEARCH_MJS = Path(__file__).parent / "vendor" / "bird-search" / "bird-search.mjs"
+
+# Depth configurations: number of results to request
+DEPTH_CONFIG = {
+    "quick": 12,
+    "default": 30,
+    "deep": 60,
+}
+
+# Module-level credentials injected from .env config
+_credentials: Dict[str, str] = {}
+
+
+def set_credentials(auth_token: Optional[str], ct0: Optional[str]):
+    """Inject AUTH_TOKEN/CT0 from .env config so Node subprocesses can use them."""
+    if auth_token:
+        _credentials['AUTH_TOKEN'] = auth_token
+    if ct0:
+        _credentials['CT0'] = ct0
+
+
+def _has_injected_credentials() -> bool:
+    """Return True when both X session cookies were injected from config."""
+    return bool(_credentials.get('AUTH_TOKEN') and _credentials.get('CT0'))
+
+
+def _has_process_credentials() -> bool:
+    """Return True when AUTH_TOKEN/CT0 are present in process env."""
+    return bool(os.environ.get("AUTH_TOKEN") and os.environ.get("CT0"))
+
+
+def _subprocess_env() -> Dict[str, str]:
+    """Build env dict for Node subprocesses, merging injected credentials."""
+    env = os.environ.copy()
+    env.update(_credentials)
+    # Hard-disable browser-cookie fallback so normal pipeline runs never hit
+    # Safari/Chrome Keychain prompts during source detection or search.
+    env["BIRD_DISABLE_BROWSER_COOKIES"] = "1"
+    return env
+
+
+def _log(msg: str):
+    log.source_log("Bird", msg, tty_only=False)
+
+
+def _extract_core_subject(topic: str) -> str:
+    """Extract core subject from verbose query for X search.
+
+    X search is literal keyword AND matching — all words must appear.
+    Aggressively strip question/meta/research words to keep only the
+    core product/concept name (max 5 words).
+    """
+    from .query import extract_core_subject
+    return extract_core_subject(topic, max_words=5, strip_suffixes=True)
+
+
+def is_bird_installed() -> bool:
+    """Check if vendored Bird search module is available.
+
+    Returns:
+        True if bird-search.mjs exists and Node.js is in PATH.
+    """
+    if not _BIRD_SEARCH_MJS.exists():
+        return False
+    return shutil.which("node") is not None
+
+
+def is_bird_authenticated() -> Optional[str]:
+    """Check if explicit X credentials are available.
+
+    Returns:
+        Auth source string if authenticated, None otherwise.
+    """
+    if not is_bird_installed():
+        return None
+
+    if _has_injected_credentials():
+        return "env AUTH_TOKEN"
+    if _has_process_credentials():
+        return "env AUTH_TOKEN"
+    return None
+
+
+def check_npm_available() -> bool:
+    """Check if npm is available (kept for API compatibility).
+
+    Returns:
+        True if 'npm' command is available in PATH, False otherwise.
+    """
+    return shutil.which("npm") is not None
+
+
+def install_bird() -> Tuple[bool, str]:
+    """No-op. Bird search is vendored in v3.0.0, no installation needed.
+
+    Returns:
+        Tuple of (success, message).
+    """
+    if is_bird_installed():
+        return True, "Bird search is bundled with /last30days v3.0.0 - no installation needed."
+    if not shutil.which("node"):
+        return False, "Node.js 22+ is required for X search. Install Node.js first."
+    return False, f"Vendored bird-search.mjs not found at {_BIRD_SEARCH_MJS}"
+
+
+def get_bird_status() -> Dict[str, Any]:
+    """Get comprehensive Bird search status.
+
+    Returns:
+        Dict with keys: installed, authenticated, username, can_install
+    """
+    installed = is_bird_installed()
+    auth_source = is_bird_authenticated() if installed else None
+
+    return {
+        "installed": installed,
+        "authenticated": auth_source is not None,
+        "username": auth_source,  # Now returns auth source (e.g., "Safari", "env AUTH_TOKEN")
+        "can_install": True,  # Always vendored in v3.0.0
+    }
+
+
+def _run_bird_search(query: str, count: int, timeout: int) -> Dict[str, Any]:
+    """Run a search using the vendored bird-search.mjs module.
+
+    Args:
+        query: Full search query string (including since: filter)
+        count: Number of results to request
+        timeout: Timeout in seconds
+
+    Returns:
+        Raw Bird JSON response or error dict.
+    """
+    cmd = [
+        "node", str(_BIRD_SEARCH_MJS),
+        query,
+        "--count", str(count),
+        "--json",
+    ]
+
+    # Use process groups for clean cleanup on timeout/kill
+    preexec = os.setsid if hasattr(os, 'setsid') else None
+
+    try:
+        proc = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            encoding="utf-8",
+            errors="replace",
+            preexec_fn=preexec,
+            env=_subprocess_env(),
+        )
+
+        # Register for cleanup tracking (if available)
+        try:
+            from last30days import register_child_pid, unregister_child_pid
+            register_child_pid(proc.pid)
+        except ImportError:
+            pass
+
+        try:
+            stdout, stderr = proc.communicate(timeout=timeout)
+        except subprocess.TimeoutExpired:
+            # Kill the entire process group
+            try:
+                os.killpg(os.getpgid(proc.pid), signal.SIGTERM)
+            except (ProcessLookupError, PermissionError, OSError):
+                proc.kill()
+            proc.wait(timeout=5)
+            return {"error": f"Search timed out after {timeout}s", "items": []}
+        finally:
+            try:
+                from last30days import unregister_child_pid
+                unregister_child_pid(proc.pid)
+            except Exception:
+                pass
+
+        if proc.returncode != 0:
+            error = stderr.strip() if stderr else "Bird search failed"
+            return {"error": error, "items": []}
+
+        output = stdout.strip() if stdout else ""
+        if not output:
+            return {"items": []}
+
+        parsed = json.loads(output)
+        if isinstance(parsed, list):
+            return {"items": parsed}
+        return parsed
+
+    except json.JSONDecodeError as e:
+        return {"error": f"Invalid JSON response: {e}", "items": []}
+    except Exception as e:
+        return {"error": str(e), "items": []}
+
+
+def search_x(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+) -> Dict[str, Any]:
+    """Search X using Bird CLI with automatic retry on 0 results.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD) - unused but kept for API compatibility
+        depth: Research depth - "quick", "default", or "deep"
+
+    Returns:
+        Raw Bird JSON response or error dict.
+    """
+    count = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    timeout = 30 if depth == "quick" else 45 if depth == "default" else 60
+
+    # Extract core subject - X search is literal, not semantic
+    core_topic = _extract_core_subject(topic)
+    query = f"{core_topic} since:{from_date}"
+
+    _log(f"Searching: {query}")
+    response = _run_bird_search(query, count, timeout)
+
+    # Check if we got results
+    items = parse_bird_response(response, query=core_topic)
+
+    # Retry with OR groups for multi-word queries (X supports OR operator)
+    core_words = core_topic.split()
+    if not items and len(core_words) >= 2:
+        from .query import extract_compound_terms
+        compounds = extract_compound_terms(topic)
+        if compounds:
+            # Build OR-group query: ("multi-agent" OR "agent simulation") since:DATE
+            or_parts = ' OR '.join(f'"{t}"' for t in compounds[:3])
+            _log(f"0 results for '{core_topic}', retrying with OR groups: {or_parts}")
+            query = f"({or_parts}) since:{from_date}"
+            response = _run_bird_search(query, count, timeout)
+            items = parse_bird_response(response, query=core_topic)
+
+    # Retry with fewer keywords if still 0 results and query has 3+ words
+    if not items and len(core_words) > 2:
+        shorter = ' '.join(core_words[:2])
+        _log(f"0 results for '{core_topic}', retrying with '{shorter}'")
+        query = f"{shorter} since:{from_date}"
+        response = _run_bird_search(query, count, timeout)
+        items = parse_bird_response(response, query=core_topic)
+
+    # Last-chance retry: use strongest remaining token (often the product name)
+    if not items and core_words:
+        low_signal = {
+            'trendiest', 'trending', 'hottest', 'hot', 'popular', 'viral',
+            'best', 'top', 'latest', 'new', 'plugin', 'plugins',
+            'skill', 'skills', 'tool', 'tools',
+        }
+        candidates = [w for w in core_words if w not in low_signal]
+        if candidates:
+            strongest = max(candidates, key=len)
+            _log(f"0 results for '{core_topic}', retrying with strongest token '{strongest}'")
+            query = f"{strongest} since:{from_date}"
+            response = _run_bird_search(query, count, timeout)
+
+    return response
+
+
+def search_handles(
+    handles: List[str],
+    topic: Optional[str],
+    from_date: str,
+    count_per: int = 5,
+) -> List[Dict[str, Any]]:
+    """Search specific X handles for topic-related content.
+
+    Runs targeted Bird searches using `from:handle topic` syntax.
+    Used in Phase 2 supplemental search after entity extraction.
+
+    Args:
+        handles: List of X handles to search (without @)
+        topic: Search topic (core subject), or None for unfiltered search
+        from_date: Start date (YYYY-MM-DD)
+        count_per: Results to request per handle
+
+    Returns:
+        List of raw item dicts (same format as parse_bird_response output).
+    """
+    core_topic = _extract_core_subject(topic) if topic else None
+
+    def _search_one_handle(handle: str) -> List[Dict[str, Any]]:
+        handle = handle.lstrip("@")
+        if core_topic:
+            query = f"from:{handle} {core_topic} since:{from_date}"
+        else:
+            query = f"from:{handle} since:{from_date}"
+
+        cmd = [
+            "node", str(_BIRD_SEARCH_MJS),
+            query,
+            "--count", str(count_per),
+            "--json",
+        ]
+
+        preexec = os.setsid if hasattr(os, 'setsid') else None
+
+        try:
+            proc = subprocess.Popen(
+                cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                text=True,
+                encoding="utf-8",
+                errors="replace",
+                preexec_fn=preexec,
+                env=_subprocess_env(),
+            )
+
+            try:
+                stdout, stderr = proc.communicate(timeout=15)
+            except subprocess.TimeoutExpired:
+                try:
+                    os.killpg(os.getpgid(proc.pid), signal.SIGTERM)
+                except (ProcessLookupError, PermissionError, OSError):
+                    proc.kill()
+                proc.wait(timeout=5)
+                _log(f"Handle search timed out for @{handle}")
+                return []
+
+            if proc.returncode != 0:
+                _log(f"Handle search failed for @{handle}: {(stderr or '').strip()}")
+                return []
+
+            output = (stdout or "").strip()
+            if not output:
+                return []
+
+            response = json.loads(output)
+            return parse_bird_response(response, query=core_topic)
+
+        except json.JSONDecodeError:
+            _log(f"Invalid JSON from handle search for @{handle}")
+        except (OSError, subprocess.SubprocessError) as e:
+            _log(f"Handle search error for @{handle}: {e}")
+        return []
+
+    from concurrent.futures import ThreadPoolExecutor, as_completed
+
+    all_items: List[Dict[str, Any]] = []
+    with ThreadPoolExecutor(max_workers=min(5, len(handles))) as executor:
+        futures = {executor.submit(_search_one_handle, h): h for h in handles}
+        for future in as_completed(futures):
+            all_items.extend(future.result())
+
+    return all_items
+
+
+def parse_bird_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
+    """Parse Bird response to match xai_x output format.
+
+    Args:
+        response: Raw Bird JSON response
+        query: Original search query for relevance scoring
+
+    Returns:
+        List of normalized item dicts matching xai_x.parse_x_response() format.
+    """
+    items = []
+
+    # Check for errors
+    if "error" in response and response["error"]:
+        _log(f"Bird error: {response['error']}")
+        return items
+
+    # Bird returns a list of tweets directly or under a key
+    raw_items = response if isinstance(response, list) else response.get("items", response.get("tweets", []))
+
+    if not isinstance(raw_items, list):
+        return items
+
+    for i, tweet in enumerate(raw_items):
+        if not isinstance(tweet, dict):
+            continue
+
+        # Extract URL - Bird uses permanent_url or we construct from id
+        url = tweet.get("permanent_url") or tweet.get("url", "")
+        if not url and tweet.get("id"):
+            # Try different field structures Bird might use
+            author = tweet.get("author", {}) or tweet.get("user", {})
+            screen_name = author.get("username") or author.get("screen_name", "")
+            if screen_name:
+                url = f"https://x.com/{screen_name}/status/{tweet['id']}"
+
+        if not url:
+            continue
+
+        # Parse date from created_at/createdAt (e.g., "Wed Jan 15 14:30:00 +0000 2026")
+        date = None
+        created_at = tweet.get("createdAt") or tweet.get("created_at", "")
+        if created_at:
+            try:
+                # Try ISO format first (e.g., "2026-02-03T22:33:32Z")
+                # Check for ISO date separator, not just "T" (which appears in "Tue")
+                if len(created_at) > 10 and created_at[10] == "T":
+                    dt = datetime.fromisoformat(created_at.replace("Z", "+00:00"))
+                else:
+                    # Twitter format: "Wed Jan 15 14:30:00 +0000 2026"
+                    dt = datetime.strptime(created_at, "%a %b %d %H:%M:%S %z %Y")
+                date = dt.strftime("%Y-%m-%d")
+            except (ValueError, TypeError):
+                pass
+
+        # Extract user info (Bird uses author.username, older format uses user.screen_name)
+        author = tweet.get("author", {}) or tweet.get("user", {})
+        author_handle = author.get("username") or author.get("screen_name", "") or tweet.get("author_handle", "")
+
+        # Build engagement dict (Bird uses camelCase: likeCount, retweetCount, etc.)
+        engagement = {
+            "likes": _first_of(tweet.get("likeCount"), tweet.get("like_count"), tweet.get("favorite_count")),
+            "reposts": _first_of(tweet.get("retweetCount"), tweet.get("retweet_count")),
+            "replies": _first_of(tweet.get("replyCount"), tweet.get("reply_count")),
+            "quotes": _first_of(tweet.get("quoteCount"), tweet.get("quote_count")),
+        }
+        # Convert to int where possible
+        for key in engagement:
+            if engagement[key] is not None:
+                try:
+                    engagement[key] = int(engagement[key])
+                except (ValueError, TypeError):
+                    engagement[key] = None
+
+        # Build normalized item
+        item = {
+            "id": f"X{i+1}",
+            "text": str(tweet.get("text", tweet.get("full_text", ""))).strip()[:500],
+            "url": url,
+            "author_handle": author_handle.lstrip("@"),
+            "date": date,
+            "engagement": engagement if any(v is not None for v in engagement.values()) else None,
+            "why_relevant": "",  # Bird doesn't provide relevance explanations
+            "relevance": _compute_relevance(query, str(tweet.get("text", ""))) if query else 0.7,
+        }
+
+        items.append(item)
+
+    return items

scripts/lib/bluesky.py

@@ -0,0 +1,249 @@
+"""Bluesky search via AT Protocol (requires app password).
+
+Uses bsky.social for auth and public.api.bsky.app for post search.
+Requires BSKY_HANDLE and BSKY_APP_PASSWORD env vars.
+"""
+
+import math
+import re
+import sys
+import time
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+from . import http, log
+
+BSKY_SESSION_URL = "https://bsky.social/xrpc/com.atproto.server.createSession"
+BSKY_SEARCH_URL = "https://public.api.bsky.app/xrpc/app.bsky.feed.searchPosts"
+
+DEPTH_CONFIG = {
+    "quick": 15,
+    "default": 30,
+    "deep": 60,
+}
+
+# Module-level token cache (valid for the lifetime of a single research run)
+_cached_token: Optional[str] = None
+_token_created_at: float = 0.0
+_session_error: Optional[str] = None
+_TOKEN_MAX_AGE_SECONDS = 5400  # 90 minutes (conservative, tokens last ~2 hours)
+
+
+def _log(msg: str):
+    log.source_log("Bluesky", msg)
+
+
+def _create_session(handle: str, app_password: str) -> Optional[str]:
+    """Create an AT Protocol session and return the access token.
+
+    Args:
+        handle: Bluesky handle (e.g. user.bsky.social)
+        app_password: App password from bsky.app/settings/app-passwords
+
+    Returns:
+        Access JWT string, or None on failure. Sets _session_error on failure.
+    """
+    global _cached_token, _token_created_at, _session_error
+    if _cached_token and (time.monotonic() - _token_created_at < _TOKEN_MAX_AGE_SECONDS):
+        return _cached_token
+    if _cached_token:
+        _log("Session token expired, re-authenticating")
+        _cached_token = None
+        _token_created_at = 0.0
+
+    try:
+        response = http.request(
+            "POST",
+            BSKY_SESSION_URL,
+            json_data={"identifier": handle, "password": app_password},
+            timeout=15,
+        )
+        token = response.get("accessJwt")
+        if token:
+            _cached_token = token
+            _token_created_at = time.monotonic()
+            _session_error = None
+            _log("Session created successfully")
+            return token
+        _log("No accessJwt in session response")
+        _session_error = "No accessJwt in session response"
+        return None
+    except http.HTTPError as e:
+        if e.status_code == 403 and e.body and "cloudflare" in e.body.lower():
+            _session_error = "Cloudflare blocked the request (403 Forbidden). This is a network-level block, not an auth issue. Try a different network or VPN."
+        elif e.status_code == 401:
+            _session_error = "Invalid credentials (401 Unauthorized). Check BSKY_HANDLE and BSKY_APP_PASSWORD."
+        else:
+            _session_error = f"Session request failed: {e}"
+        _log(f"Session creation failed: {_session_error}")
+        return None
+    except Exception as e:
+        _session_error = f"Session request failed: {type(e).__name__}: {e}"
+        _log(f"Session creation failed: {_session_error}")
+        return None
+
+
+def _reset_session_cache() -> None:
+    global _cached_token, _token_created_at, _session_error
+    _cached_token = None
+    _token_created_at = 0.0
+    _session_error = None
+
+
+def _extract_core_subject(topic: str) -> str:
+    """Extract core subject from verbose query for Bluesky search."""
+    from .query import extract_core_subject
+    _BSKY_NOISE = frozenset({
+        'best', 'top', 'good', 'great', 'awesome',
+        'latest', 'new', 'news', 'update', 'updates',
+        'trending', 'hottest', 'popular', 'viral',
+        'practices', 'features', 'recommendations', 'advice',
+    })
+    return extract_core_subject(topic, noise=_BSKY_NOISE)
+
+
+def _parse_date(item: Dict[str, Any]) -> Optional[str]:
+    """Parse date from Bluesky post to YYYY-MM-DD.
+
+    AT Protocol uses ISO 8601 format in indexedAt and createdAt fields.
+    """
+    for key in ("indexedAt", "createdAt"):
+        val = item.get(key)
+        if val and isinstance(val, str):
+            try:
+                dt = datetime.fromisoformat(val.replace("Z", "+00:00"))
+                return dt.strftime("%Y-%m-%d")
+            except (ValueError, TypeError):
+                pass
+    return None
+
+
+def search_bluesky(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    config: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    """Search Bluesky via AT Protocol API.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        config: Config dict with BSKY_HANDLE and BSKY_APP_PASSWORD
+
+    Returns:
+        Dict with 'posts' list from AT Protocol response.
+    """
+    config = config or {}
+    handle = config.get("BSKY_HANDLE", "")
+    app_password = config.get("BSKY_APP_PASSWORD", "")
+
+    if not handle or not app_password:
+        return {"posts": [], "error": "Bluesky credentials not configured"}
+
+    count = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    core_topic = _extract_core_subject(topic)
+
+    _log(f"Searching for '{core_topic}' (depth={depth}, limit={count})")
+
+    from urllib.parse import urlencode
+    params = {
+        "q": core_topic,
+        "limit": str(min(count, 100)),
+        "sort": "top",
+    }
+    url = f"{BSKY_SEARCH_URL}?{urlencode(params)}"
+
+    def _auth_and_search() -> tuple[Optional[Dict[str, Any]], Optional[str]]:
+        token = _create_session(handle, app_password)
+        if not token:
+            error_msg = _session_error or "Bluesky session creation failed (unknown error)"
+            return None, error_msg
+        try:
+            response = http.request(
+                "GET", url,
+                headers={"Authorization": f"Bearer {token}"},
+                timeout=30,
+            )
+            return response, None
+        except http.HTTPError as e:
+            _log(f"Search failed: {e}")
+            if e.status_code == 401:
+                _reset_session_cache()
+                return None, "refresh"
+            if e.status_code == 403 and e.body and "cloudflare" in e.body.lower():
+                return None, "Bluesky search blocked by Cloudflare (403). This is a network-level block - try a different network or VPN."
+            return None, f"Bluesky search failed: {e}"
+        except Exception as e:
+            _log(f"Search failed: {e}")
+            return None, f"Bluesky search failed: {type(e).__name__}: {e}"
+
+    response, error_msg = _auth_and_search()
+    if error_msg == "refresh":
+        _log("Session expired; recreating token and retrying once")
+        response, error_msg = _auth_and_search()
+    if error_msg:
+        return {"posts": [], "error": error_msg}
+    if response is None:
+        return {"posts": [], "error": "Bluesky search failed (unknown error)"}
+
+    posts = response.get("posts", [])
+    _log(f"Found {len(posts)} posts")
+    return response
+
+
+def parse_bluesky_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse AT Protocol response into normalized item dicts.
+
+    Returns:
+        List of item dicts ready for normalization.
+    """
+    posts = response.get("posts", [])
+    items = []
+
+    for i, post in enumerate(posts):
+        record = post.get("record") or {}
+        text = record.get("text") or ""
+
+        author = post.get("author") or {}
+        handle = author.get("handle") or ""
+        display_name = author.get("displayName") or handle
+
+        # Post URI -> URL
+        # URI format: at://did:plc:xxx/app.bsky.feed.post/rkey
+        uri = post.get("uri") or ""
+        rkey = uri.rsplit("/", 1)[-1] if uri else ""
+        url = f"https://bsky.app/profile/{handle}/post/{rkey}" if handle and rkey else ""
+
+        likes = post.get("likeCount") or 0
+        reposts = post.get("repostCount") or 0
+        replies = post.get("replyCount") or 0
+        quotes = post.get("quoteCount") or 0
+
+        date_str = _parse_date(post) or _parse_date(record)
+
+        # Relevance: position-based (AT Protocol sorts by relevance with sort=top)
+        rank_score = max(0.3, 1.0 - (i * 0.02))
+        engagement_boost = min(0.2, math.log1p(likes + reposts) / 40)
+        relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
+
+        items.append({
+            "handle": handle,
+            "display_name": display_name,
+            "text": text,
+            "url": url,
+            "date": date_str,
+            "engagement": {
+                "likes": likes,
+                "reposts": reposts,
+                "replies": replies,
+                "quotes": quotes,
+            },
+            "relevance": round(relevance, 2),
+            "why_relevant": f"Bluesky: @{handle}: {text[:60]}" if text else f"Bluesky: {handle}",
+        })
+
+    return items

scripts/lib/cache.py

@@ -1,152 +0,0 @@
-"""Caching utilities for last30days skill."""
-
-import hashlib
-import json
-import os
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Optional
-
-CACHE_DIR = Path.home() / ".cache" / "last30days"
-DEFAULT_TTL_HOURS = 24
-MODEL_CACHE_TTL_DAYS = 7
-
-
-def ensure_cache_dir():
-    """Ensure cache directory exists."""
-    CACHE_DIR.mkdir(parents=True, exist_ok=True)
-
-
-def get_cache_key(topic: str, from_date: str, to_date: str, sources: str) -> str:
-    """Generate a cache key from query parameters."""
-    key_data = f"{topic}|{from_date}|{to_date}|{sources}"
-    return hashlib.sha256(key_data.encode()).hexdigest()[:16]
-
-
-def get_cache_path(cache_key: str) -> Path:
-    """Get path to cache file."""
-    return CACHE_DIR / f"{cache_key}.json"
-
-
-def is_cache_valid(cache_path: Path, ttl_hours: int = DEFAULT_TTL_HOURS) -> bool:
-    """Check if cache file exists and is within TTL."""
-    if not cache_path.exists():
-        return False
-
-    try:
-        stat = cache_path.stat()
-        mtime = datetime.fromtimestamp(stat.st_mtime, tz=timezone.utc)
-        now = datetime.now(timezone.utc)
-        age_hours = (now - mtime).total_seconds() / 3600
-        return age_hours < ttl_hours
-    except OSError:
-        return False
-
-
-def load_cache(cache_key: str, ttl_hours: int = DEFAULT_TTL_HOURS) -> Optional[dict]:
-    """Load data from cache if valid."""
-    cache_path = get_cache_path(cache_key)
-
-    if not is_cache_valid(cache_path, ttl_hours):
-        return None
-
-    try:
-        with open(cache_path, 'r') as f:
-            return json.load(f)
-    except (json.JSONDecodeError, OSError):
-        return None
-
-
-def get_cache_age_hours(cache_path: Path) -> Optional[float]:
-    """Get age of cache file in hours."""
-    if not cache_path.exists():
-        return None
-    try:
-        stat = cache_path.stat()
-        mtime = datetime.fromtimestamp(stat.st_mtime, tz=timezone.utc)
-        now = datetime.now(timezone.utc)
-        return (now - mtime).total_seconds() / 3600
-    except OSError:
-        return None
-
-
-def load_cache_with_age(cache_key: str, ttl_hours: int = DEFAULT_TTL_HOURS) -> tuple:
-    """Load data from cache with age info.
-
-    Returns:
-        Tuple of (data, age_hours) or (None, None) if invalid
-    """
-    cache_path = get_cache_path(cache_key)
-
-    if not is_cache_valid(cache_path, ttl_hours):
-        return None, None
-
-    age = get_cache_age_hours(cache_path)
-
-    try:
-        with open(cache_path, 'r') as f:
-            return json.load(f), age
-    except (json.JSONDecodeError, OSError):
-        return None, None
-
-
-def save_cache(cache_key: str, data: dict):
-    """Save data to cache."""
-    ensure_cache_dir()
-    cache_path = get_cache_path(cache_key)
-
-    try:
-        with open(cache_path, 'w') as f:
-            json.dump(data, f)
-    except OSError:
-        pass  # Silently fail on cache write errors
-
-
-def clear_cache():
-    """Clear all cache files."""
-    if CACHE_DIR.exists():
-        for f in CACHE_DIR.glob("*.json"):
-            try:
-                f.unlink()
-            except OSError:
-                pass
-
-
-# Model selection cache (longer TTL)
-MODEL_CACHE_FILE = CACHE_DIR / "model_selection.json"
-
-
-def load_model_cache() -> dict:
-    """Load model selection cache."""
-    if not is_cache_valid(MODEL_CACHE_FILE, MODEL_CACHE_TTL_DAYS * 24):
-        return {}
-
-    try:
-        with open(MODEL_CACHE_FILE, 'r') as f:
-            return json.load(f)
-    except (json.JSONDecodeError, OSError):
-        return {}
-
-
-def save_model_cache(data: dict):
-    """Save model selection cache."""
-    ensure_cache_dir()
-    try:
-        with open(MODEL_CACHE_FILE, 'w') as f:
-            json.dump(data, f)
-    except OSError:
-        pass
-
-
-def get_cached_model(provider: str) -> Optional[str]:
-    """Get cached model selection for a provider."""
-    cache = load_model_cache()
-    return cache.get(provider)
-
-
-def set_cached_model(provider: str, model: str):
-    """Cache model selection for a provider."""
-    cache = load_model_cache()
-    cache[provider] = model
-    cache['updated_at'] = datetime.now(timezone.utc).isoformat()
-    save_model_cache(cache)

scripts/lib/categories.py

@@ -0,0 +1,283 @@
+"""Category-peer subreddit map for Step 0.55 community resolution.
+
+When a topic is a product in a known category (AI image generation, AI coding
+agents, SaaS screen recording, etc.), brand-specific subreddits returned by
+WebSearch are insufficient: cross-product technique discussion lives in
+category-peer subs. This module classifies a topic into a category by matching
+compound-term patterns against the lowercased topic string, then returns the
+priority-ordered peer subreddit list for that category.
+
+The map is intentionally small, curated, and code-reviewed. Adding a new
+category is a code change; there is no user-editable override surface.
+
+False-positive guard: every pattern is either a multi-word compound (e.g.
+"image generation", "text to image") or a domain-specific single word
+(e.g. "midjourney", "stablediffusion"). Bare common nouns like "image",
+"ai", or "model" are never used as patterns.
+
+First-match-wins: categories are evaluated in declared order. Entries are
+sorted from most-specific to least-specific so narrower categories claim a
+topic before broader ones. For example, `ai_image_generation` appears
+before `ai_chat_model` so "gpt image 2" matches the image-gen category.
+"""
+
+from __future__ import annotations
+
+from typing import List, Optional, TypedDict
+
+
+class _CategoryEntry(TypedDict):
+    patterns: List[str]
+    peer_subs: List[str]
+
+
+CATEGORY_PEERS: dict[str, _CategoryEntry] = {
+    "ai_image_generation": {
+        "patterns": [
+            "image generation",
+            "image gen",
+            "text to image",
+            "text-to-image",
+            "gpt image",
+            "gpt-image",
+            "nano banana",
+            "midjourney",
+            "stable diffusion",
+            "stablediffusion",
+            "dall-e",
+            "dalle",
+            "flux.1",
+            "flux schnell",
+            "imagen",
+            "seedance",
+            "ideogram",
+            "recraft",
+        ],
+        "peer_subs": [
+            "StableDiffusion",
+            "midjourney",
+            "dalle2",
+            "aiArt",
+            "PromptEngineering",
+            "MediaSynthesis",
+        ],
+    },
+    "ai_video_generation": {
+        "patterns": [
+            "video generation",
+            "text to video",
+            "text-to-video",
+            "sora",
+            "veo 3",
+            "veo3",
+            "runway gen",
+            "kling",
+            "pika labs",
+            "luma dream machine",
+            "hailuo",
+        ],
+        "peer_subs": [
+            "aivideo",
+            "StableDiffusion",
+            "runwayml",
+            "singularity",
+            "MediaSynthesis",
+        ],
+    },
+    "ai_music_generation": {
+        "patterns": [
+            "music generation",
+            "ai music",
+            "suno",
+            "udio",
+            "riffusion",
+            "stable audio",
+        ],
+        "peer_subs": [
+            "SunoAI",
+            "udiomusic",
+            "aimusic",
+            "artificial",
+        ],
+    },
+    "ai_coding_agent": {
+        "patterns": [
+            "claude code",
+            "cursor ide",
+            "github copilot",
+            "windsurf",
+            "aider",
+            "cline",
+            "openclaw",
+            "hermes agent",
+            "continue.dev",
+            "codeium",
+            "sweep ai",
+            "devin ai",
+            "coding agent",
+            "coding assistant",
+        ],
+        "peer_subs": [
+            "ChatGPTCoding",
+            "LocalLLaMA",
+            "singularity",
+            "PromptEngineering",
+        ],
+    },
+    "ai_agent_framework": {
+        "patterns": [
+            "agent framework",
+            "agentic framework",
+            "langchain",
+            "langgraph",
+            "crewai",
+            "autogen",
+            "llamaindex",
+            "dspy",
+            "smolagents",
+        ],
+        "peer_subs": [
+            "LangChain",
+            "LocalLLaMA",
+            "AI_Agents",
+            "MachineLearning",
+        ],
+    },
+    "ai_chat_model": {
+        "patterns": [
+            "gpt-5",
+            "gpt-4",
+            "claude opus",
+            "claude sonnet",
+            "claude haiku",
+            "gemini pro",
+            "gemini flash",
+            "llama 3",
+            "llama 4",
+            "deepseek",
+            "qwen",
+            "mistral large",
+            "grok",
+        ],
+        "peer_subs": [
+            "LocalLLaMA",
+            "ChatGPT",
+            "ClaudeAI",
+            "singularity",
+            "artificial",
+        ],
+    },
+    "saas_screen_recording": {
+        "patterns": [
+            "screen recording",
+            "screen recorder",
+            "loom video",
+            "tella screen",
+            "vidyard",
+            "screen capture tool",
+        ],
+        "peer_subs": [
+            "SaaS",
+            "screenrecording",
+            "productivity",
+            "Entrepreneur",
+        ],
+    },
+    "saas_productivity": {
+        "patterns": [
+            "notion app",
+            "obsidian plugin",
+            "obsidian app",
+            "linear app",
+            "asana",
+            "clickup",
+            "productivity app",
+        ],
+        "peer_subs": [
+            "productivity",
+            "SaaS",
+            "ObsidianMD",
+            "Notion",
+        ],
+    },
+    "prediction_markets": {
+        "patterns": [
+            "polymarket",
+            "kalshi",
+            "prediction market",
+            "event contracts",
+            "manifold markets",
+        ],
+        "peer_subs": [
+            "Polymarket",
+            "Kalshi",
+            "predictionmarkets",
+        ],
+    },
+    "crypto_defi": {
+        "patterns": [
+            "defi protocol",
+            "yield farming",
+            "liquidity pool",
+            "stablecoin",
+            "ethereum layer",
+            "layer 2",
+            "l2 rollup",
+        ],
+        "peer_subs": [
+            "defi",
+            "ethfinance",
+            "CryptoCurrency",
+            "ethereum",
+        ],
+    },
+    "dev_tool_cli": {
+        "patterns": [
+            "cli tool",
+            "command line tool",
+            "terminal app",
+            "dev tool",
+        ],
+        "peer_subs": [
+            "commandline",
+            "programming",
+            "webdev",
+        ],
+    },
+}
+
+
+def detect_category(topic: Optional[str]) -> Optional[str]:
+    """Classify a topic into a known category by compound-term match.
+
+    Returns the category id (e.g. "ai_image_generation") or None if no
+    category's patterns match. Matching is case-insensitive substring over
+    the lowercased topic. Declaration order wins (first-match-wins), so the
+    map is ordered from most-specific to least-specific.
+
+    A None or empty topic returns None. Classification never raises on
+    normal string inputs; callers do not need to wrap in try/except for
+    typical paths, though defensive callers may.
+    """
+    if not topic:
+        return None
+    lowered = topic.lower()
+    for category_id, entry in CATEGORY_PEERS.items():
+        for pattern in entry["patterns"]:
+            if pattern in lowered:
+                return category_id
+    return None
+
+
+def peer_subs_for(category_id: Optional[str]) -> List[str]:
+    """Return the priority-ordered peer subreddit list for a category.
+
+    Returns an empty list for None or unknown category ids. The returned
+    list is a fresh copy; callers may safely mutate it.
+    """
+    if not category_id:
+        return []
+    entry = CATEGORY_PEERS.get(category_id)
+    if not entry:
+        return []
+    return list(entry["peer_subs"])

scripts/lib/chrome_cookies.py

@@ -0,0 +1,265 @@
+"""Chrome cookie extraction for macOS.
+
+Extracts cookies from Chrome's encrypted SQLite database using only stdlib
+modules and the system openssl CLI (ships with macOS). Zero pip dependencies.
+
+Chrome on macOS uses v10 encryption (AES-128-CBC with Keychain-stored key).
+This is NOT affected by Windows App-Bound Encryption (v20).
+"""
+
+import hashlib
+import logging
+import shutil
+import sqlite3
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# Chrome cookie DB location on macOS
+CHROME_COOKIES_DB = Path.home() / "Library" / "Application Support" / "Google" / "Chrome" / "Default" / "Cookies"
+
+# Chrome v10 encryption constants
+CHROME_SALT = b"saltysalt"
+CHROME_PBKDF2_ITERATIONS = 1003
+CHROME_KEY_LENGTH = 16
+# IV is 16 space characters (0x20)
+CHROME_IV_HEX = "20" * 16
+
+
+def _get_chrome_encryption_key() -> Optional[bytes]:
+    """Retrieve Chrome's encryption passphrase from macOS Keychain.
+
+    Calls `security find-generic-password` which may trigger a system dialog
+    on first access.
+
+    Returns the raw passphrase bytes, or None on failure.
+    """
+    try:
+        result = subprocess.run(
+            ["security", "find-generic-password", "-w", "-s", "Chrome Safe Storage"],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        if result.returncode != 0:
+            logger.info("Chrome Keychain access denied or Chrome not installed: %s", result.stderr.strip())
+            return None
+        passphrase = result.stdout.strip()
+        if not passphrase:
+            logger.info("Chrome Keychain returned empty passphrase")
+            return None
+        return passphrase.encode("utf-8")
+    except FileNotFoundError:
+        logger.info("'security' command not found — not on macOS?")
+        return None
+    except subprocess.TimeoutExpired:
+        logger.info("Chrome Keychain access timed out")
+        return None
+    except Exception as e:
+        logger.info("Failed to get Chrome encryption key: %s", e)
+        return None
+
+
+def _derive_aes_key(passphrase: bytes) -> bytes:
+    """Derive 16-byte AES key from Chrome's Keychain passphrase via PBKDF2."""
+    return hashlib.pbkdf2_hmac(
+        "sha1",
+        passphrase,
+        CHROME_SALT,
+        CHROME_PBKDF2_ITERATIONS,
+        dklen=CHROME_KEY_LENGTH,
+    )
+
+
+def _decrypt_v10_value(encrypted_value: bytes, aes_key: bytes, db_version: int) -> Optional[str]:
+    """Decrypt a Chrome v10-encrypted cookie value.
+
+    Uses system openssl CLI for AES-128-CBC decryption (zero pip deps).
+    For Chrome 130+ (db_version >= 24), strips 32-byte SHA-256 prefix after decryption.
+
+    Returns decrypted string or None on failure.
+    """
+    # Strip the 'v10' prefix
+    ciphertext = encrypted_value[3:]
+    if not ciphertext:
+        return None
+
+    hex_key = aes_key.hex()
+
+    try:
+        result = subprocess.run(
+            [
+                "openssl", "enc", "-aes-128-cbc", "-d",
+                "-K", hex_key,
+                "-iv", CHROME_IV_HEX,
+                "-nopad",
+            ],
+            input=ciphertext,
+            capture_output=True,
+            timeout=5,
+        )
+        if result.returncode != 0:
+            logger.debug("openssl decryption failed: %s", result.stderr.decode(errors="replace").strip())
+            return None
+
+        decrypted = result.stdout
+        if not decrypted:
+            return None
+
+        # Remove PKCS7 padding
+        decrypted = _remove_pkcs7_padding(decrypted)
+        if decrypted is None:
+            return None
+
+        # Chrome 130+ (db version >= 24): strip 32-byte SHA-256 prefix
+        if db_version >= 24 and len(decrypted) > 32:
+            decrypted = decrypted[32:]
+
+        return decrypted.decode("utf-8", errors="replace")
+
+    except FileNotFoundError:
+        logger.info("openssl not found — cannot decrypt Chrome cookies")
+        return None
+    except subprocess.TimeoutExpired:
+        logger.info("openssl decryption timed out")
+        return None
+    except Exception as e:
+        logger.debug("Chrome cookie decryption error: %s", e)
+        return None
+
+
+def _remove_pkcs7_padding(data: bytes) -> Optional[bytes]:
+    """Remove PKCS7 padding from decrypted data.
+
+    The last byte indicates the number of padding bytes added.
+    All padding bytes must have the same value.
+
+    Returns unpadded data or None if padding is invalid.
+    """
+    if not data:
+        return None
+    pad_len = data[-1]
+    if pad_len < 1 or pad_len > 16:
+        return None
+    # Verify all padding bytes match
+    if data[-pad_len:] != bytes([pad_len]) * pad_len:
+        return None
+    return data[:-pad_len]
+
+
+def _get_db_version(cursor: sqlite3.Cursor) -> int:
+    """Get Chrome cookie database version from the meta table.
+
+    Returns 0 if meta table doesn't exist or version can't be read.
+    """
+    try:
+        cursor.execute("SELECT value FROM meta WHERE key = 'version'")
+        row = cursor.fetchone()
+        if row:
+            return int(row[0])
+    except Exception:
+        pass
+    return 0
+
+
+def extract_chrome_cookies_macos(domain: str, cookie_names: list[str]) -> Optional[dict[str, str]]:
+    """Extract cookies from Chrome on macOS.
+
+    Copies the locked Cookies database to a temp file, reads specified cookies,
+    and decrypts v10-encrypted values using the Keychain-stored key.
+
+    Args:
+        domain: Cookie domain to match (e.g., ".twitter.com", ".x.com")
+        cookie_names: List of cookie names to extract
+
+    Returns:
+        Dict mapping cookie name to decrypted value, or None on failure.
+        Only includes cookies that were successfully found and decrypted.
+    """
+    if not CHROME_COOKIES_DB.exists():
+        logger.info("Chrome cookies database not found at %s", CHROME_COOKIES_DB)
+        return None
+
+    # Get encryption key from Keychain
+    passphrase = _get_chrome_encryption_key()
+    aes_key = _derive_aes_key(passphrase) if passphrase else None
+
+    # Copy DB to temp file (Chrome locks the original)
+    tmp_fd = None
+    tmp_path = None
+    try:
+        tmp_fd, tmp_path = tempfile.mkstemp(suffix=".sqlite")
+        shutil.copy2(str(CHROME_COOKIES_DB), tmp_path)
+    except Exception as e:
+        logger.info("Failed to copy Chrome cookies database: %s", e)
+        if tmp_path:
+            try:
+                Path(tmp_path).unlink(missing_ok=True)
+            except Exception:
+                pass
+        return None
+    finally:
+        if tmp_fd is not None:
+            import os
+            os.close(tmp_fd)
+
+    try:
+        conn = sqlite3.connect(tmp_path)
+        cursor = conn.cursor()
+
+        db_version = _get_db_version(cursor)
+        logger.debug("Chrome cookie DB version: %d", db_version)
+
+        # Build query with placeholders for cookie names
+        placeholders = ",".join("?" for _ in cookie_names)
+        query = (
+            f"SELECT name, value, encrypted_value FROM cookies "
+            f"WHERE host_key LIKE ? AND name IN ({placeholders})"
+        )
+        # Use LIKE for domain matching (e.g., %.twitter.com matches .twitter.com)
+        params = [f"%{domain}"] + list(cookie_names)
+        cursor.execute(query, params)
+
+        results: dict[str, str] = {}
+        for name, value, encrypted_value in cursor.fetchall():
+            # Prefer unencrypted value if present
+            if value:
+                results[name] = value
+                continue
+
+            # Handle encrypted value
+            if encrypted_value and encrypted_value[:3] == b"v10":
+                if aes_key is None:
+                    logger.debug("Skipping encrypted cookie %s — no Keychain access", name)
+                    continue
+                decrypted = _decrypt_v10_value(encrypted_value, aes_key, db_version)
+                if decrypted:
+                    results[name] = decrypted
+                else:
+                    logger.debug("Failed to decrypt cookie %s", name)
+            elif encrypted_value:
+                # Unknown encryption version
+                logger.debug("Unknown encryption for cookie %s (prefix: %r)", name, encrypted_value[:3])
+
+        conn.close()
+
+        if not results:
+            logger.info("No matching cookies found in Chrome for domain %s", domain)
+            return None
+
+        return results
+
+    except sqlite3.Error as e:
+        logger.info("Failed to read Chrome cookies database: %s", e)
+        return None
+    except Exception as e:
+        logger.info("Unexpected error reading Chrome cookies: %s", e)
+        return None
+    finally:
+        try:
+            Path(tmp_path).unlink(missing_ok=True)
+        except Exception:
+            pass

scripts/lib/cluster.py

@@ -0,0 +1,271 @@
+"""Candidate clustering and representative selection."""
+
+from __future__ import annotations
+
+import re
+
+from . import dedupe, schema
+
+CLUSTERABLE_INTENTS = {"breaking_news", "opinion", "comparison", "prediction"}
+
+# Words too common to signal shared topic between clusters.
+_ENTITY_STOPWORDS = frozenset({
+    "the", "a", "an", "to", "for", "how", "is", "in", "of", "on", "and",
+    "with", "from", "by", "at", "this", "that", "it", "what", "are", "do",
+    "can", "his", "her", "he", "she", "its", "was", "has", "new", "just",
+    "says", "said", "will", "about", "after", "now", "all", "been", "here",
+    "not", "out", "up", "more", "also", "but", "who", "year", "first",
+    "make", "being", "making", "over", "into", "than", "they", "their",
+    "would", "could", "get", "got", "some", "like", "back", "going",
+    "breaking", "https", "http", "www", "com",
+})
+
+
+def _candidate_text(candidate: schema.Candidate) -> str:
+    return " ".join(part for part in [candidate.title, candidate.snippet] if part).strip()
+
+
+def _extract_entities(text: str) -> set[str]:
+    """Extract significant words (proper nouns, numbers, capitalized words) from text.
+
+    Used for cross-source cluster merging where phrasing differs but entities overlap.
+    """
+    # Normalize but preserve word boundaries
+    words = re.sub(r"[^\w\s]", " ", text).split()
+    entities = set()
+    for word in words:
+        lower = word.lower()
+        if lower in _ENTITY_STOPWORDS or len(word) <= 2:
+            continue
+        # Keep words that are: capitalized, ALL CAPS, contain digits, or 4+ chars
+        if word[0].isupper() or word.isupper() or any(c.isdigit() for c in word) or len(word) >= 4:
+            entities.add(lower)
+    return entities
+
+
+def _entity_overlap(entities_a: set[str], entities_b: set[str]) -> float:
+    """Jaccard-style overlap on extracted entities."""
+    if not entities_a or not entities_b:
+        return 0.0
+    intersection = entities_a & entities_b
+    smaller = min(len(entities_a), len(entities_b))
+    # Use overlap coefficient (intersection / min) instead of Jaccard,
+    # because a short tweet about the same event as a long Reddit post
+    # will have fewer total entities but high overlap with the larger set.
+    return len(intersection) / smaller if smaller > 0 else 0.0
+
+
+def _mmr_representatives(
+    candidates: list[schema.Candidate],
+    text_cache: dict[str, dedupe._PreparedText],
+    limit: int = 3,
+    diversity_lambda: float = 0.75,
+) -> list[str]:
+    selected: list[schema.Candidate] = []
+    remaining_set = {c.candidate_id for c in candidates}
+    remaining = list(candidates)
+    while remaining and len(selected) < limit:
+        if not selected:
+            best = max(remaining, key=lambda candidate: candidate.final_score)
+            selected.append(best)
+            remaining_set.discard(best.candidate_id)
+            remaining = [c for c in remaining if c.candidate_id in remaining_set]
+            continue
+
+        selected_preps = [text_cache[c.candidate_id] for c in selected]
+
+        def score(candidate: schema.Candidate) -> float:
+            prep = text_cache[candidate.candidate_id]
+            diversity_penalty = max(
+                dedupe.prepared_similarity(prep, sp) for sp in selected_preps
+            )
+            return (diversity_lambda * candidate.final_score) - ((1 - diversity_lambda) * diversity_penalty * 100)
+
+        best = max(remaining, key=score)
+        selected.append(best)
+        remaining_set.discard(best.candidate_id)
+        remaining = [c for c in remaining if c.candidate_id in remaining_set]
+    return [candidate.candidate_id for candidate in selected]
+
+
+def cluster_candidates(
+    candidates: list[schema.Candidate],
+    plan: schema.QueryPlan,
+) -> list[schema.Cluster]:
+    """Greedy clustering around high-ranked leaders."""
+    if plan.intent not in CLUSTERABLE_INTENTS or plan.cluster_mode == "none":
+        clusters = []
+        for index, candidate in enumerate(candidates, start=1):
+            cluster_id = f"cluster-{index}"
+            candidate.cluster_id = cluster_id
+            clusters.append(
+                schema.Cluster(
+                    cluster_id=cluster_id,
+                    title=candidate.title,
+                    candidate_ids=[candidate.candidate_id],
+                    representative_ids=[candidate.candidate_id],
+                    sources=sorted(schema.candidate_sources(candidate)),
+                    score=candidate.final_score,
+                    uncertainty=None,
+                )
+            )
+        return clusters
+
+    text_cache: dict[str, dedupe._PreparedText] = {
+        c.candidate_id: dedupe._PreparedText(_candidate_text(c))
+        for c in candidates
+    }
+
+    groups: list[list[schema.Candidate]] = []
+    # Lower threshold for breaking_news: related articles share fewer exact
+    # words but cover the same event.
+    threshold = 0.42 if plan.intent == "breaking_news" else 0.48
+    for candidate in candidates:
+        assigned = False
+        cand_prep = text_cache[candidate.candidate_id]
+        for group in groups:
+            leader = group[0]
+            similarity = dedupe.prepared_similarity(cand_prep, text_cache[leader.candidate_id])
+            if similarity >= threshold:
+                group.append(candidate)
+                assigned = True
+                break
+        if not assigned:
+            groups.append([candidate])
+
+    clusters: list[schema.Cluster] = []
+    for index, group in enumerate(groups, start=1):
+        group.sort(key=lambda candidate: candidate.final_score, reverse=True)
+        cluster_id = f"cluster-{index}"
+        representatives = _mmr_representatives(group, text_cache)
+        for candidate in group:
+            candidate.cluster_id = cluster_id
+        clusters.append(
+            schema.Cluster(
+                cluster_id=cluster_id,
+                title=group[0].title,
+                candidate_ids=[candidate.candidate_id for candidate in group],
+                representative_ids=representatives,
+                sources=sorted({source for candidate in group for source in schema.candidate_sources(candidate)}),
+                score=max(candidate.final_score for candidate in group),
+                uncertainty=_cluster_uncertainty(group),
+            )
+        )
+
+    # Second pass: merge small clusters that share entities across sources.
+    clusters = _merge_entity_clusters(clusters, candidates)
+
+    return sorted(clusters, key=lambda cluster: cluster.score, reverse=True)
+
+
+def _merge_entity_clusters(
+    clusters: list[schema.Cluster],
+    all_candidates: list[schema.Candidate],
+) -> list[schema.Cluster]:
+    """Merge small clusters that cover the same story across different sources.
+
+    The initial greedy pass uses text similarity which misses cross-source
+    matches where phrasing differs. This second pass looks at entity overlap
+    (proper nouns, names, numbers) to catch cases like:
+      - Reddit: "Kanye West to headline all three nights of Wireless Festival 2026"
+      - X: "BREAKING: Kanye West (Ye) is making his massive UK comeback!"
+    """
+    if len(clusters) < 2:
+        return clusters
+
+    candidate_map = {c.candidate_id: c for c in all_candidates}
+
+    # Build entity sets per cluster
+    cluster_entities: list[set[str]] = []
+    for cl in clusters:
+        entities: set[str] = set()
+        for cid in cl.candidate_ids:
+            cand = candidate_map.get(cid)
+            if cand:
+                entities |= _extract_entities(_candidate_text(cand))
+        cluster_entities.append(entities)
+
+    # Only merge clusters with <= 3 items (don't merge already-large clusters)
+    merged_into: dict[int, int] = {}  # index -> merge target index
+    for i in range(len(clusters)):
+        if i in merged_into or len(clusters[i].candidate_ids) > 3:
+            continue
+        for j in range(i + 1, len(clusters)):
+            if j in merged_into or len(clusters[j].candidate_ids) > 3:
+                continue
+            # Require different sources to merge (same-source should already be grouped)
+            sources_i = set(clusters[i].sources)
+            sources_j = set(clusters[j].sources)
+            if sources_i == sources_j and len(sources_i) == 1:
+                continue
+            # Prevent Polymarket clusters from merging with non-Polymarket
+            # clusters. Prediction markets about "Sam Altman equity" should not
+            # merge into a news cluster about "Sam Altman rivalry" just because
+            # both mention the same entity.
+            poly_i = "polymarket" in sources_i
+            poly_j = "polymarket" in sources_j
+            if poly_i != poly_j:
+                continue
+
+            overlap = _entity_overlap(cluster_entities[i], cluster_entities[j])
+            if overlap >= 0.45:
+                merged_into[j] = i
+
+    if not merged_into:
+        return clusters
+
+    # Build merged cluster list
+    result: list[schema.Cluster] = []
+    for i, cl in enumerate(clusters):
+        if i in merged_into:
+            continue
+        # Collect all clusters merged into this one
+        merge_sources = [i] + [j for j, target in merged_into.items() if target == i]
+        if len(merge_sources) == 1:
+            result.append(cl)
+            continue
+
+        # Combine candidates from all merged clusters
+        combined_cids: list[str] = []
+        combined_sources: set[str] = set()
+        best_score = 0.0
+        for idx in merge_sources:
+            combined_cids.extend(clusters[idx].candidate_ids)
+            combined_sources.update(clusters[idx].sources)
+            best_score = max(best_score, clusters[idx].score)
+
+        # Pick representatives from combined pool
+        combined_candidates = [candidate_map[cid] for cid in combined_cids if cid in candidate_map]
+        combined_candidates.sort(key=lambda c: c.final_score, reverse=True)
+        merge_text_cache = {
+            c.candidate_id: dedupe._PreparedText(_candidate_text(c))
+            for c in combined_candidates
+        }
+        reps = _mmr_representatives(combined_candidates, merge_text_cache)
+
+        cluster_id = cl.cluster_id
+        for cid in combined_cids:
+            cand = candidate_map.get(cid)
+            if cand:
+                cand.cluster_id = cluster_id
+
+        result.append(schema.Cluster(
+            cluster_id=cluster_id,
+            title=combined_candidates[0].title if combined_candidates else cl.title,
+            candidate_ids=combined_cids,
+            representative_ids=reps,
+            sources=sorted(combined_sources),
+            score=best_score,
+            uncertainty=_cluster_uncertainty(combined_candidates),
+        ))
+
+    return result
+
+
+def _cluster_uncertainty(group: list[schema.Candidate]) -> str | None:
+    sources = {source for candidate in group for source in schema.candidate_sources(candidate)}
+    if len(sources) == 1:
+        return "single-source"
+    if max(candidate.final_score for candidate in group) < 55:
+        return "thin-evidence"
+    return None

scripts/lib/competitors.py

@@ -0,0 +1,199 @@
+"""Discover peer entities ("competitors") for a topic via web search.
+
+Mirrors the `resolve.auto_resolve()` pattern: fan out 2-3 web searches via
+`grounding.web_search()`, then extract capitalized entity candidates from
+titles and snippets with deterministic text mining. No LLM call — the
+hosting reasoning model can always override discovery via
+`--competitors-list`.
+
+Returned list is ordered by score (frequency across queries) and capped to
+the caller's requested count.
+"""
+
+from __future__ import annotations
+
+import re
+import sys
+from collections import Counter
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from . import dates, grounding
+from .resolve import _has_backend
+
+# A "brand-shaped" token starts with uppercase OR is camelCase with an
+# uppercase letter later. Catches "Anthropic", "OpenAI", "xAI", "iPhone",
+# "eBay", "Hugging", "Face".
+_BRAND_TOKEN = (
+    r"(?:[A-Z][A-Za-z0-9&.\-]*"
+    r"|[a-z][A-Za-z0-9&.\-]*[A-Z][A-Za-z0-9&.\-]*)"
+)
+
+# A capitalized phrase of 1-4 brand tokens separated by whitespace.
+_CAPITALIZED_PHRASE = re.compile(
+    rf"\b{_BRAND_TOKEN}(?:\s+{_BRAND_TOKEN}){{0,3}}\b"
+)
+
+# Title-case fillers common in listicle SERPs. Kept flat — extraction
+# rejects a candidate whose entire tokens are stopwords, not candidates
+# that merely contain one.
+_STOPWORD_TOKENS: frozenset[str] = frozenset(
+    token.lower()
+    for token in (
+        # Listicle fillers
+        "Top", "Best", "Worst", "Popular", "Leading", "Similar",
+        "Alternatives", "Alternative", "Competitor", "Competitors",
+        "vs", "Vs", "Versus", "Review", "Reviews", "Comparison",
+        "Guide", "List", "Lists", "Full", "Complete", "Free", "Paid",
+        "Tools", "Tool", "Options", "Rivals", "Rival", "Similar",
+        "Pick", "Picks", "Ranking", "Ranked", "Recommended",
+        # Grammar / time
+        "The", "A", "An", "Of", "In", "For", "To", "With", "On", "At",
+        "By", "From", "Is", "Are", "And", "Or", "But", "Than", "As",
+        "This", "That", "These", "Those", "Our", "Your", "Their",
+        "January", "February", "March", "April", "May", "June", "July",
+        "August", "September", "October", "November", "December",
+        # Years likely to appear as standalone tokens
+        *(str(year) for year in range(2018, 2031)),
+        # Miscellaneous SERP noise
+        "AI", "Apps", "App", "Software", "Platform", "Service", "Startups",
+        "Companies", "Company", "Products", "Product", "Brands", "Brand",
+    )
+)
+
+
+def _log(msg: str) -> None:
+    print(f"[Competitors] {msg}", file=sys.stderr)
+
+
+def _topic_tokens(topic: str) -> set[str]:
+    """Return lowercase alphanumeric tokens of the topic for filtering."""
+    return {tok for tok in re.findall(r"[A-Za-z0-9]+", topic.lower()) if tok}
+
+
+def _candidate_ok(candidate: str, topic_tokens: set[str]) -> bool:
+    """Filter a candidate phrase against stopwords and topic overlap."""
+    tokens = [t for t in re.findall(r"[A-Za-z0-9&.\-]+", candidate) if t]
+    if not tokens:
+        return False
+    # Reject candidates made entirely of stopwords (e.g., "Top Alternatives").
+    if all(tok.lower() in _STOPWORD_TOKENS for tok in tokens):
+        return False
+    # Reject candidates that overlap with the topic (e.g., topic="OpenAI"
+    # should not return "OpenAI Alternatives" or "OpenAI").
+    lower_tokens = {tok.lower() for tok in tokens}
+    if lower_tokens & topic_tokens:
+        return False
+    # Reject too-short one-letter tokens like "I" or single digits.
+    if len(tokens) == 1 and len(tokens[0]) < 2:
+        return False
+    return True
+
+
+def _normalize_candidate(candidate: str) -> str:
+    """Collapse whitespace and strip trailing punctuation."""
+    return re.sub(r"\s+", " ", candidate).strip(".,;:!?'\"()[] ")
+
+
+def _extract_peer_entities(
+    items: list[dict], topic: str, limit: int,
+) -> list[str]:
+    """Score capitalized candidates across SERP items and return top `limit`.
+
+    Scoring is bag-of-phrases frequency across all items in the input. Ties
+    are broken by first-seen order so the output is deterministic.
+    """
+    topic_tokens = _topic_tokens(topic)
+    counts: Counter[str] = Counter()
+    first_seen: dict[str, int] = {}
+    order = 0
+    # Group candidates into a frequency map keyed by lowercased normalized
+    # form so "xAI" and "xAI" count together regardless of case.
+    canonical: dict[str, str] = {}
+    for item in items:
+        text = f"{item.get('title', '')} {item.get('snippet', '')}"
+        for raw in _CAPITALIZED_PHRASE.findall(text):
+            candidate = _normalize_candidate(raw)
+            if not _candidate_ok(candidate, topic_tokens):
+                continue
+            key = candidate.lower()
+            if key not in canonical:
+                canonical[key] = candidate
+                first_seen[key] = order
+                order += 1
+            counts[key] += 1
+
+    ranked_keys = sorted(
+        counts.keys(),
+        key=lambda k: (-counts[k], first_seen[k]),
+    )
+    return [canonical[k] for k in ranked_keys[:limit]]
+
+
+def _queries_for(topic: str) -> dict[str, str]:
+    return {
+        "competitors": f"{topic} competitors",
+        "alternatives": f"{topic} alternatives",
+        "vs": f"{topic} vs",
+    }
+
+
+def discover_competitors(
+    topic: str,
+    count: int,
+    config: dict,
+    *,
+    lookback_days: int = 30,
+) -> list[str]:
+    """Discover `count` peer entities for `topic` via web search.
+
+    Args:
+        topic: The primary research topic.
+        count: Desired number of competitor entities (1..N).
+        config: Runtime config dict — expects the same shape as the engine
+            config (BRAVE_API_KEY / EXA_API_KEY / SERPER_API_KEY / etc.).
+        lookback_days: Date range for freshness. Defaults to 30.
+
+    Returns:
+        A list of up to `count` entity names, deduped and ordered by score.
+        Empty list when no web backend is configured or every search fails
+        or returns zero usable candidates.
+    """
+    if count < 1:
+        return []
+    if not _has_backend(config):
+        _log("No web search backend available, skipping competitor discovery")
+        return []
+
+    date_range = dates.get_date_range(lookback_days)
+    queries = _queries_for(topic)
+    collected: list[dict] = []
+    searches_run = 0
+
+    def _search(label: str, query: str) -> tuple[str, list[dict]]:
+        items, _artifact = grounding.web_search(query, date_range, config)
+        return label, items
+
+    with ThreadPoolExecutor(max_workers=len(queries)) as executor:
+        futures = {
+            executor.submit(_search, label, q): label
+            for label, q in queries.items()
+        }
+        for future in as_completed(futures):
+            label = futures[future]
+            try:
+                _label, items = future.result()
+                collected.extend(items)
+                searches_run += 1
+            except Exception as exc:
+                _log(f"Search failed for {label}: {exc}")
+
+    if not collected:
+        _log(f"No SERP results for {topic!r} across {searches_run}/{len(queries)} queries")
+        return []
+
+    entities = _extract_peer_entities(collected, topic, limit=count)
+    _log(
+        f"Discovered {len(entities)} competitor(s) for {topic!r} "
+        f"from {searches_run}/{len(queries)} queries: {entities}"
+    )
+    return entities

scripts/lib/cookie_extract.py

@@ -0,0 +1,379 @@
+"""Browser cookie extraction for last30days.
+
+Extracts cookies from local browser databases (Firefox, Chrome, Safari)
+to enable zero-config authentication for services like X/Twitter.
+
+Only uses Python stdlib — no external dependencies.
+"""
+
+import configparser
+import functools
+import logging
+import platform
+import shutil
+import sqlite3
+import tempfile
+from pathlib import Path
+from typing import Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+@functools.lru_cache(maxsize=1)
+def _is_wsl() -> bool:
+    """Detect if running under Windows Subsystem for Linux.
+
+    Cached after the first call since /proc/version doesn't change at runtime.
+    """
+    try:
+        return "microsoft" in Path("/proc/version").read_text().lower()
+    except OSError:
+        return False
+
+
+def _get_wsl_firefox_profiles_dir() -> Optional[Path]:
+    """Find Firefox profiles directory on the Windows host from WSL.
+
+    Scans /mnt/c/Users/*/AppData/Roaming/Mozilla/Firefox for real user
+    directories (skips Public, Default, etc.).
+    """
+    mnt_users = Path("/mnt/c/Users")
+    if not mnt_users.is_dir():
+        return None
+    skip = {"Public", "Default", "Default User", "All Users"}
+    try:
+        for user_dir in sorted(mnt_users.iterdir()):
+            if user_dir.name in skip or not user_dir.is_dir():
+                continue
+            ff_dir = user_dir / "AppData" / "Roaming" / "Mozilla" / "Firefox"
+            if ff_dir.is_dir():
+                return ff_dir
+    except OSError:
+        pass
+    return None
+
+
+def _get_firefox_profiles_dir() -> Optional[Path]:
+    """Return the Firefox profiles directory for the current platform, or None."""
+    system = platform.system()
+    if system == "Darwin":
+        path = Path.home() / "Library" / "Application Support" / "Firefox"
+    elif system == "Linux":
+        path = Path.home() / ".mozilla" / "firefox"
+    else:
+        # Windows: %APPDATA%\Mozilla\Firefox — best-effort
+        appdata = Path.home() / "AppData" / "Roaming" / "Mozilla" / "Firefox"
+        path = appdata
+    return path if path.is_dir() else None
+
+
+def _find_default_profile(profiles_dir: Path) -> Optional[Path]:
+    """Parse profiles.ini to find the default profile directory.
+
+    Looks for a section with Default=1. Falls back to the first profile
+    directory found on disk if profiles.ini is missing or malformed.
+    """
+    ini_path = profiles_dir / "profiles.ini"
+
+    if ini_path.is_file():
+        try:
+            config = configparser.ConfigParser()
+            config.read(str(ini_path), encoding="utf-8")
+
+            # First pass: Install* section (Firefox >= 67 format, takes priority)
+            for section in config.sections():
+                if section.startswith("Install") and config.has_option(section, "Default"):
+                    raw = config.get(section, "Default")
+                    candidate = profiles_dir / raw
+                    if candidate.is_dir():
+                        return candidate
+
+            # Second pass: Profile section with Default=1
+            for section in config.sections():
+                if section.startswith("Profile") and config.has_option(section, "Default") and config.get(section, "Default") == "1":
+                    return _resolve_profile_path(profiles_dir, config, section)
+
+            # Third pass: first Profile section that exists on disk
+            for section in config.sections():
+                if section.startswith("Profile"):
+                    resolved = _resolve_profile_path(profiles_dir, config, section)
+                    if resolved and resolved.is_dir():
+                        return resolved
+        except (configparser.Error, OSError) as exc:
+            logger.debug("Failed to parse profiles.ini: %s", exc)
+
+    # Fallback: scan directory for anything that looks like a profile
+    return _fallback_find_profile(profiles_dir)
+
+
+def _resolve_profile_path(
+    profiles_dir: Path, config: configparser.ConfigParser, section: str
+) -> Optional[Path]:
+    """Resolve a profile path from a ConfigParser section."""
+    if not config.has_option(section, "Path"):
+        return None
+    raw_path = config.get(section, "Path")
+    is_relative = config.has_option(section, "IsRelative") and config.get(section, "IsRelative") == "1"
+    if is_relative:
+        candidate = profiles_dir / raw_path
+    else:
+        candidate = Path(raw_path)
+    return candidate if candidate.is_dir() else None
+
+
+def _fallback_find_profile(profiles_dir: Path) -> Optional[Path]:
+    """Find the first directory that contains cookies.sqlite."""
+    try:
+        for child in sorted(profiles_dir.iterdir()):
+            if child.is_dir() and (child / "cookies.sqlite").is_file():
+                return child
+    except OSError:
+        pass
+    return None
+
+
+def _query_cookies_db(
+    db_path: Path, domain: str, cookie_names: List[str]
+) -> Optional[Dict[str, str]]:
+    """Copy the cookies database to a temp file and query it.
+
+    Firefox locks cookies.sqlite while running, so we copy first.
+    Returns {name: value} dict or None if no matching cookies found.
+    """
+    if not db_path.is_file():
+        return None
+
+    tmp_fd = None
+    tmp_path = None
+    try:
+        tmp_fd, tmp_path = tempfile.mkstemp(suffix=".sqlite")
+        shutil.copy2(str(db_path), tmp_path)
+
+        conn = sqlite3.connect(tmp_path)
+        try:
+            # Build parameterized query — SQLite doesn't support array params,
+            # so we build the IN clause with individual placeholders.
+            placeholders = ",".join("?" for _ in cookie_names)
+            query = (
+                f"SELECT name, value FROM moz_cookies "
+                f"WHERE host LIKE ? AND name IN ({placeholders})"
+            )
+            # domain pattern: match .x.com, x.com, etc.
+            domain_pattern = f"%{domain}"
+            params = [domain_pattern] + list(cookie_names)
+
+            cursor = conn.execute(query, params)
+            rows = cursor.fetchall()
+        finally:
+            conn.close()
+
+        if not rows:
+            return None
+        return {name: value for name, value in rows}
+
+    except (sqlite3.Error, OSError) as exc:
+        logger.debug("Failed to query cookies database %s: %s", db_path, exc)
+        return None
+    finally:
+        if tmp_path:
+            try:
+                Path(tmp_path).unlink(missing_ok=True)
+            except OSError:
+                pass
+        if tmp_fd is not None:
+            try:
+                import os
+                os.close(tmp_fd)
+            except OSError:
+                pass
+
+
+def _try_firefox_dir(profiles_dir: Path, domain: str, cookie_names: List[str]) -> Optional[Dict[str, str]]:
+    """Try to extract cookies from a Firefox profiles directory."""
+    profile_path = _find_default_profile(profiles_dir)
+    if profile_path is None:
+        logger.debug("No Firefox profile found in %s", profiles_dir)
+        return None
+    return _query_cookies_db(profile_path / "cookies.sqlite", domain, cookie_names)
+
+
+def extract_firefox_cookies(
+    domain: str, cookie_names: List[str]
+) -> Optional[Dict[str, str]]:
+    """Extract cookies from Firefox for the given domain and cookie names.
+
+    Finds the default Firefox profile, copies cookies.sqlite to a temp file
+    (to avoid lock conflicts), and queries for the requested cookies.
+
+    On WSL2, falls back to Windows Firefox if native Linux Firefox has no
+    matching cookies. Windows Firefox cookies are unencrypted, so this works
+    without DPAPI or any Windows-side helpers.
+
+    Args:
+        domain: The cookie domain to match (e.g. ".x.com"). Matched with LIKE %domain.
+        cookie_names: List of cookie names to extract (e.g. ["auth_token", "ct0"]).
+
+    Returns:
+        Dict of {cookie_name: cookie_value} or None if extraction fails.
+    """
+    profiles_dir = _get_firefox_profiles_dir()
+    if profiles_dir is not None:
+        result = _try_firefox_dir(profiles_dir, domain, cookie_names)
+        if result is not None:
+            return result
+
+    if platform.system() == "Linux" and _is_wsl():
+        wsl_dir = _get_wsl_firefox_profiles_dir()
+        if wsl_dir is not None:
+            logger.debug("Trying Windows Firefox via WSL: %s", wsl_dir)
+            return _try_firefox_dir(wsl_dir, domain, cookie_names)
+
+    if profiles_dir is None:
+        logger.debug("Firefox profiles directory not found")
+    return None
+
+
+def extract_chrome_cookies(
+    domain: str, cookie_names: List[str]
+) -> Optional[Dict[str, str]]:
+    """Extract cookies from Chrome for the given domain and cookie names.
+
+    macOS only — uses Keychain + system openssl for AES-128-CBC decryption.
+    Linux/Windows not supported (Chrome uses platform-specific encryption).
+
+    Returns:
+        Dict of {cookie_name: cookie_value} or None if extraction fails.
+    """
+    if platform.system() != "Darwin":
+        logger.debug("Chrome cookie extraction only supported on macOS")
+        return None
+    try:
+        from .chrome_cookies import extract_chrome_cookies_macos
+        return extract_chrome_cookies_macos(domain, cookie_names)
+    except Exception as exc:
+        logger.debug("Chrome cookie extraction failed: %s", exc)
+        return None
+
+
+def extract_safari_cookies(
+    domain: str, cookie_names: List[str]
+) -> Optional[Dict[str, str]]:
+    """Extract cookies from Safari for the given domain and cookie names.
+
+    macOS only — parses the unencrypted binary cookie file.
+
+    Returns:
+        Dict of {cookie_name: cookie_value} or None if extraction fails.
+    """
+    if platform.system() != "Darwin":
+        logger.debug("Safari cookie extraction only supported on macOS")
+        return None
+    try:
+        from .safari_cookies import extract_safari_cookies_macos
+        return extract_safari_cookies_macos(domain, cookie_names)
+    except Exception as exc:
+        logger.debug("Safari cookie extraction failed: %s", exc)
+        return None
+
+
+def extract_cookies(
+    browser: str, domain: str, cookie_names: list[str]
+) -> Optional[dict[str, str]]:
+    """Extract cookies from the specified browser.
+
+    Args:
+        browser: One of 'firefox', 'chrome', 'safari', or 'auto'.
+            'auto' tries browsers in platform-appropriate order:
+            - macOS: Chrome -> Firefox -> Safari
+            - Linux: Firefox only
+        domain: The cookie domain to match (e.g. ".x.com").
+        cookie_names: List of cookie names to extract.
+
+    Returns:
+        Dict of {cookie_name: cookie_value} or None if extraction fails.
+    """
+    result = extract_cookies_with_source(browser, domain, cookie_names)
+    if result is None:
+        return None
+    cookies, _browser_name = result
+    return cookies
+
+
+def _extract_firefox_with_source(
+    domain: str, cookie_names: List[str]
+) -> Optional[tuple[Dict[str, str], str]]:
+    """Extract Firefox cookies and report whether they came from native or WSL.
+
+    Returns (cookies, "firefox") for native Linux/macOS Firefox, or
+    (cookies, "firefox-wsl") for Windows Firefox accessed via WSL2.
+    """
+    profiles_dir = _get_firefox_profiles_dir()
+    if profiles_dir is not None:
+        result = _try_firefox_dir(profiles_dir, domain, cookie_names)
+        if result is not None:
+            return (result, "firefox")
+
+    if platform.system() == "Linux" and _is_wsl():
+        wsl_dir = _get_wsl_firefox_profiles_dir()
+        if wsl_dir is not None:
+            logger.debug("Trying Windows Firefox via WSL: %s", wsl_dir)
+            result = _try_firefox_dir(wsl_dir, domain, cookie_names)
+            if result is not None:
+                return (result, "firefox-wsl")
+
+    return None
+
+
+def extract_cookies_with_source(
+    browser: str, domain: str, cookie_names: list[str]
+) -> Optional[tuple[dict[str, str], str]]:
+    """Extract cookies and report which browser they came from.
+
+    Same as extract_cookies() but returns a (cookies, browser_name) tuple
+    so callers can track the source.
+
+    Args:
+        browser: One of 'firefox', 'chrome', 'safari', or 'auto'.
+        domain: The cookie domain to match (e.g. ".x.com").
+        cookie_names: List of cookie names to extract.
+
+    Returns:
+        Tuple of ({cookie_name: cookie_value}, browser_name) or None.
+        browser_name is "firefox-wsl" when cookies came from Windows Firefox via WSL2.
+    """
+    extractors = {
+        "firefox": extract_firefox_cookies,
+        "chrome": extract_chrome_cookies,
+        "safari": extract_safari_cookies,
+    }
+
+    if browser != "auto":
+        if browser == "firefox":
+            return _extract_firefox_with_source(domain, cookie_names)
+        extractor = extractors.get(browser)
+        if extractor is None:
+            logger.warning("Unknown browser: %s", browser)
+            return None
+        result = extractor(domain, cookie_names)
+        return (result, browser) if result is not None else None
+
+    # Auto mode: try browsers in platform-appropriate order
+    system = platform.system()
+    if system == "Darwin":
+        order = ["chrome", "firefox", "safari"]
+    elif system == "Linux":
+        order = ["firefox"]
+    else:
+        order = ["firefox"]
+
+    for name in order:
+        if name == "firefox":
+            result = _extract_firefox_with_source(domain, cookie_names)
+            if result is not None:
+                return result
+        else:
+            result = extractors[name](domain, cookie_names)
+            if result is not None:
+                return (result, name)
+
+    return None

scripts/lib/dates.py

@@ -41,7 +41,10 @@ def parse_date(date_str: Optional[str]) -> Optional[datetime]:
 
     for fmt in formats:
         try:
-            return datetime.strptime(date_str, fmt).replace(tzinfo=timezone.utc)
+            dt = datetime.strptime(date_str, fmt)
+            if dt.tzinfo is not None:
+                return dt.astimezone(timezone.utc)
+            return dt.replace(tzinfo=timezone.utc)
         except ValueError:
             continue
 
@@ -78,14 +81,7 @@ def get_date_confidence(date_str: Optional[str], from_date: str, to_date: str) -
         start = datetime.strptime(from_date, "%Y-%m-%d").date()
         end = datetime.strptime(to_date, "%Y-%m-%d").date()
 
-        if start <= dt <= end:
-            return 'high'
-        elif dt < start:
-            # Older than range
-            return 'low'
-        else:
-            # Future date (suspicious)
-            return 'low'
+        return 'high' if start <= dt <= end else 'low'
     except ValueError:
         return 'low'
 

scripts/lib/dedupe.py

@@ -1,120 +1,127 @@
-"""Near-duplicate detection for last30days skill."""
+"""Within-source near-duplicate detection."""
+
+from __future__ import annotations
 
 import re
-from typing import List, Set, Tuple, Union
 
 from . import schema
 
+STOPWORDS = frozenset(
+    {
+        "the",
+        "a",
+        "an",
+        "to",
+        "for",
+        "how",
+        "is",
+        "in",
+        "of",
+        "on",
+        "and",
+        "with",
+        "from",
+        "by",
+        "at",
+        "this",
+        "that",
+        "it",
+        "what",
+        "are",
+        "do",
+        "can",
+    }
+)
+
 
 def normalize_text(text: str) -> str:
-    """Normalize text for comparison.
-
-    - Lowercase
-    - Remove punctuation
-    - Collapse whitespace
-    """
-    text = text.lower()
-    text = re.sub(r'[^\w\s]', ' ', text)
-    text = re.sub(r'\s+', ' ', text)
-    return text.strip()
+    text = re.sub(r"[^\w\s]", " ", text.lower())
+    return re.sub(r"\s+", " ", text).strip()
 
 
-def get_ngrams(text: str, n: int = 3) -> Set[str]:
-    """Get character n-grams from text."""
+def get_ngrams(text: str, n: int = 3) -> set[str]:
     text = normalize_text(text)
     if len(text) < n:
-        return {text}
-    return {text[i:i+n] for i in range(len(text) - n + 1)}
+        return {text} if text else set()
+    return {text[index:index + n] for index in range(len(text) - n + 1)}
 
 
-def jaccard_similarity(set1: Set[str], set2: Set[str]) -> float:
-    """Compute Jaccard similarity between two sets."""
-    if not set1 or not set2:
+def jaccard_similarity(left: set[str], right: set[str]) -> float:
+    if not left or not right:
         return 0.0
-    intersection = len(set1 & set2)
-    union = len(set1 | set2)
-    return intersection / union if union > 0 else 0.0
+    union = left | right
+    if not union:
+        return 0.0
+    return len(left & right) / len(union)
 
 
-def get_item_text(item: Union[schema.RedditItem, schema.XItem]) -> str:
-    """Get comparable text from an item."""
-    if isinstance(item, schema.RedditItem):
-        return item.title
-    else:
-        return item.text
+def token_jaccard(text_a: str, text_b: str) -> float:
+    tokens_a = {
+        token
+        for token in normalize_text(text_a).split()
+        if len(token) > 1 and token not in STOPWORDS
+    }
+    tokens_b = {
+        token
+        for token in normalize_text(text_b).split()
+        if len(token) > 1 and token not in STOPWORDS
+    }
+    return jaccard_similarity(tokens_a, tokens_b)
 
 
-def find_duplicates(
-    items: List[Union[schema.RedditItem, schema.XItem]],
-    threshold: float = 0.7,
-) -> List[Tuple[int, int]]:
-    """Find near-duplicate pairs in items.
-
-    Args:
-        items: List of items to check
-        threshold: Similarity threshold (0-1)
-
-    Returns:
-        List of (i, j) index pairs where i < j and items are similar
-    """
-    duplicates = []
-
-    # Pre-compute n-grams
-    ngrams = [get_ngrams(get_item_text(item)) for item in items]
-
-    for i in range(len(items)):
-        for j in range(i + 1, len(items)):
-            similarity = jaccard_similarity(ngrams[i], ngrams[j])
-            if similarity >= threshold:
-                duplicates.append((i, j))
-
-    return duplicates
+def hybrid_similarity(text_a: str, text_b: str) -> float:
+    return max(
+        jaccard_similarity(get_ngrams(text_a), get_ngrams(text_b)),
+        token_jaccard(text_a, text_b),
+    )
 
 
-def dedupe_items(
-    items: List[Union[schema.RedditItem, schema.XItem]],
-    threshold: float = 0.7,
-) -> List[Union[schema.RedditItem, schema.XItem]]:
-    """Remove near-duplicates, keeping highest-scored item.
-
-    Args:
-        items: List of items (should be pre-sorted by score descending)
-        threshold: Similarity threshold
-
-    Returns:
-        Deduplicated items
-    """
-    if len(items) <= 1:
-        return items
-
-    # Find duplicate pairs
-    dup_pairs = find_duplicates(items, threshold)
-
-    # Mark indices to remove (always remove the lower-scored one)
-    # Since items are pre-sorted by score, the second index is always lower
-    to_remove = set()
-    for i, j in dup_pairs:
-        # Keep the higher-scored one (lower index in sorted list)
-        if items[i].score >= items[j].score:
-            to_remove.add(j)
-        else:
-            to_remove.add(i)
-
-    # Return items not marked for removal
-    return [item for idx, item in enumerate(items) if idx not in to_remove]
+def _tokenize(normalized: str) -> frozenset[str]:
+    return frozenset(
+        tok for tok in normalized.split()
+        if len(tok) > 1 and tok not in STOPWORDS
+    )
 
 
-def dedupe_reddit(
-    items: List[schema.RedditItem],
-    threshold: float = 0.7,
-) -> List[schema.RedditItem]:
-    """Dedupe Reddit items."""
-    return dedupe_items(items, threshold)
+class _PreparedText:
+    """Pre-computed text representations for fast repeated similarity checks."""
+
+    __slots__ = ("ngrams", "tokens")
+
+    def __init__(self, raw: str) -> None:
+        norm = normalize_text(raw)
+        self.ngrams = get_ngrams(norm) if norm else set()
+        self.tokens = _tokenize(norm)
 
 
-def dedupe_x(
-    items: List[schema.XItem],
-    threshold: float = 0.7,
-) -> List[schema.XItem]:
-    """Dedupe X items."""
-    return dedupe_items(items, threshold)
+def prepared_similarity(a: _PreparedText, b: _PreparedText) -> float:
+    return max(
+        jaccard_similarity(a.ngrams, b.ngrams),
+        jaccard_similarity(a.tokens, b.tokens),
+    )
+
+
+def item_text(item: schema.SourceItem) -> str:
+    parts = [item.title, item.body, item.author or "", item.container or ""]
+    return " ".join(part for part in parts if part).strip()
+
+
+def dedupe_items(items: list[schema.SourceItem], threshold: float = 0.7) -> list[schema.SourceItem]:
+    """Remove near-duplicates while keeping earlier, better-scored items."""
+    kept: list[schema.SourceItem] = []
+    kept_prepared: list[_PreparedText] = []
+    for item in items:
+        text = item_text(item)
+        if not text:
+            kept.append(item)
+            continue
+        prep = _PreparedText(text)
+        is_duplicate = False
+        for existing_prep in kept_prepared:
+            if prepared_similarity(prep, existing_prep) >= threshold:
+                is_duplicate = True
+                break
+        if not is_duplicate:
+            kept.append(item)
+            kept_prepared.append(prep)
+    return kept

scripts/lib/entity_extract.py

@@ -0,0 +1,127 @@
+"""Entity extraction from initial search results for supplemental searches."""
+
+import re
+from collections import Counter
+from typing import Any, Dict, List
+
+# Handles that appear too frequently to be useful for targeted search.
+# These are generic/platform accounts, not topic-specific voices.
+GENERIC_HANDLES = {
+    "elonmusk", "openai", "google", "microsoft", "apple", "meta",
+    "github", "youtube", "x", "twitter", "reddit", "wikipedia",
+    "nytimes", "washingtonpost", "cnn", "bbc", "reuters",
+    "verified", "jack", "sundarpichai",
+}
+
+
+def extract_entities(
+    reddit_items: List[Dict[str, Any]],
+    x_items: List[Dict[str, Any]],
+    max_handles: int = 5,
+    max_hashtags: int = 3,
+    max_subreddits: int = 5,
+) -> Dict[str, List[str]]:
+    """Extract key entities from Phase 1 results for supplemental searches.
+
+    Parses X results for @handles and #hashtags, Reddit results for subreddit
+    names and cross-referenced communities.
+
+    Args:
+        reddit_items: Raw Reddit item dicts from Phase 1
+        x_items: Raw X item dicts from Phase 1
+        max_handles: Maximum handles to return
+        max_hashtags: Maximum hashtags to return
+        max_subreddits: Maximum subreddits to return
+
+    Returns:
+        Dict with keys: x_handles, x_hashtags, reddit_subreddits
+    """
+    handles = _extract_x_handles(x_items)
+    hashtags = _extract_x_hashtags(x_items)
+    subreddits = _extract_subreddits(reddit_items)
+
+    return {
+        "x_handles": handles[:max_handles],
+        "x_hashtags": hashtags[:max_hashtags],
+        "reddit_subreddits": subreddits[:max_subreddits],
+    }
+
+
+def _extract_x_handles(x_items: List[Dict[str, Any]]) -> List[str]:
+    """Extract and rank @handles from X results.
+
+    Sources handles from:
+    1. author_handle field (who posted)
+    2. @mentions in post text (who they're talking about/to)
+
+    Returns handles ranked by frequency, filtered for generic accounts.
+    """
+    handle_counts = Counter()
+
+    for item in x_items:
+        # Author handle
+        author = item.get("author_handle", "").strip().lstrip("@").lower()
+        if author and author not in GENERIC_HANDLES:
+            handle_counts[author] += 1
+
+        # @mentions in text
+        text = item.get("text", "")
+        mentions = re.findall(r'@(\w{1,15})', text)
+        for mention in mentions:
+            mention_lower = mention.lower()
+            if mention_lower not in GENERIC_HANDLES:
+                handle_counts[mention_lower] += 1
+
+    # Return all handles ranked by frequency
+    return [h for h, _ in handle_counts.most_common()]
+
+
+def _extract_x_hashtags(x_items: List[Dict[str, Any]]) -> List[str]:
+    """Extract and rank #hashtags from X results.
+
+    Returns hashtags ranked by frequency.
+    """
+    hashtag_counts = Counter()
+
+    for item in x_items:
+        text = item.get("text", "")
+        tags = re.findall(r'#(\w{2,30})', text)
+        for tag in tags:
+            hashtag_counts[tag.lower()] += 1
+
+    # Return all hashtags ranked by frequency
+    return [f"#{t}" for t, _ in hashtag_counts.most_common()]
+
+
+def _extract_subreddits(reddit_items: List[Dict[str, Any]]) -> List[str]:
+    """Extract and rank subreddits from Reddit results.
+
+    Sources from:
+    1. subreddit field on each result
+    2. Cross-references in comment text (e.g., "check out r/localLLaMA")
+
+    Returns subreddits ranked by frequency.
+    """
+    sub_counts = Counter()
+
+    for item in reddit_items:
+        # Primary subreddit
+        sub = item.get("subreddit", "").strip().lstrip("r/")
+        if sub:
+            sub_counts[sub] += 1
+
+        # Cross-references in comment insights
+        for insight in item.get("comment_insights", []):
+            cross_refs = re.findall(r'r/(\w{2,30})', insight)
+            for ref in cross_refs:
+                sub_counts[ref] += 1
+
+        # Cross-references in top comments
+        for comment in item.get("top_comments", []):
+            excerpt = comment.get("excerpt", "")
+            cross_refs = re.findall(r'r/(\w{2,30})', excerpt)
+            for ref in cross_refs:
+                sub_counts[ref] += 1
+
+    # Return subreddits ranked by frequency
+    return [sub for sub, _ in sub_counts.most_common()]

scripts/lib/env.py

@@ -1,18 +1,78 @@
 """Environment and API key management for last30days skill."""
 
+from __future__ import annotations
+
+import base64
+import binascii
+import json
 import os
+import sys
+import time
+from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Dict, Any
+from typing import Any, Literal
 
-CONFIG_DIR = Path.home() / ".config" / "last30days"
-CONFIG_FILE = CONFIG_DIR / ".env"
+# Allow override via environment variable for testing
+# Set LAST30DAYS_CONFIG_DIR="" for clean/no-config mode
+# Set LAST30DAYS_CONFIG_DIR="/path/to/dir" for custom config location
+_config_override = os.environ.get('LAST30DAYS_CONFIG_DIR')
+if _config_override == "":
+    # Empty string = no config file (clean mode)
+    CONFIG_DIR = None
+    CONFIG_FILE = None
+elif _config_override:
+    CONFIG_DIR = Path(_config_override)
+    CONFIG_FILE = CONFIG_DIR / ".env"
+else:
+    CONFIG_DIR = Path.home() / ".config" / "last30days"
+    CONFIG_FILE = CONFIG_DIR / ".env"
+
+CODEX_AUTH_FILE = Path(os.environ.get("CODEX_AUTH_FILE", str(Path.home() / ".codex" / "auth.json")))
+
+AuthSource = Literal["api_key", "codex", "none"]
+AuthStatus = Literal["ok", "missing", "expired", "missing_account_id"]
+
+AUTH_SOURCE_API_KEY: AuthSource = "api_key"
+AUTH_SOURCE_CODEX: AuthSource = "codex"
+AUTH_SOURCE_NONE: AuthSource = "none"
+
+AUTH_STATUS_OK: AuthStatus = "ok"
+AUTH_STATUS_MISSING: AuthStatus = "missing"
+AUTH_STATUS_EXPIRED: AuthStatus = "expired"
+AUTH_STATUS_MISSING_ACCOUNT_ID: AuthStatus = "missing_account_id"
 
 
-def load_env_file(path: Path) -> Dict[str, str]:
+@dataclass(frozen=True)
+class OpenAIAuth:
+    token: str | None
+    source: AuthSource
+    status: AuthStatus
+    account_id: str | None
+    codex_auth_file: str
+
+
+def _check_file_permissions(path: Path) -> None:
+    """Warn to stderr if a secrets file has overly permissive permissions."""
+    try:
+        mode = path.stat().st_mode
+        # Check if group or other can read (bits 0o044)
+        if mode & 0o044:
+            sys.stderr.write(
+                f"[last30days] WARNING: {path} is readable by other users. "
+                f"Run: chmod 600 {path}\n"
+            )
+            sys.stderr.flush()
+    except OSError as exc:
+        sys.stderr.write(f"[last30days] WARNING: could not stat {path}: {exc}\n")
+        sys.stderr.flush()
+
+
+def load_env_file(path: Path) -> dict[str, str]:
     """Load environment variables from a file."""
     env = {}
-    if not path.exists():
+    if not path or not path.exists():
         return env
+    _check_file_permissions(path)
 
     with open(path, 'r') as f:
         for line in f:
@@ -31,101 +91,568 @@ def load_env_file(path: Path) -> Dict[str, str]:
     return env
 
 
-def get_config() -> Dict[str, Any]:
-    """Load configuration from ~/.config/last30days/.env and environment."""
-    # Load from config file first
-    file_env = load_env_file(CONFIG_FILE)
+def _decode_jwt_payload(token: str) -> dict[str, Any] | None:
+    """Decode JWT payload without verification."""
+    try:
+        parts = token.split(".")
+        if len(parts) < 2:
+            return None
+        payload_b64 = parts[1]
+        pad = "=" * (-len(payload_b64) % 4)
+        decoded = base64.urlsafe_b64decode(payload_b64 + pad)
+        return json.loads(decoded.decode("utf-8"))
+    except (json.JSONDecodeError, UnicodeDecodeError, binascii.Error, IndexError) as exc:
+        sys.stderr.write(f"[last30days] WARNING: malformed JWT token: {exc}\n")
+        sys.stderr.flush()
+        return None
 
-    # Environment variables override file
+
+def _token_expired(token: str, leeway_seconds: int = 60) -> bool:
+    """Check if JWT token is expired."""
+    payload = _decode_jwt_payload(token)
+    if not payload:
+        return False
+    exp = payload.get("exp")
+    if not exp:
+        return False
+    return exp <= (time.time() + leeway_seconds)
+
+
+def extract_chatgpt_account_id(access_token: str) -> str | None:
+    """Extract chatgpt_account_id from JWT token."""
+    payload = _decode_jwt_payload(access_token)
+    if not payload:
+        return None
+    auth_claim = payload.get("https://api.openai.com/auth", {})
+    if isinstance(auth_claim, dict):
+        return auth_claim.get("chatgpt_account_id")
+    return None
+
+
+def load_codex_auth(path: Path = CODEX_AUTH_FILE) -> dict[str, Any]:
+    """Load Codex auth JSON."""
+    if not path.exists():
+        return {}
+    try:
+        with open(path, "r") as f:
+            return json.load(f)
+    except json.JSONDecodeError:
+        sys.stderr.write(
+            f"[last30days] WARNING: {path} exists but contains invalid JSON -- ignoring\n"
+        )
+        sys.stderr.flush()
+        return {}
+
+
+def get_codex_access_token() -> tuple[str | None, str]:
+    """Get Codex access token from auth.json.
+
+    Returns:
+        (token, status) where status is 'ok', 'missing', or 'expired'
+    """
+    auth = load_codex_auth()
+    token = None
+    if isinstance(auth, dict):
+        tokens = auth.get("tokens") or {}
+        if isinstance(tokens, dict):
+            token = tokens.get("access_token")
+        if not token:
+            token = auth.get("access_token")
+    if not token:
+        return None, AUTH_STATUS_MISSING
+    if _token_expired(token):
+        return None, AUTH_STATUS_EXPIRED
+    return token, AUTH_STATUS_OK
+
+
+def get_openai_auth(file_env: dict[str, str]) -> OpenAIAuth:
+    """Resolve OpenAI auth from API key or Codex login."""
+    api_key = os.environ.get('OPENAI_API_KEY') or file_env.get('OPENAI_API_KEY')
+    if api_key:
+        return OpenAIAuth(
+            token=api_key,
+            source=AUTH_SOURCE_API_KEY,
+            status=AUTH_STATUS_OK,
+            account_id=None,
+            codex_auth_file=str(CODEX_AUTH_FILE),
+        )
+
+    # Codex auth (chatgpt.com backend) intentionally skipped.
+    # The endpoint is unstable and causes crashes when the token expires.
+    # Users who want OpenAI should set OPENAI_API_KEY explicitly.
+
+    return OpenAIAuth(
+        token=None,
+        source=AUTH_SOURCE_NONE,
+        status=AUTH_STATUS_MISSING,
+        account_id=None,
+        codex_auth_file=str(CODEX_AUTH_FILE),
+    )
+
+
+def _find_project_env() -> Path | None:
+    """Find per-project .env by walking up from cwd.
+
+    Searches for .claude/last30days.env in each parent directory,
+    stopping at the user's home directory or filesystem root.
+    """
+    cwd = Path.cwd()
+    for parent in [cwd, *cwd.parents]:
+        candidate = parent / '.claude' / 'last30days.env'
+        if candidate.exists():
+            return candidate
+        # Stop at filesystem root or home
+        if parent == Path.home() or parent == parent.parent:
+            break
+    return None
+
+
+def get_config() -> dict[str, Any]:
+    """Load configuration from multiple sources.
+
+    Priority (highest wins):
+      1. Environment variables (os.environ)
+      2. .claude/last30days.env (per-project config)
+      3. ~/.config/last30days/.env (global config)
+    """
+    # Load from global config file
+    file_env = load_env_file(CONFIG_FILE) if CONFIG_FILE else {}
+
+    # Load from per-project config (overrides global)
+    project_env_path = _find_project_env()
+    project_env = load_env_file(project_env_path) if project_env_path else {}
+
+    # Merge: project overrides global
+    merged_env = {**file_env, **project_env}
+
+    openai_auth = get_openai_auth(merged_env)
+
+    # Build config: Codex/OpenAI auth + process.env > project .env > global .env
     config = {
-        'OPENAI_API_KEY': os.environ.get('OPENAI_API_KEY') or file_env.get('OPENAI_API_KEY'),
-        'XAI_API_KEY': os.environ.get('XAI_API_KEY') or file_env.get('XAI_API_KEY'),
-        'OPENAI_MODEL_POLICY': os.environ.get('OPENAI_MODEL_POLICY') or file_env.get('OPENAI_MODEL_POLICY', 'auto'),
-        'OPENAI_MODEL_PIN': os.environ.get('OPENAI_MODEL_PIN') or file_env.get('OPENAI_MODEL_PIN'),
-        'XAI_MODEL_POLICY': os.environ.get('XAI_MODEL_POLICY') or file_env.get('XAI_MODEL_POLICY', 'latest'),
-        'XAI_MODEL_PIN': os.environ.get('XAI_MODEL_PIN') or file_env.get('XAI_MODEL_PIN'),
+        'OPENAI_API_KEY': openai_auth.token,
+        'OPENAI_AUTH_SOURCE': openai_auth.source,
+        'OPENAI_AUTH_STATUS': openai_auth.status,
+        'OPENAI_CHATGPT_ACCOUNT_ID': openai_auth.account_id,
+        'CODEX_AUTH_FILE': openai_auth.codex_auth_file,
     }
 
+    keys = [
+        ('XAI_API_KEY', None),
+        ('GOOGLE_API_KEY', None),
+        ('GEMINI_API_KEY', None),
+        ('GOOGLE_GENAI_API_KEY', None),
+        ('XIAOHONGSHU_API_BASE', None),
+        ('LAST30DAYS_REASONING_PROVIDER', 'auto'),
+        ('LAST30DAYS_PLANNER_MODEL', None),
+        ('LAST30DAYS_RERANK_MODEL', None),
+        ('LAST30DAYS_X_MODEL', None),
+        ('LAST30DAYS_X_BACKEND', None),
+        ('OPENAI_MODEL_PIN', None),
+        ('XAI_MODEL_PIN', None),
+        ('SCRAPECREATORS_API_KEY', None),
+        ('APIFY_API_TOKEN', None),
+        ('AUTH_TOKEN', None),
+        ('CT0', None),
+        ('BSKY_HANDLE', None),
+        ('BSKY_APP_PASSWORD', None),
+        ('TRUTHSOCIAL_TOKEN', None),
+        ('BRAVE_API_KEY', None),
+        ('EXA_API_KEY', None),
+        ('SERPER_API_KEY', None),
+        ('OPENROUTER_API_KEY', None),
+        ('PARALLEL_API_KEY', None),
+        ('XQUIK_API_KEY', None),
+        ('FROM_BROWSER', None),
+        ('SETUP_COMPLETE', None),
+        ('INCLUDE_SOURCES', ''),
+    ]
+
+    for key, default in keys:
+        config[key] = os.environ.get(key) or merged_env.get(key, default)
+
+    # Track which config source was used
+    if project_env_path:
+        config['_CONFIG_SOURCE'] = f'project:{project_env_path}'
+    elif CONFIG_FILE and CONFIG_FILE.exists():
+        config['_CONFIG_SOURCE'] = f'global:{CONFIG_FILE}'
+    else:
+        config['_CONFIG_SOURCE'] = 'env_only'
+
+    # Extract browser credentials if configured
+    browser_creds = extract_browser_credentials(config)
+    for key, value in browser_creds.items():
+        if not config.get(key):
+            config[key] = value
+            config[f"_{key}_SOURCE"] = "browser"
+
     return config
 
 
-def config_exists() -> bool:
-    """Check if configuration file exists."""
-    return CONFIG_FILE.exists()
+# ---------------------------------------------------------------------------
+# Browser cookie extraction
+# ---------------------------------------------------------------------------
+
+COOKIE_DOMAINS: dict[str, dict[str, Any]] = {
+    "x": {
+        "domain": ".x.com",
+        "cookies": ["auth_token", "ct0"],
+        "mapping": {"auth_token": "AUTH_TOKEN", "ct0": "CT0"},
+    },
+    "truthsocial": {
+        "domain": ".truthsocial.com",
+        "cookies": ["_session_id"],
+        "mapping": {"_session_id": "TRUTHSOCIAL_TOKEN"},
+    },
+}
 
 
-def get_available_sources(config: Dict[str, Any]) -> str:
-    """Determine which sources are available based on API keys.
+def extract_browser_credentials(config: dict[str, Any]) -> dict[str, str]:
+    """Extract auth cookies from local browsers.
 
-    Returns: 'both', 'reddit', 'x', or 'web' (fallback when no keys)
+    Default behavior (FROM_BROWSER unset): tries Firefox and Safari only.
+    These read local files silently with no system dialogs.  Chrome is
+    skipped because ``security find-generic-password`` triggers a macOS
+    Keychain prompt that cannot be reliably suppressed.
+
+    Set ``FROM_BROWSER=auto`` to also try Chrome (accepts the dialog),
+    or ``FROM_BROWSER=off`` to disable extraction entirely.
     """
-    has_openai = bool(config.get('OPENAI_API_KEY'))
-    has_xai = bool(config.get('XAI_API_KEY'))
-
-    if has_openai and has_xai:
-        return 'both'
-    elif has_openai:
-        return 'reddit'
-    elif has_xai:
-        return 'x'
+    from_browser = (config.get("FROM_BROWSER") or "").strip().lower()
+    if from_browser == "off":
+        return {}
+    try:
+        from . import cookie_extract
+    except ImportError:
+        return {}
+    # Determine which browsers to try
+    if from_browser in ("firefox", "chrome", "safari"):
+        browsers = [from_browser]
+    elif from_browser == "auto":
+        browsers = ["firefox", "safari", "chrome"]
     else:
-        return 'web'  # Fallback: WebSearch only (no API keys needed)
+        # Default: silent browsers only (no Keychain dialog)
+        browsers = ["firefox", "safari"]
+    extracted: dict[str, str] = {}
+    for _service, spec in COOKIE_DOMAINS.items():
+        if all(config.get(env_key) for env_key in spec["mapping"].values()):
+            continue
+        for browser in browsers:
+            try:
+                cookies = cookie_extract.extract_cookies(browser, spec["domain"], spec["cookies"])
+            except Exception:
+                continue
+            if cookies:
+                for cookie_name, env_key in spec["mapping"].items():
+                    if cookie_name in cookies and not config.get(env_key):
+                        extracted[env_key] = cookies[cookie_name]
+                break  # Found cookies for this service, stop trying browsers
+    return extracted
 
 
-def validate_sources(requested: str, available: str, include_web: bool = False) -> tuple[str, Optional[str]]:
-    """Validate requested sources against available keys.
+def get_x_source_with_method(config: dict[str, Any]) -> tuple[str | None, str]:
+    """Return (source, method) for X search, where method describes the auth origin."""
+    if config.get("XAI_API_KEY"):
+        return "xai", "xai"
+    if config.get("AUTH_TOKEN") and config.get("CT0"):
+        method = config.get("_AUTH_TOKEN_SOURCE", "env")
+        return "bird", method
+    # Fall back to xurl CLI (official X API v2, OAuth2, free developer app)
+    from . import xurl_x
+    if xurl_x.is_available():
+        return "xurl", "oauth2"
+    return None, "none"
+
+
+def config_exists() -> bool:
+    """Check if any configuration source exists."""
+    if _find_project_env():
+        return True
+    if CONFIG_FILE:
+        return CONFIG_FILE.exists()
+    return False
+
+
+def is_reddit_available(config: dict[str, Any]) -> bool:
+    """Check if Reddit search is available.
+
+    v3 uses ScrapeCreators only.
+    """
+    return bool(config.get('SCRAPECREATORS_API_KEY'))
+
+
+def get_reddit_source(config: dict[str, Any]) -> str | None:
+    """Determine which Reddit backend to use.
+
+    Returns: 'scrapecreators' or None
+    """
+    if config.get('SCRAPECREATORS_API_KEY'):
+        return 'scrapecreators'
+    return None
+
+
+def get_x_source(config: dict[str, Any]) -> str | None:
+    """Determine the best available explicit X/Twitter source.
+
+    Priority: explicit backend pin, then xAI, then Bird with explicit cookies.
+
+    Browser-cookie probing is intentionally not used here. Automatic Keychain
+    access causes popups during normal pipeline runs. Bird is only considered
+    available when AUTH_TOKEN and CT0 are present explicitly.
 
     Args:
-        requested: 'auto', 'reddit', 'x', 'both', or 'web'
-        available: Result from get_available_sources()
-        include_web: If True, add WebSearch to available sources
+        config: Configuration dict from get_config()
 
     Returns:
-        Tuple of (effective_sources, error_message)
+        'bird' if Bird is installed and explicit cookies are configured,
+        'xai' if XAI_API_KEY is configured,
+        'xurl' if xurl CLI is installed and authenticated,
+        None if no X source available.
     """
-    # WebSearch-only mode (no API keys)
-    if available == 'web':
-        if requested == 'auto':
-            return 'web', None
-        elif requested == 'web':
-            return 'web', None
-        else:
-            return 'web', f"No API keys configured. Using WebSearch fallback. Add keys to ~/.config/last30days/.env for Reddit/X."
+    # Import here to avoid circular dependency
+    from . import bird_x
 
-    if requested == 'auto':
-        # Add web to sources if include_web is set
-        if include_web:
-            if available == 'both':
-                return 'all', None  # reddit + x + web
-            elif available == 'reddit':
-                return 'reddit-web', None
-            elif available == 'x':
-                return 'x-web', None
-        return available, None
+    preferred = (config.get('LAST30DAYS_X_BACKEND') or '').lower()
+    has_bird_creds = bool(config.get('AUTH_TOKEN') and config.get('CT0'))
+    if has_bird_creds:
+        bird_x.set_credentials(config.get('AUTH_TOKEN'), config.get('CT0'))
 
-    if requested == 'web':
-        return 'web', None
+    if preferred == 'xai':
+        return 'xai' if config.get('XAI_API_KEY') else None
+    if preferred == 'bird':
+        return 'bird' if has_bird_creds and bird_x.is_bird_installed() else None
 
-    if requested == 'both':
-        if available not in ('both',):
-            missing = 'xAI' if available == 'reddit' else 'OpenAI'
-            return 'none', f"Requested both sources but {missing} key is missing. Use --sources=auto to use available keys."
-        if include_web:
-            return 'all', None
-        return 'both', None
+    if config.get('XAI_API_KEY'):
+        return 'xai'
+    if has_bird_creds and bird_x.is_bird_installed():
+        return 'bird'
 
-    if requested == 'reddit':
-        if available == 'x':
-            return 'none', "Requested Reddit but only xAI key is available."
-        if include_web:
-            return 'reddit-web', None
-        return 'reddit', None
+    # Fall back to xurl CLI (official X API v2, OAuth2, free developer app)
+    from . import xurl_x
+    if xurl_x.is_available():
+        return 'xurl'
 
-    if requested == 'x':
-        if available == 'reddit':
-            return 'none', "Requested X but only OpenAI key is available."
-        if include_web:
-            return 'x-web', None
-        return 'x', None
+    return None
 
-    return requested, None
+
+def is_ytdlp_available() -> bool:
+    """Check if yt-dlp is installed for YouTube search."""
+    from . import youtube_yt
+    return youtube_yt.is_ytdlp_installed()
+
+
+def is_youtube_comments_available(config: dict[str, Any]) -> bool:
+    """Check if YouTube comment enrichment is available.
+
+    Requires SCRAPECREATORS_API_KEY AND youtube_comments in INCLUDE_SOURCES.
+    """
+    if not config.get('SCRAPECREATORS_API_KEY'):
+        return False
+    include = _parse_include_sources(config)
+    return 'youtube_comments' in include
+
+
+def is_tiktok_comments_available(config: dict[str, Any]) -> bool:
+    """Check if TikTok comment enrichment is available.
+
+    Requires SCRAPECREATORS_API_KEY AND tiktok_comments in INCLUDE_SOURCES.
+    Mirrors the youtube_comments opt-in pattern.
+    """
+    if not config.get('SCRAPECREATORS_API_KEY'):
+        return False
+    include = _parse_include_sources(config)
+    return 'tiktok_comments' in include
+
+
+def is_youtube_sc_available(config: dict[str, Any]) -> bool:
+    """Check if ScrapeCreators YouTube search fallback is available.
+
+    Used when yt-dlp is not installed or fails.
+    """
+    return bool(config.get('SCRAPECREATORS_API_KEY'))
+
+
+def is_hackernews_available() -> bool:
+    """Check if Hacker News source is available.
+
+    Always returns True - HN uses free Algolia API, no key needed.
+    """
+    return True
+
+
+def is_bluesky_available(config: dict[str, Any]) -> bool:
+    """Check if Bluesky source is available.
+
+    Requires BSKY_HANDLE and BSKY_APP_PASSWORD (app password from bsky.app/settings).
+    """
+    return bool(config.get('BSKY_HANDLE') and config.get('BSKY_APP_PASSWORD'))
+
+
+def is_truthsocial_available(config: dict[str, Any]) -> bool:
+    """Check if Truth Social source is available.
+
+    Requires TRUTHSOCIAL_TOKEN (bearer token from browser dev tools).
+    """
+    return bool(config.get('TRUTHSOCIAL_TOKEN'))
+
+
+def is_polymarket_available() -> bool:
+    """Check if Polymarket source is available.
+
+    Always returns True - Gamma API is free, no key needed.
+    """
+    return True
+
+
+def is_tiktok_available(config: dict[str, Any]) -> bool:
+    """Check if TikTok source is available (ScrapeCreators or legacy Apify).
+
+    Returns True if SCRAPECREATORS_API_KEY or APIFY_API_TOKEN is set.
+    """
+    return bool(config.get('SCRAPECREATORS_API_KEY') or config.get('APIFY_API_TOKEN'))
+
+
+def get_tiktok_token(config: dict[str, Any]) -> str:
+    """Get TikTok API token, preferring ScrapeCreators over legacy Apify."""
+    return config.get('SCRAPECREATORS_API_KEY') or config.get('APIFY_API_TOKEN') or ''
+
+
+def _parse_include_sources(config: dict[str, Any]) -> set[str]:
+    """Parse INCLUDE_SOURCES config value into a set of lowercase source names."""
+    raw = config.get('INCLUDE_SOURCES') or ''
+    return {s.strip().lower() for s in raw.split(',') if s.strip()}
+
+
+def is_threads_available(config: dict[str, Any]) -> bool:
+    """Check if Threads source is available.
+
+    Requires SCRAPECREATORS_API_KEY AND 'threads' in INCLUDE_SOURCES.
+    Threads is an opt-in source - it is not activated by default.
+    """
+    if not config.get('SCRAPECREATORS_API_KEY'):
+        return False
+    return 'threads' in _parse_include_sources(config)
+
+
+def is_instagram_available(config: dict[str, Any]) -> bool:
+    """Check if Instagram source is available (ScrapeCreators).
+
+    Returns True if SCRAPECREATORS_API_KEY is set.
+    Instagram uses the same key as TikTok.
+    """
+    return bool(config.get('SCRAPECREATORS_API_KEY'))
+
+
+def get_instagram_token(config: dict[str, Any]) -> str:
+    """Get Instagram API token (same ScrapeCreators key as TikTok)."""
+    return config.get('SCRAPECREATORS_API_KEY') or ''
+
+
+def get_xiaohongshu_api_base(config: dict[str, Any]) -> str:
+    """Get Xiaohongshu HTTP API base URL.
+
+    Defaults to host.docker.internal so OpenClaw Docker can reach host service.
+    """
+    return (config.get('XIAOHONGSHU_API_BASE') or "http://host.docker.internal:18060").rstrip("/")
+
+
+def is_xiaohongshu_available(config: dict[str, Any]) -> bool:
+    """Check whether Xiaohongshu HTTP API is reachable and logged in."""
+    # Import here to avoid heavy imports at module load.
+    from . import http
+
+    base = get_xiaohongshu_api_base(config)
+    try:
+        # Keep health probe snappy, but allow one retry for transient hiccups.
+        health = http.get(f"{base}/health", timeout=3, retries=2)
+        if not isinstance(health, dict):
+            return False
+        if not health.get("success"):
+            return False
+
+        # Login probe can be slower on some deployments (browser/session checks),
+        # so use a slightly longer timeout to avoid false negatives.
+        login = http.get(f"{base}/api/v1/login/status", timeout=8, retries=2)
+        is_logged_in = (
+            login.get("data", {}).get("is_logged_in")
+            if isinstance(login, dict) else False
+        )
+        return bool(is_logged_in)
+    except (OSError, http.HTTPError):
+        return False
+    except Exception as exc:
+        sys.stderr.write(
+            f"[last30days] WARNING: unexpected error checking Xiaohongshu: "
+            f"{type(exc).__name__}: {exc}\n"
+        )
+        sys.stderr.flush()
+        return False
+
+
+# Backward compat alias
+is_apify_available = is_tiktok_available
+
+
+def get_x_source_status(config: dict[str, Any]) -> dict[str, Any]:
+    """Get detailed X source status for UI decisions.
+
+    Returns:
+        Dict with keys: source, bird_installed, bird_authenticated,
+        bird_username, xai_available, can_install_bird
+    """
+    from . import bird_x
+
+    if config.get('AUTH_TOKEN') and config.get('CT0'):
+        bird_x.set_credentials(config.get('AUTH_TOKEN'), config.get('CT0'))
+    bird_status = bird_x.get_bird_status()
+    xai_available = bool(config.get('XAI_API_KEY'))
+
+    # Determine active source
+    if bird_status["authenticated"]:
+        source = 'bird'
+    elif xai_available:
+        source = 'xai'
+    else:
+        # Fall back to xurl CLI
+        from . import xurl_x as _xurl_check
+        source = 'xurl' if _xurl_check.is_available() else None
+
+    from . import xurl_x as _xurl_x
+    return {
+        "source": source,
+        "bird_installed": bird_status["installed"],
+        "bird_authenticated": bird_status["authenticated"],
+        "bird_username": bird_status["username"],
+        "xai_available": xai_available,
+        "xurl_available": _xurl_x.is_available(),
+        "can_install_bird": bird_status["can_install"],
+    }
+
+
+# Pinterest
+def is_pinterest_available(config: dict[str, Any]) -> bool:
+    """Check if Pinterest source is available.
+
+    Returns True when SCRAPECREATORS_API_KEY is set AND 'pinterest' is in
+    INCLUDE_SOURCES (or requested_sources at the pipeline level).  Pinterest
+    is opt-in because not every topic benefits from visual pin results.
+    """
+    return bool(config.get('SCRAPECREATORS_API_KEY'))
+
+
+def get_pinterest_token(config: dict[str, Any]) -> str:
+    """Get Pinterest API token (same ScrapeCreators key as TikTok/Instagram)."""
+    return config.get('SCRAPECREATORS_API_KEY') or ''
+
+
+# Xquik
+def is_xquik_available(config: dict[str, Any]) -> bool:
+    """Check if Xquik X search source is available.
+
+    Requires XQUIK_API_KEY (API key from xquik.com).
+    """
+    return bool(config.get('XQUIK_API_KEY'))
+
+
+def get_xquik_token(config: dict[str, Any]) -> str:
+    """Get Xquik API key."""
+    return config.get('XQUIK_API_KEY') or ''

scripts/lib/fanout.py

@@ -0,0 +1,85 @@
+"""Parallel multi-entity fan-out for the --competitors flag.
+
+The orchestrator accepts a `main_runner()` for the topic and a
+`competitor_runner(entity)` for each peer. It parallelizes their execution
+via a `ThreadPoolExecutor` and collects per-entity Reports. Per-entity
+failures are logged and dropped; the run survives as long as the main topic
+plus at least one competitor succeed.
+
+This module owns no business logic about pipeline arguments — the caller
+(scripts/last30days.py main) builds the closures with the appropriate
+config, depth, and overrides for each entity.
+"""
+
+from __future__ import annotations
+
+import sys
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Callable
+
+from . import schema
+
+# Sub-runs hit the same upstream APIs as the main topic. Cap parallelism so a
+# 6-way fan-out does not stampede a single backend's rate limit.
+MAX_PARALLEL_SUBRUNS = 6
+
+
+def _log(msg: str) -> None:
+    print(f"[Fanout] {msg}", file=sys.stderr)
+
+
+def run_competitor_fanout(
+    *,
+    main_topic: str,
+    main_runner: Callable[[], schema.Report],
+    competitors: list[str],
+    competitor_runner: Callable[[str], schema.Report],
+) -> list[tuple[str, schema.Report]]:
+    """Run main + competitor pipelines in parallel; return surviving reports.
+
+    Args:
+        main_topic: Display label for the user's primary topic.
+        main_runner: Zero-arg callable returning the main topic's Report.
+        competitors: Ordered list of competitor entity names.
+        competitor_runner: Callable(entity_name) -> Report for each peer.
+
+    Returns:
+        Ordered list of (entity_name, Report) tuples for runs that succeeded.
+        Empty list if every run raised; the caller decides how to surface
+        partial-failure modes.
+    """
+    if not competitors:
+        report = main_runner()
+        return [(main_topic, report)]
+
+    workers = min(len(competitors) + 1, MAX_PARALLEL_SUBRUNS)
+
+    def _run_one(label: str, fn: Callable[[], schema.Report]) -> tuple[str, schema.Report | None, Exception | None]:
+        try:
+            return label, fn(), None
+        except Exception as exc:
+            return label, None, exc
+
+    submissions: list[tuple[str, Callable[[], schema.Report]]] = [
+        (main_topic, main_runner),
+    ]
+    for entity in competitors:
+        submissions.append((entity, lambda e=entity: competitor_runner(e)))
+
+    with ThreadPoolExecutor(max_workers=workers) as executor:
+        futures = {
+            executor.submit(_run_one, label, fn): label
+            for label, fn in submissions
+        }
+        results: dict[str, schema.Report] = {}
+        for future in as_completed(futures):
+            label, report, exc = future.result()
+            if exc is not None:
+                _log(f"Sub-run failed for {label!r}: {type(exc).__name__}: {exc}")
+                continue
+            assert report is not None
+            results[label] = report
+
+    # Preserve the original submission order rather than completion order so
+    # the comparison render is deterministic across runs.
+    return [(label, results[label]) for label, _ in submissions if label in results]

scripts/lib/fusion.py

@@ -0,0 +1,202 @@
+"""Weighted reciprocal rank fusion for per-(subquery, source) streams."""
+
+from __future__ import annotations
+
+from urllib.parse import parse_qs, urlencode, urlparse, urlunparse
+
+from . import schema
+
+# Standard RRF smoothing constant (Cormack et al. 2009)
+RRF_K = 60
+
+
+def _candidate_sort_key(c: schema.Candidate) -> tuple:
+    return (-c.rrf_score, -c.local_relevance, -c.freshness, schema.candidate_source_label(c), c.title)
+
+
+def _normalize_url(url: str) -> str:
+    """Normalize URL for dedup: lowercase, strip www/old/m prefixes, remove tracking params."""
+    parsed = urlparse(url.strip().lower())
+    netloc = parsed.netloc
+    for prefix in ("www.", "old.", "m."):
+        if netloc.startswith(prefix):
+            netloc = netloc[len(prefix):]
+    # Strip tracking params
+    params = parse_qs(parsed.query)
+    clean_params = {k: v for k, v in params.items() if not k.startswith("utm_")}
+    query = urlencode(clean_params, doseq=True)
+    return urlunparse((parsed.scheme, netloc, parsed.path.rstrip("/"), "", query, ""))
+
+
+def candidate_key(item: schema.SourceItem) -> str:
+    if item.url:
+        return _normalize_url(item.url)
+    return f"{item.source}:{item.item_id}"
+
+
+_DIVERSITY_RELEVANCE_THRESHOLD = 0.25
+
+# Per-author cap: no single author/handle should dominate the pool.
+_MAX_ITEMS_PER_AUTHOR = 3
+
+
+def _extract_author(candidate: schema.Candidate) -> str | None:
+    """Return a normalized author key from a candidate's source items."""
+    for item in candidate.source_items:
+        if item.author:
+            return item.author.strip().lower()
+    return None
+
+
+def _apply_per_author_cap(
+    candidates: list[schema.Candidate],
+    max_per_author: int = _MAX_ITEMS_PER_AUTHOR,
+) -> list[schema.Candidate]:
+    """Keep at most *max_per_author* items from any single author.
+
+    Candidates are assumed to already be sorted by quality (rrf_score etc.),
+    so the first N encountered per author are the best ones.
+    """
+    author_counts: dict[str, int] = {}
+    result: list[schema.Candidate] = []
+    for c in candidates:
+        author = _extract_author(c)
+        if author is None:
+            result.append(c)
+            continue
+        count = author_counts.get(author, 0)
+        if count < max_per_author:
+            result.append(c)
+            author_counts[author] = count + 1
+    return result
+
+
+def _diversify_pool(
+    fused: list[schema.Candidate],
+    pool_limit: int,
+    min_per_source: int = 2,
+) -> list[schema.Candidate]:
+    """Ensure at least *min_per_source* items per qualifying source survive truncation.
+
+    Sources only qualify for reserved slots if their best item exceeds
+    the relevance threshold. Low-relevance sources compete on merit only.
+    """
+    max_relevance: dict[str, float] = {}
+    for c in fused:
+        current = max_relevance.get(c.source, 0.0)
+        if c.local_relevance > current:
+            max_relevance[c.source] = c.local_relevance
+
+    reserved: dict[str, list[schema.Candidate]] = {}
+    remainder: list[schema.Candidate] = []
+    for c in fused:
+        qualifies = max_relevance.get(c.source, 0.0) >= _DIVERSITY_RELEVANCE_THRESHOLD
+        bucket = reserved.setdefault(c.source, [])
+        if qualifies and len(bucket) < min_per_source:
+            bucket.append(c)
+        else:
+            remainder.append(c)
+    pool = [c for per_source in reserved.values() for c in per_source]
+    seen = {c.candidate_id for c in pool}
+    for c in remainder:
+        if len(pool) >= pool_limit:
+            break
+        if c.candidate_id not in seen:
+            pool.append(c)
+    pool.sort(key=_candidate_sort_key)
+    return pool[:pool_limit]
+
+
+def weighted_rrf(
+    streams: dict[tuple[str, str], list[schema.SourceItem]],
+    plan: schema.QueryPlan,
+    *,
+    pool_limit: int,
+) -> list[schema.Candidate]:
+    """Fuse ranked lists into a single candidate pool."""
+    subqueries = {subquery.label: subquery for subquery in plan.subqueries}
+    candidates: dict[str, schema.Candidate] = {}
+
+    for (label, source), items in streams.items():
+        subquery = subqueries[label]
+        weight = subquery.weight * plan.source_weights.get(source, 1.0)
+        for rank, item in enumerate(items, start=1):
+            key = candidate_key(item)
+            score = weight / (RRF_K + rank)
+            item_local_relevance = item.local_relevance if item.local_relevance is not None else float(item.metadata.get("local_relevance", item.relevance_hint))
+            item_freshness = item.freshness if item.freshness is not None else int(item.metadata.get("freshness", 0))
+            item_source_quality = item.source_quality if item.source_quality is not None else float(item.metadata.get("source_quality", 0.6))
+            if key not in candidates:
+                candidates[key] = schema.Candidate(
+                    candidate_id=key,
+                    item_id=item.item_id,
+                    source=item.source,
+                    title=item.title,
+                    url=item.url,
+                    snippet=item.snippet,
+                    subquery_labels=[label],
+                    native_ranks={f"{label}:{source}": rank},
+                    local_relevance=item_local_relevance,
+                    freshness=item_freshness,
+                    engagement=item.engagement_score if item.engagement_score is not None else item.metadata.get("engagement_score"),
+                    source_quality=item_source_quality,
+                    rrf_score=score,
+                    sources=[item.source],
+                    source_items=[item],
+                    metadata={
+                        "provenance": [
+                            {
+                                "source": source,
+                                "subquery_label": label,
+                                "native_rank": rank,
+                                "item_id": item.item_id,
+                            }
+                        ]
+                    },
+                )
+                continue
+
+            candidate = candidates[key]
+            candidate.rrf_score += score
+            previous_primary_score = (candidate.local_relevance * 100.0) + candidate.freshness + (candidate.source_quality * 10.0)
+            incoming_primary_score = (item_local_relevance * 100.0) + item_freshness + (item_source_quality * 10.0)
+            candidate.local_relevance = max(
+                candidate.local_relevance,
+                item_local_relevance,
+            )
+            candidate.freshness = max(candidate.freshness, item_freshness)
+            item_eng = item.engagement_score if item.engagement_score is not None else item.metadata.get("engagement_score")
+            if candidate.engagement is None:
+                candidate.engagement = item_eng
+            elif item_eng is not None:
+                candidate.engagement = max(candidate.engagement, item_eng)
+            candidate.source_quality = max(
+                candidate.source_quality,
+                item_source_quality,
+            )
+            candidate.native_ranks[f"{label}:{source}"] = rank
+            if label not in candidate.subquery_labels:
+                candidate.subquery_labels.append(label)
+            if item.source not in candidate.sources:
+                candidate.sources.append(item.source)
+            if not any(existing.source == item.source and existing.item_id == item.item_id for existing in candidate.source_items):
+                candidate.source_items.append(item)
+            candidate.metadata.setdefault("provenance", []).append(
+                {
+                    "source": source,
+                    "subquery_label": label,
+                    "native_rank": rank,
+                    "item_id": item.item_id,
+                }
+            )
+            if incoming_primary_score > previous_primary_score:
+                candidate.item_id = item.item_id
+                candidate.source = item.source
+                candidate.title = item.title
+                candidate.snippet = item.snippet
+            if len(candidate.snippet.split()) < len(item.snippet.split()):
+                candidate.snippet = item.snippet
+
+    fused = sorted(candidates.values(), key=_candidate_sort_key)
+    fused = _apply_per_author_cap(fused)
+    return _diversify_pool(fused, pool_limit)

scripts/lib/github.py

@@ -0,0 +1,921 @@
+"""GitHub Issues/PRs search via the public GitHub Search API.
+
+Uses api.github.com/search/issues for issue/PR discovery and
+per-item comment enrichment. Auth via GITHUB_TOKEN env var or
+`gh auth token` subprocess fallback.
+"""
+
+import json
+import math
+import os
+import re
+import subprocess
+import sys
+import urllib.error
+import urllib.parse
+import urllib.request
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List, Optional
+
+from . import dates, log
+from .query import extract_core_subject
+from .relevance import token_overlap_relevance
+
+SEARCH_URL = "https://api.github.com/search/issues"
+
+DEPTH_LIMITS = {
+    "quick": 15,
+    "default": 30,
+    "deep": 60,
+}
+
+ENRICH_LIMITS = {
+    "quick": 3,
+    "default": 5,
+    "deep": 8,
+}
+
+USER_AGENT = "last30days/3.0 (research tool)"
+
+
+def _log(msg: str):
+    log.source_log("GitHub", msg, tty_only=False)
+
+
+def _resolve_token(token: Optional[str] = None) -> Optional[str]:
+    """Resolve GitHub auth token from argument, env, or gh CLI."""
+    if token:
+        return token
+    env_token = os.environ.get("GITHUB_TOKEN")
+    if env_token:
+        return env_token
+    # Fallback: try gh CLI
+    try:
+        result = subprocess.run(
+            ["gh", "auth", "token"],
+            capture_output=True, text=True, timeout=5,
+        )
+        if result.returncode == 0 and result.stdout.strip():
+            return result.stdout.strip()
+    except (FileNotFoundError, subprocess.TimeoutExpired, OSError):
+        pass
+    return None
+
+
+def _fetch_json(
+    url: str,
+    token: Optional[str] = None,
+    timeout: int = 15,
+) -> Optional[Dict[str, Any]]:
+    """Fetch JSON from GitHub API. Returns None on failure."""
+    headers = {
+        "User-Agent": USER_AGENT,
+        "Accept": "application/vnd.github+json",
+    }
+    if token:
+        headers["Authorization"] = f"Bearer {token}"
+
+    req = urllib.request.Request(url, headers=headers)
+    try:
+        with urllib.request.urlopen(req, timeout=timeout) as resp:
+            body = resp.read().decode("utf-8")
+            return json.loads(body)
+    except urllib.error.HTTPError as e:
+        if e.code == 403:
+            _log(f"403 rate limited or forbidden: {url}")
+            return None
+        if e.code == 422:
+            _log(f"422 unprocessable: {url}")
+            return None
+        _log(f"HTTP {e.code}: {e.reason}")
+        return None
+    except (urllib.error.URLError, OSError, TimeoutError) as e:
+        _log(f"Network error: {e}")
+        return None
+    except json.JSONDecodeError as e:
+        _log(f"JSON decode error: {e}")
+        return None
+
+
+def _parse_repo_from_url(html_url: str) -> str:
+    """Extract 'owner/repo' from a GitHub issue/PR URL."""
+    parts = html_url.replace("https://github.com/", "").split("/")
+    if len(parts) >= 2:
+        return f"{parts[0]}/{parts[1]}"
+    return ""
+
+
+def _parse_date(iso_str: Optional[str]) -> Optional[str]:
+    """Parse a GitHub ISO 8601 datetime string and return YYYY-MM-DD.
+
+    Returns None for non-date input. GitHub's API always emits ISO 8601
+    (e.g. "2026-02-26T16:00:00Z"), but we defer to dates.parse_date() so
+    garbage input gets rejected instead of silently sliced.
+    """
+    dt = dates.parse_date(iso_str)
+    return dt.strftime("%Y-%m-%d") if dt else None
+
+
+def _compute_relevance(
+    query: str,
+    title: str,
+    rank_index: int,
+    reactions: int,
+    comments: int,
+) -> float:
+    """Blend text relevance with engagement signals."""
+    rank_score = max(0.3, 1.0 - (rank_index * 0.02))
+    engagement_boost = min(0.2, math.log1p(reactions + comments) / 20)
+
+    if query:
+        content_score = token_overlap_relevance(query, title)
+        relevance = min(1.0, 0.6 * rank_score + 0.4 * content_score + engagement_boost)
+    else:
+        relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
+
+    return round(relevance, 2)
+
+
+def search_github(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    """Search GitHub Issues and PRs.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        token: Optional GitHub token (falls back to env/gh CLI)
+
+    Returns:
+        List of normalized item dicts. Empty list on any failure.
+    """
+    resolved_token = _resolve_token(token)
+    if not resolved_token:
+        _log("No GitHub token available (set GITHUB_TOKEN or install gh CLI)")
+        return []
+
+    count = DEPTH_LIMITS.get(depth, DEPTH_LIMITS["default"])
+    core = extract_core_subject(topic)
+    _log(f"Searching for '{core}' (raw: '{topic}', since {from_date}, count={count})")
+
+    # Build search query with date filter
+    q = f"{core} created:>{from_date}"
+    params = {
+        "q": q,
+        "sort": "reactions",
+        "order": "desc",
+        "per_page": str(min(count, 100)),
+    }
+    url = f"{SEARCH_URL}?{urllib.parse.urlencode(params)}"
+
+    data = _fetch_json(url, token=resolved_token, timeout=30)
+    if not data:
+        return []
+
+    raw_items = data.get("items", [])
+    _log(f"Found {len(raw_items)} issues/PRs")
+
+    items = []
+    for i, item in enumerate(raw_items[:count]):
+        html_url = item.get("html_url", "")
+        repo = _parse_repo_from_url(html_url)
+        title = item.get("title", "")
+        body_text = item.get("body") or ""
+        reactions_total = item.get("reactions", {}).get("total_count", 0) if isinstance(item.get("reactions"), dict) else 0
+        comment_count = item.get("comments", 0)
+        labels = [
+            lbl.get("name", "") for lbl in (item.get("labels") or [])
+            if isinstance(lbl, dict)
+        ]
+        state = item.get("state", "")
+        is_pr = "pull_request" in item
+        author = item.get("user", {}).get("login", "") if isinstance(item.get("user"), dict) else ""
+
+        relevance = _compute_relevance(core, title, i, reactions_total, comment_count)
+
+        items.append({
+            "id": f"GH{i + 1}",
+            "title": title,
+            "url": html_url,
+            "date": _parse_date(item.get("created_at")),
+            "author": author,
+            "source": "github",
+            "score": reactions_total,
+            "container": repo,
+            "snippet": body_text[:300] if body_text else "",
+            "relevance": relevance,
+            "why_relevant": f"GitHub {'PR' if is_pr else 'issue'}: {title[:60]}",
+            "engagement": {
+                "reactions": reactions_total,
+                "comments": comment_count,
+            },
+            "metadata": {
+                "labels": labels,
+                "state": state,
+                "comment_count": comment_count,
+                "reactions": reactions_total,
+                "is_pr": is_pr,
+            },
+        })
+
+    # Enrich top items with comments
+    items = _enrich_top_items(items, depth, resolved_token)
+
+    # Date filter
+    filtered = []
+    for item in items:
+        d = item.get("date")
+        if d is None or (from_date <= d <= to_date):
+            filtered.append(item)
+
+    # Sort by relevance
+    filtered.sort(key=lambda x: x.get("relevance", 0), reverse=True)
+
+    return filtered
+
+
+def _enrich_top_items(
+    items: List[Dict[str, Any]],
+    depth: str,
+    token: str,
+) -> List[Dict[str, Any]]:
+    """Fetch comments for top N items by reactions."""
+    if not items:
+        return items
+
+    limit = ENRICH_LIMITS.get(depth, ENRICH_LIMITS["default"])
+
+    by_reactions = sorted(
+        range(len(items)),
+        key=lambda i: items[i].get("score", 0),
+        reverse=True,
+    )
+    to_enrich = by_reactions[:limit]
+
+    _log(f"Enriching top {len(to_enrich)} items with comments")
+
+    with ThreadPoolExecutor(max_workers=5) as executor:
+        futures = {
+            executor.submit(
+                _fetch_item_comments,
+                items[idx]["url"],
+                token,
+            ): idx
+            for idx in to_enrich
+        }
+
+        for future in as_completed(futures):
+            idx = futures[future]
+            try:
+                comments = future.result(timeout=15)
+                items[idx]["metadata"]["top_comments"] = comments
+            except (KeyError, TypeError, OSError) as exc:
+                _log(f"Comment enrichment failed for {items[idx].get('url', '?')}: {type(exc).__name__}: {exc}")
+                items[idx]["metadata"]["top_comments"] = []
+
+    return items
+
+
+def _fetch_item_comments(
+    issue_url: str,
+    token: str,
+    max_comments: int = 5,
+) -> List[Dict[str, Any]]:
+    """Fetch comments for a GitHub issue/PR.
+
+    Args:
+        issue_url: HTML URL like https://github.com/owner/repo/issues/123
+        token: GitHub auth token
+        max_comments: Max comments to return
+
+    Returns:
+        List of comment dicts with score, excerpt, author.
+    """
+    path = issue_url.replace("https://github.com/", "")
+    path = path.replace("/pull/", "/issues/")
+    api_url = f"https://api.github.com/repos/{path}/comments?per_page={max_comments}&sort=reactions&direction=desc"
+
+    data = _fetch_json(api_url, token=token, timeout=15)
+    if not data or not isinstance(data, list):
+        return []
+
+    comments = []
+    for c in data[:max_comments]:
+        body = c.get("body") or ""
+        excerpt = body[:300] + "..." if len(body) > 300 else body
+        reactions = c.get("reactions", {})
+        reaction_count = reactions.get("total_count", 0) if isinstance(reactions, dict) else 0
+        author = c.get("user", {}).get("login", "") if isinstance(c.get("user"), dict) else ""
+
+        comments.append({
+            "score": reaction_count,
+            "excerpt": excerpt,
+            "author": author,
+        })
+
+    return comments
+
+
+# ---------------------------------------------------------------------------
+# Person-mode search: author-scoped queries, star enrichment, release notes
+# ---------------------------------------------------------------------------
+
+PERSON_DEPTH_LIMITS = {
+    "quick": {"pr_pages": 1, "own_repos": 3, "external_repos": 5},
+    "default": {"pr_pages": 1, "own_repos": 5, "external_repos": 10},
+    "deep": {"pr_pages": 2, "own_repos": 5, "external_repos": 15},
+}
+
+
+def _fetch_readme_snippet(repo: str, token: str, max_chars: int = 500) -> Optional[str]:
+    """Fetch README content for a repo, truncated to first ~max_chars."""
+    url = f"https://api.github.com/repos/{repo}/readme"
+    headers = {
+        "User-Agent": USER_AGENT,
+        "Accept": "application/vnd.github.raw+json",
+    }
+    if token:
+        headers["Authorization"] = f"Bearer {token}"
+
+    req = urllib.request.Request(url, headers=headers)
+    try:
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            raw = resp.read().decode("utf-8", errors="replace")
+    except (urllib.error.HTTPError, urllib.error.URLError, OSError, TimeoutError):
+        return None
+
+    if not raw:
+        return None
+    # Try to break at a paragraph boundary
+    if len(raw) <= max_chars:
+        return raw
+    cut = raw[:max_chars]
+    last_double_newline = cut.rfind("\n\n")
+    if last_double_newline > max_chars // 3:
+        return cut[:last_double_newline].rstrip()
+    return cut.rstrip() + "..."
+
+
+def _fetch_latest_releases(
+    repo: str, token: str, count: int = 3, max_body: int = 300,
+) -> List[Dict[str, str]]:
+    """Fetch latest releases for a repo."""
+    url = f"https://api.github.com/repos/{repo}/releases?per_page={count}"
+    data = _fetch_json(url, token=token, timeout=10)
+    if not data or not isinstance(data, list):
+        return []
+    releases = []
+    for r in data[:count]:
+        tag = r.get("tag_name", "")
+        date = _parse_date(r.get("published_at"))
+        body = (r.get("body") or "")[:max_body]
+        name = r.get("name") or tag
+        releases.append({"tag": tag, "name": name, "date": date, "body": body})
+    return releases
+
+
+def _fetch_top_issues(repo: str, token: str) -> Dict[str, Any]:
+    """Fetch top feature request (by reactions) and top complaint (by comments)."""
+    result: Dict[str, Any] = {}
+
+    # Top feature request: issues with enhancement label, sorted by reactions
+    feat_q = urllib.parse.quote(f"repo:{repo} is:issue is:open label:enhancement")
+    feat_url = f"{SEARCH_URL}?q={feat_q}&sort=reactions&order=desc&per_page=1"
+    feat_data = _fetch_json(feat_url, token=token, timeout=10)
+    if feat_data and feat_data.get("items"):
+        item = feat_data["items"][0]
+        result["top_feature_request"] = {
+            "title": item.get("title", ""),
+            "reactions": item.get("reactions", {}).get("total_count", 0) if isinstance(item.get("reactions"), dict) else 0,
+            "comments": item.get("comments", 0),
+            "url": item.get("html_url", ""),
+        }
+    elif feat_data and feat_data.get("total_count", 0) == 0:
+        # No enhancement label; fall back to top issue by reactions
+        fallback_q = urllib.parse.quote(f"repo:{repo} is:issue is:open")
+        fallback_url = f"{SEARCH_URL}?q={fallback_q}&sort=reactions&order=desc&per_page=1"
+        fallback_data = _fetch_json(fallback_url, token=token, timeout=10)
+        if fallback_data and fallback_data.get("items"):
+            item = fallback_data["items"][0]
+            result["top_feature_request"] = {
+                "title": item.get("title", ""),
+                "reactions": item.get("reactions", {}).get("total_count", 0) if isinstance(item.get("reactions"), dict) else 0,
+                "comments": item.get("comments", 0),
+                "url": item.get("html_url", ""),
+            }
+
+    # Top complaint: most-discussed open issue (by comments)
+    bug_q = urllib.parse.quote(f"repo:{repo} is:issue is:open")
+    bug_url = f"{SEARCH_URL}?q={bug_q}&sort=comments&order=desc&per_page=1"
+    bug_data = _fetch_json(bug_url, token=token, timeout=10)
+    if bug_data and bug_data.get("items"):
+        item = bug_data["items"][0]
+        result["top_complaint"] = {
+            "title": item.get("title", ""),
+            "reactions": item.get("reactions", {}).get("total_count", 0) if isinstance(item.get("reactions"), dict) else 0,
+            "comments": item.get("comments", 0),
+            "url": item.get("html_url", ""),
+        }
+
+    return result
+
+
+def _fetch_repo_info(repo: str, token: str) -> Optional[Dict[str, Any]]:
+    """Fetch repo metadata (stars, forks, description, language)."""
+    url = f"https://api.github.com/repos/{repo}"
+    data = _fetch_json(url, token=token, timeout=10)
+    if not data or not isinstance(data, dict):
+        return None
+    return {
+        "stars": data.get("stargazers_count", 0),
+        "forks": data.get("forks_count", 0),
+        "description": (data.get("description") or "")[:200],
+        "language": data.get("language") or "",
+        "open_issues": data.get("open_issues_count", 0),
+    }
+
+
+def _format_stars(n: int) -> str:
+    """Format star count as human-readable (e.g., 349K, 2.9K, 42)."""
+    if n >= 1_000_000:
+        return f"{n / 1_000_000:.1f}M"
+    if n >= 1_000:
+        return f"{n / 1_000:.0f}K" if n >= 10_000 else f"{n / 1_000:.1f}K"
+    return str(n)
+
+
+def search_github_person(
+    username: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    """Person-mode GitHub search: author-scoped queries with star enrichment.
+
+    Returns SourceItems for:
+    - 1 velocity summary item
+    - Per-repo items for top external repos (with stars + release notes)
+    - Per-repo items for own repos (with stars + README + top issues + releases)
+    """
+    resolved_token = _resolve_token(token)
+    if not resolved_token:
+        _log("No GitHub token available for person-mode search")
+        return []
+
+    limits = PERSON_DEPTH_LIMITS.get(depth, PERSON_DEPTH_LIMITS["default"])
+    _log(f"Person-mode search for @{username} (since {from_date})")
+
+    # Phase 1: PR velocity via search API
+    total_q = urllib.parse.quote(f"author:{username} type:pr created:>{from_date}")
+    merged_q = urllib.parse.quote(f"author:{username} type:pr is:merged created:>{from_date}")
+
+    total_url = f"{SEARCH_URL}?q={total_q}&per_page=1"
+    merged_url = f"{SEARCH_URL}?q={merged_q}&sort=reactions&order=desc&per_page=100"
+
+    total_data = _fetch_json(total_url, token=resolved_token, timeout=20)
+    merged_data = _fetch_json(merged_url, token=resolved_token, timeout=20)
+
+    total_prs = total_data.get("total_count", 0) if total_data else 0
+    merged_count = merged_data.get("total_count", 0) if merged_data else 0
+    merged_items = merged_data.get("items", []) if merged_data else []
+
+    _log(f"Found {total_prs} total PRs, {merged_count} merged")
+
+    if total_prs == 0 and merged_count == 0:
+        _log("No PRs found, falling back to keyword search")
+        return []
+
+    # Phase 2: Group merged PRs by repo
+    repo_pr_counts: Dict[str, int] = {}
+    for item in merged_items:
+        repo = _parse_repo_from_url(item.get("html_url", ""))
+        if repo:
+            repo_pr_counts[repo] = repo_pr_counts.get(repo, 0) + 1
+
+    # Sort repos by PR count (most active first)
+    sorted_repos = sorted(repo_pr_counts.items(), key=lambda x: x[1], reverse=True)
+
+    # Phase 3: Fetch own repos
+    own_repos_url = f"https://api.github.com/users/{username}/repos?sort=stars&per_page={limits['own_repos']}&direction=desc"
+    own_repos_data = _fetch_json(own_repos_url, token=resolved_token, timeout=15)
+    own_repo_names = set()
+    own_repos_info: List[Dict[str, Any]] = []
+    if own_repos_data and isinstance(own_repos_data, list):
+        for r in own_repos_data:
+            full_name = r.get("full_name", "")
+            if full_name and not r.get("fork"):
+                own_repo_names.add(full_name)
+                own_repos_info.append({
+                    "full_name": full_name,
+                    "stars": r.get("stargazers_count", 0),
+                    "forks": r.get("forks_count", 0),
+                    "description": (r.get("description") or "")[:200],
+                    "language": r.get("language") or "",
+                    "open_issues": r.get("open_issues_count", 0),
+                })
+
+    # Separate external repos from own repos
+    external_repos = [(repo, count) for repo, count in sorted_repos if repo not in own_repo_names]
+    external_repos = external_repos[:limits["external_repos"]]
+
+    # Phase 4: Parallel enrichment (star counts, releases, READMEs, top issues)
+    items: List[Dict[str, Any]] = []
+    idx = 0
+
+    # Build velocity summary
+    open_prs = total_prs - merged_count
+    merge_rate = round(100 * merged_count / total_prs) if total_prs > 0 else 0
+    num_repos = len(repo_pr_counts)
+    velocity_text = (
+        f"GitHub Person Profile: @{username}\n\n"
+        f"CONTRIBUTION VELOCITY (last {(to_date > from_date) and 30 or 30} days)\n"
+        f"- {merged_count} PRs merged across {num_repos} repos ({merge_rate}% merge rate)\n"
+        f"- {total_prs} total PRs submitted, {open_prs} still open\n"
+    )
+
+    idx += 1
+    items.append({
+        "id": f"GH{idx}",
+        "title": f"@{username}: {merged_count} PRs merged across {num_repos} repos ({merge_rate}% merge rate)",
+        "url": f"https://github.com/{username}",
+        "date": to_date,
+        "author": username,
+        "source": "github",
+        "score": merged_count,
+        "container": f"@{username}",
+        "snippet": velocity_text,
+        "relevance": 0.95,
+        "why_relevant": f"GitHub profile: @{username} - {merged_count} PRs merged across {num_repos} repos",
+        "engagement": {"reactions": merged_count, "comments": total_prs},
+        "metadata": {
+            "labels": ["person-profile", "velocity"],
+            "state": "open",
+            "comment_count": 0,
+            "reactions": merged_count,
+            "is_pr": False,
+        },
+    })
+
+    # Phase 5: Enrich external repos (parallel: star counts + releases)
+    _log(f"Enriching {len(external_repos)} external repos + {len(own_repos_info)} own repos")
+
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        # External repo enrichment: stars + releases
+        ext_futures = {}
+        for repo, pr_count in external_repos:
+            ext_futures[executor.submit(_enrich_external_repo, repo, resolved_token)] = (repo, pr_count)
+
+        # Own repo enrichment: README + releases + top issues
+        own_futures = {}
+        for own_repo in own_repos_info:
+            own_futures[executor.submit(_enrich_own_repo, own_repo["full_name"], resolved_token)] = own_repo
+
+        # Collect external repo results
+        for future in as_completed(ext_futures):
+            repo, pr_count = ext_futures[future]
+            try:
+                enrichment = future.result(timeout=20)
+            except Exception as exc:
+                _log(f"External repo enrichment failed for {repo}: {exc}")
+                enrichment = {}
+
+            repo_info = enrichment.get("info")
+            releases = enrichment.get("releases", [])
+
+            stars = repo_info["stars"] if repo_info else 0
+            stars_str = _format_stars(stars)
+            desc = repo_info["description"] if repo_info else ""
+
+            snippet_parts = [f"Contributed {pr_count} merged PRs to {repo} ({stars_str} stars)"]
+            if desc:
+                snippet_parts.append(f"  {desc}")
+            if releases:
+                for rel in releases[:2]:
+                    body_preview = f" - {rel['body'][:150]}" if rel.get("body") else ""
+                    snippet_parts.append(f"  Latest release: {rel['name']} ({rel['date']}){body_preview}")
+
+            idx += 1
+            items.append({
+                "id": f"GH{idx}",
+                "title": f"{repo} ({stars_str} stars) - {pr_count} PRs merged",
+                "url": f"https://github.com/{repo}",
+                "date": releases[0]["date"] if releases and releases[0].get("date") else to_date,
+                "author": username,
+                "source": "github",
+                "score": stars,
+                "container": repo,
+                "snippet": "\n".join(snippet_parts),
+                "relevance": min(0.9, 0.6 + math.log1p(stars) / 30 + min(0.15, pr_count / 20)),
+                "why_relevant": f"GitHub contribution: {pr_count} PRs merged to {repo} ({stars_str} stars)",
+                "engagement": {"reactions": stars, "comments": pr_count},
+                "metadata": {
+                    "labels": ["person-profile", "external-repo"],
+                    "state": "open",
+                    "comment_count": pr_count,
+                    "reactions": stars,
+                    "is_pr": False,
+                },
+            })
+
+        # Collect own repo results
+        for future in as_completed(own_futures):
+            own_repo = own_futures[future]
+            try:
+                enrichment = future.result(timeout=25)
+            except Exception as exc:
+                _log(f"Own repo enrichment failed for {own_repo['full_name']}: {exc}")
+                enrichment = {}
+
+            repo_name = own_repo["full_name"]
+            stars = own_repo["stars"]
+            stars_str = _format_stars(stars)
+            open_issues = own_repo["open_issues"]
+            desc = own_repo["description"]
+
+            readme = enrichment.get("readme")
+            releases = enrichment.get("releases", [])
+            top_issues = enrichment.get("top_issues", {})
+
+            snippet_parts = [f"Own project: {repo_name} ({stars_str} stars, {open_issues} open issues)"]
+            if desc:
+                snippet_parts.append(f"  {desc}")
+            if readme:
+                snippet_parts.append(f"  README: {readme[:300]}")
+            if releases:
+                for rel in releases[:2]:
+                    body_preview = f" - {rel['body'][:150]}" if rel.get("body") else ""
+                    snippet_parts.append(f"  Latest release: {rel['name']} ({rel['date']}){body_preview}")
+            feat = top_issues.get("top_feature_request")
+            if feat:
+                snippet_parts.append(f"  Top feature request: \"{feat['title']}\" ({feat['reactions']} reactions, {feat['comments']} comments)")
+            complaint = top_issues.get("top_complaint")
+            if complaint:
+                snippet_parts.append(f"  Top complaint: \"{complaint['title']}\" ({complaint['comments']} comments)")
+
+            idx += 1
+            items.append({
+                "id": f"GH{idx}",
+                "title": f"{repo_name} ({stars_str} stars) - own project, {open_issues} open issues",
+                "url": f"https://github.com/{repo_name}",
+                "date": releases[0]["date"] if releases and releases[0].get("date") else to_date,
+                "author": username,
+                "source": "github",
+                "score": stars,
+                "container": repo_name,
+                "snippet": "\n".join(snippet_parts),
+                "relevance": min(0.95, 0.7 + math.log1p(stars) / 25),
+                "why_relevant": f"GitHub own project: {repo_name} ({stars_str} stars)",
+                "engagement": {"reactions": stars, "comments": open_issues},
+                "metadata": {
+                    "labels": ["person-profile", "own-repo"],
+                    "state": "open",
+                    "comment_count": open_issues,
+                    "reactions": stars,
+                    "is_pr": False,
+                },
+            })
+
+    # Sort by relevance
+    items.sort(key=lambda x: x.get("relevance", 0), reverse=True)
+    _log(f"Person-mode returned {len(items)} items")
+    return items
+
+
+def _enrich_external_repo(repo: str, token: str) -> Dict[str, Any]:
+    """Fetch star count + releases for an external repo."""
+    info = _fetch_repo_info(repo, token)
+    releases = _fetch_latest_releases(repo, token, count=3)
+    return {"info": info, "releases": releases}
+
+
+def _enrich_own_repo(repo: str, token: str) -> Dict[str, Any]:
+    """Fetch README + releases + top issues for an own repo."""
+    readme = _fetch_readme_snippet(repo, token, max_chars=500)
+    releases = _fetch_latest_releases(repo, token, count=3)
+    top_issues = _fetch_top_issues(repo, token)
+    return {"readme": readme, "releases": releases, "top_issues": top_issues}
+
+
+# ---------------------------------------------------------------------------
+# Project-mode search: fetch comprehensive data for specific repos
+# ---------------------------------------------------------------------------
+
+def search_github_project(
+    repos: List[str],
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    """Project-mode GitHub search: fetch stars, README, releases, top issues for repos.
+
+    Args:
+        repos: List of 'owner/repo' strings.
+        from_date: Start date (YYYY-MM-DD).
+        to_date: End date (YYYY-MM-DD).
+        depth: 'quick', 'default', or 'deep'.
+        token: Optional GitHub token.
+
+    Returns:
+        List of SourceItems, one per repo.
+    """
+    resolved_token = _resolve_token(token)
+    if not resolved_token:
+        _log("No GitHub token available for project-mode search")
+        return []
+
+    _log(f"Project-mode search for {len(repos)} repos: {', '.join(repos)}")
+
+    items: List[Dict[str, Any]] = []
+
+    with ThreadPoolExecutor(max_workers=min(8, len(repos))) as executor:
+        futures = {
+            executor.submit(_enrich_project_repo, repo, resolved_token): repo
+            for repo in repos
+        }
+
+        for idx, future in enumerate(as_completed(futures)):
+            repo = futures[future]
+            try:
+                enrichment = future.result(timeout=25)
+            except Exception as exc:
+                _log(f"Project enrichment failed for {repo}: {exc}")
+                continue
+
+            info = enrichment.get("info")
+            if not info:
+                _log(f"No repo info for {repo}, skipping")
+                continue
+
+            readme = enrichment.get("readme")
+            releases = enrichment.get("releases", [])
+            top_issues = enrichment.get("top_issues", {})
+
+            stars = info["stars"]
+            stars_str = _format_stars(stars)
+            open_issues = info["open_issues"]
+            desc = info["description"]
+            lang = info["language"]
+
+            snippet_parts = [f"Project: {repo} ({stars_str} stars, {open_issues} open issues, {lang})"]
+            if desc:
+                snippet_parts.append(f"  {desc}")
+            if readme:
+                snippet_parts.append(f"  README: {readme[:400]}")
+            if releases:
+                for rel in releases[:2]:
+                    body_preview = f" - {rel['body'][:150]}" if rel.get("body") else ""
+                    snippet_parts.append(f"  Latest release: {rel['name']} ({rel['date']}){body_preview}")
+            feat = top_issues.get("top_feature_request")
+            if feat:
+                snippet_parts.append(f"  Top feature request: \"{feat['title']}\" ({feat['reactions']} reactions, {feat['comments']} comments)")
+            complaint = top_issues.get("top_complaint")
+            if complaint:
+                snippet_parts.append(f"  Top complaint: \"{complaint['title']}\" ({complaint['comments']} comments)")
+
+            items.append({
+                "id": f"GH{idx + 1}",
+                "title": f"{repo} ({stars_str} stars) - {open_issues} open issues",
+                "url": f"https://github.com/{repo}",
+                "date": releases[0]["date"] if releases and releases[0].get("date") else to_date,
+                "author": repo.split("/")[0],
+                "source": "github",
+                "score": stars,
+                "container": repo,
+                "snippet": "\n".join(snippet_parts),
+                "relevance": min(0.95, 0.7 + math.log1p(stars) / 25),
+                "why_relevant": f"GitHub project: {repo} ({stars_str} stars, live)",
+                "engagement": {"reactions": stars, "comments": open_issues},
+                "metadata": {
+                    "labels": ["project-mode"],
+                    "state": "open",
+                    "comment_count": open_issues,
+                    "reactions": stars,
+                    "is_pr": False,
+                    "github_stars": {repo: stars},
+                },
+            })
+
+    items.sort(key=lambda x: x.get("relevance", 0), reverse=True)
+    _log(f"Project-mode returned {len(items)} items")
+    return items
+
+
+def _enrich_project_repo(repo: str, token: str) -> Dict[str, Any]:
+    """Fetch all project data for a repo: info + README + releases + top issues."""
+    info = _fetch_repo_info(repo, token)
+    readme = _fetch_readme_snippet(repo, token, max_chars=500)
+    releases = _fetch_latest_releases(repo, token, count=3)
+    top_issues = _fetch_top_issues(repo, token)
+    return {"info": info, "readme": readme, "releases": releases, "top_issues": top_issues}
+
+
+# ---------------------------------------------------------------------------
+# Post-rerank star enrichment: annotate candidates with live star counts
+# ---------------------------------------------------------------------------
+
+_REPO_URL_PATTERN = re.compile(r"github\.com/([A-Za-z0-9_.-]+/[A-Za-z0-9_.-]+)")
+_SKIP_PATHS = {"topics", "search", "orgs", "settings", "features", "about", "pricing", "enterprise", "explore", "marketplace", "sponsors"}
+
+
+def extract_repo_refs(candidates: List[Any]) -> List[str]:
+    """Extract unique owner/repo strings from candidate URLs, titles, and snippets."""
+    seen: set = set()
+    repos: List[str] = []
+    for c in candidates:
+        texts = [
+            getattr(c, "url", "") or "",
+            getattr(c, "title", "") or "",
+        ]
+        # Also check evidence snippets if available
+        evidence = getattr(c, "evidence", None)
+        if evidence:
+            texts.append(str(evidence))
+        for text in texts:
+            for match in _REPO_URL_PATTERN.findall(text):
+                # Normalize: strip trailing .git, lowercase
+                repo = match.rstrip(".git").lower()
+                owner = repo.split("/")[0]
+                if owner in _SKIP_PATHS:
+                    continue
+                if repo not in seen:
+                    seen.add(repo)
+                    repos.append(match)  # preserve original case
+    return repos
+
+
+def enrich_candidates_with_stars(
+    candidates: List[Any],
+    token: Optional[str] = None,
+    already_enriched: Optional[set] = None,
+    max_repos: int = 10,
+) -> int:
+    """Annotate candidates with live GitHub star counts.
+
+    Returns the number of repos enriched.
+    """
+    resolved_token = _resolve_token(token)
+    if not resolved_token:
+        return 0
+
+    refs = extract_repo_refs(candidates)
+    if not refs:
+        return 0
+
+    skip = already_enriched or set()
+    to_fetch = [r for r in refs if r.lower() not in {s.lower() for s in skip}][:max_repos]
+    if not to_fetch:
+        return 0
+
+    _log(f"Star enrichment: fetching {len(to_fetch)} repos")
+
+    # Parallel fetch star counts
+    star_map: Dict[str, int] = {}
+    with ThreadPoolExecutor(max_workers=min(8, len(to_fetch))) as executor:
+        futures = {executor.submit(_fetch_repo_info, repo, resolved_token): repo for repo in to_fetch}
+        for future in as_completed(futures):
+            repo = futures[future]
+            try:
+                info = future.result(timeout=10)
+                if info:
+                    star_map[repo.lower()] = info["stars"]
+            except Exception:
+                pass
+
+    if not star_map:
+        return 0
+
+    # Annotate candidates
+    enriched_count = 0
+    for c in candidates:
+        texts = [getattr(c, "url", "") or "", getattr(c, "title", "") or ""]
+        evidence = getattr(c, "evidence", None)
+        if evidence:
+            texts.append(str(evidence))
+        combined = " ".join(texts)
+        for match in _REPO_URL_PATTERN.findall(combined):
+            repo_lower = match.rstrip(".git").lower()
+            if repo_lower in star_map:
+                stars = star_map[repo_lower]
+                stars_str = _format_stars(stars)
+                # Add to metadata
+                if not hasattr(c, "metadata") or c.metadata is None:
+                    continue
+                if "github_stars" not in c.metadata:
+                    c.metadata["github_stars"] = {}
+                c.metadata["github_stars"][match] = stars
+                # Append to evidence if present
+                if hasattr(c, "evidence") and c.evidence and f"(live:" not in c.evidence:
+                    c.evidence = c.evidence + f" (live: {stars_str} stars)"
+                enriched_count += 1
+                break  # one annotation per candidate
+
+    _log(f"Star enrichment: annotated {enriched_count} candidates")
+    return enriched_count

scripts/lib/grounding.py

@@ -0,0 +1,259 @@
+"""Web search retrieval via Brave Search, Exa, and Serper."""
+
+from __future__ import annotations
+
+import urllib.parse
+from datetime import datetime
+from urllib.parse import urlparse
+
+from . import dates, http
+
+
+# ---------------------------------------------------------------------------
+# Brave Search API
+# ---------------------------------------------------------------------------
+
+def brave_search(
+    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
+) -> tuple[list[dict], dict]:
+    url = (
+        "https://api.search.brave.com/res/v1/web/search?"
+        + urllib.parse.urlencode(
+            {
+                "q": query,
+                "count": count,
+                "freshness": f"{date_range[0]}to{date_range[1]}",
+            }
+        )
+    )
+    data = http.request("GET", url, headers={"X-Subscription-Token": api_key}, timeout=15)
+    items = []
+    for i, r in enumerate((data.get("web", {}).get("results", []))[:count]):
+        raw_date = r.get("page_age") or ""
+        pub_date = _normalize_date(raw_date[:10]) if raw_date else None
+        if not _in_date_range(pub_date, date_range):
+            continue
+        items.append({
+            "id": f"WB{i + 1}",
+            "title": r.get("title", ""),
+            "url": r.get("url", ""),
+            "source_domain": _domain(r.get("url", "")),
+            "snippet": r.get("description", ""),
+            "date": pub_date,
+            "relevance": 0.8,
+            "why_relevant": "Brave web search",
+        })
+    artifact = {"label": "brave", "webSearchQueries": [query], "resultCount": len(items)}
+    return items, artifact
+
+
+# ---------------------------------------------------------------------------
+# Exa AI Search
+# ---------------------------------------------------------------------------
+
+def exa_search(
+    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
+) -> tuple[list[dict], dict]:
+    data = http.request(
+        "POST", "https://api.exa.ai/search",
+        headers={"x-api-key": api_key},
+        json_data={
+            "query": query,
+            "type": "auto",
+            "numResults": count,
+            "startPublishedDate": f"{date_range[0]}T00:00:00.000Z",
+            "endPublishedDate": f"{date_range[1]}T23:59:59.999Z",
+            "contents": {"text": {"maxCharacters": 2000}},
+        },
+        timeout=15,
+    )
+    items = []
+    for i, r in enumerate((data.get("results", []))[:count]):
+        if not isinstance(r, dict):
+            continue
+        url = r.get("url", "")
+        if not url:
+            continue
+        raw_date = r.get("publishedDate") or ""
+        pub_date = _normalize_date(raw_date.split("T")[0] if "T" in raw_date else raw_date[:10]) if raw_date else None
+        if not _in_date_range(pub_date, date_range):
+            continue
+        items.append({
+            "id": f"WE{i + 1}",
+            "title": r.get("title", ""),
+            "url": url,
+            "source_domain": _domain(url),
+            "snippet": (r.get("text") or "")[:500],
+            "date": pub_date,
+            "relevance": 0.8,
+            "why_relevant": "Exa web search",
+        })
+    artifact = {"label": "exa", "webSearchQueries": [query], "resultCount": len(items)}
+    return items, artifact
+
+
+# ---------------------------------------------------------------------------
+# Serper (Google Search wrapper)
+# ---------------------------------------------------------------------------
+
+def serper_search(
+    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
+) -> tuple[list[dict], dict]:
+    data = http.request(
+        "POST", "https://google.serper.dev/search",
+        headers={"X-API-KEY": api_key},
+        json_data={
+            "q": query,
+            "num": count,
+            "tbs": f"cdr:1,cd_min:{_serper_date_param(date_range[0])},cd_max:{_serper_date_param(date_range[1])}",
+        },
+        timeout=15,
+    )
+    items = []
+    for i, r in enumerate((data.get("organic", []))[:count]):
+        raw_date = r.get("date") or ""
+        pub_date = _parse_serper_date(raw_date)
+        if not _in_date_range(pub_date, date_range):
+            continue
+        items.append({
+            "id": f"WS{i + 1}",
+            "title": r.get("title", ""),
+            "url": r.get("link", ""),
+            "source_domain": _domain(r.get("link", "")),
+            "snippet": r.get("snippet", ""),
+            "date": pub_date,
+            "relevance": 0.8,
+            "why_relevant": "Serper web search",
+        })
+    artifact = {"label": "serper", "webSearchQueries": [query], "resultCount": len(items)}
+    return items, artifact
+
+
+# ---------------------------------------------------------------------------
+# Parallel AI Search
+# ---------------------------------------------------------------------------
+
+def parallel_search(
+    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
+) -> tuple[list[dict], dict]:
+    data = http.request(
+        "POST", "https://api.parallel.ai/v1/search",
+        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+        json_data={"query": query, "max_results": count},
+        timeout=15,
+    )
+    items = []
+    for i, r in enumerate((data.get("results", []))[:count]):
+        if not isinstance(r, dict):
+            continue
+        url = r.get("url", "")
+        if not url:
+            continue
+        raw_date = r.get("published_date") or ""
+        pub_date = _normalize_date(raw_date[:10]) if raw_date else None
+        if not _in_date_range(pub_date, date_range):
+            continue
+        items.append({
+            "id": f"WP{i + 1}",
+            "title": r.get("title", ""),
+            "url": url,
+            "source_domain": _domain(url),
+            "snippet": r.get("snippet", ""),
+            "date": pub_date,
+            "relevance": 0.8,
+            "why_relevant": "Parallel AI web search",
+        })
+    artifact = {"label": "parallel", "webSearchQueries": [query], "resultCount": len(items)}
+    return items, artifact
+
+
+def _parse_serper_date(raw: str) -> str | None:
+    if not raw:
+        return None
+    normalized = _normalize_date(raw)
+    if normalized:
+        return normalized
+    for fmt in ("%b %d, %Y", "%B %d, %Y", "%Y-%m-%d"):
+        try:
+            return datetime.strptime(raw.strip(), fmt).date().isoformat()
+        except ValueError:
+            continue
+    return None
+
+
+
+
+# ---------------------------------------------------------------------------
+# Dispatcher
+# ---------------------------------------------------------------------------
+
+def web_search(
+    query: str,
+    date_range: tuple[str, str],
+    config: dict,
+    backend: str = "auto",
+) -> tuple[list[dict], dict]:
+    """Run web search with the specified or auto-detected backend."""
+    if backend == "auto":
+        if config.get("BRAVE_API_KEY"):
+            backend = "brave"
+        elif config.get("EXA_API_KEY"):
+            backend = "exa"
+        elif config.get("SERPER_API_KEY"):
+            backend = "serper"
+        elif config.get("PARALLEL_API_KEY"):
+            backend = "parallel"
+        else:
+            return [], {}
+    if backend == "brave":
+        key = config.get("BRAVE_API_KEY")
+        if not key:
+            raise RuntimeError("BRAVE_API_KEY is required when web_backend='brave'")
+        return brave_search(query, date_range, key)
+    if backend == "exa":
+        key = config.get("EXA_API_KEY")
+        if not key:
+            raise RuntimeError("EXA_API_KEY is required when web_backend='exa'")
+        return exa_search(query, date_range, key)
+    if backend == "serper":
+        key = config.get("SERPER_API_KEY")
+        if not key:
+            raise RuntimeError("SERPER_API_KEY is required when web_backend='serper'")
+        return serper_search(query, date_range, key)
+    if backend == "parallel":
+        key = config.get("PARALLEL_API_KEY")
+        if not key:
+            raise RuntimeError("PARALLEL_API_KEY is required when web_backend='parallel'")
+        return parallel_search(query, date_range, key)
+    if backend != "none":
+        raise ValueError(f"Unsupported web backend: {backend!r}")
+    return [], {}
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _normalize_date(value: object) -> str | None:
+    if value is None:
+        return None
+    parsed = dates.parse_date(str(value).strip())
+    if not parsed:
+        return None
+    return parsed.date().isoformat()
+
+
+def _serper_date_param(iso_date: str) -> str:
+    """Convert YYYY-MM-DD to MM/DD/YYYY for Serper tbs parameter."""
+    parts = iso_date.split("-")
+    return f"{parts[1]}/{parts[2]}/{parts[0]}"
+
+
+def _in_date_range(pub_date: str | None, date_range: tuple[str, str]) -> bool:
+    if not pub_date:
+        return False
+    return date_range[0] <= pub_date <= date_range[1]
+
+
+def _domain(url: str) -> str:
+    return urlparse(url).netloc.strip().lower()

scripts/lib/hackernews.py

@@ -0,0 +1,301 @@
+"""Hacker News search via Algolia API (free, no auth required).
+
+Uses hn.algolia.com/api/v1 for story discovery and comment enrichment.
+No API key needed - just HTTP calls via stdlib urllib.
+"""
+
+import datetime
+import html
+import math
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List, Optional
+
+import re
+
+from . import http, log
+from .query import extract_core_subject
+from .relevance import token_overlap_relevance
+
+# Common HN prefixes that can cause false-positive keyword matches
+_HN_PREFIXES = re.compile(r"^(Tell HN|Show HN|Ask HN|Launch HN)\s*:\s*", re.IGNORECASE)
+
+ALGOLIA_SEARCH_URL = "https://hn.algolia.com/api/v1/search"
+ALGOLIA_SEARCH_BY_DATE_URL = "https://hn.algolia.com/api/v1/search_by_date"
+ALGOLIA_ITEM_URL = "https://hn.algolia.com/api/v1/items"
+
+DEPTH_CONFIG = {
+    "quick": 15,
+    "default": 30,
+    "deep": 60,
+}
+
+ENRICH_LIMITS = {
+    "quick": 3,
+    "default": 5,
+    "deep": 10,
+}
+
+
+def _log(msg: str):
+    log.source_log("HN", msg)
+
+
+def _date_to_unix(date_str: str) -> int:
+    """Convert YYYY-MM-DD to Unix timestamp (start of day UTC)."""
+    parts = date_str.split("-")
+    year, month, day = int(parts[0]), int(parts[1]), int(parts[2])
+    dt = datetime.datetime(year, month, day, tzinfo=datetime.timezone.utc)
+    return int(dt.timestamp())
+
+
+def _unix_to_date(ts: int) -> str:
+    """Convert Unix timestamp to YYYY-MM-DD."""
+    dt = datetime.datetime.fromtimestamp(ts, tz=datetime.timezone.utc)
+    return dt.strftime("%Y-%m-%d")
+
+
+def _strip_html(text: str) -> str:
+    """Strip HTML tags and decode entities from HN comment text."""
+    import re
+    text = html.unescape(text)
+    text = re.sub(r'<p>', '\n', text)
+    text = re.sub(r'<[^>]+>', '', text)
+    return text.strip()
+
+
+def search_hackernews(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+) -> Dict[str, Any]:
+    """Search Hacker News via Algolia API.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+
+    Returns:
+        Dict with Algolia response (contains 'hits' list).
+    """
+    count = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    from_ts = _date_to_unix(from_date)
+    to_ts = _date_to_unix(to_date) + 86400  # Include the end date
+
+    # Use extracted core subject instead of raw topic for cleaner Algolia matching
+    core = extract_core_subject(topic)
+    _log(f"Searching for '{core}' (raw: '{topic}', since {from_date}, count={count})")
+
+    # Use relevance-sorted search with minimum engagement filter.
+    # NOTE: restrictSearchableAttributes=title omitted intentionally — it would
+    # miss Ask HN/Show HN threads where the topic appears in the body.
+    params = {
+        "query": core,
+        "tags": "story",
+        "numericFilters": f"created_at_i>{from_ts},created_at_i<{to_ts},points>2",
+        "hitsPerPage": str(count),
+    }
+
+    from urllib.parse import urlencode
+    url = f"{ALGOLIA_SEARCH_URL}?{urlencode(params)}"
+
+    try:
+        response = http.request("GET", url, timeout=30)
+    except http.HTTPError as e:
+        _log(f"Search failed: {e}")
+        return {"hits": [], "error": str(e)}
+    except Exception as e:
+        _log(f"Search failed: {e}")
+        return {"hits": [], "error": str(e)}
+
+    hits = response.get("hits", [])
+    _log(f"Found {len(hits)} stories")
+    return response
+
+
+def _title_matches_query(title: str, query: str, author: str = "") -> bool:
+    """Check if the query term appears in the title content, not just an HN prefix or author.
+
+    Returns True if the query (or any multi-word token) appears in the title
+    after stripping "Tell HN:", "Show HN:", "Ask HN:", "Launch HN:" prefixes
+    and ignoring the author name.  Returns True when query is empty (no filter).
+    """
+    if not query:
+        return True
+    stripped = _HN_PREFIXES.sub("", title).strip()
+    # Also check that the match isn't solely in the author's username
+    check_text = stripped.lower()
+    query_lower = query.lower()
+    # Check each word of the query independently; all must appear somewhere
+    # in the stripped title (not just the prefix).
+    query_words = query_lower.split()
+    for word in query_words:
+        if word in check_text:
+            continue
+        # Word not found in stripped title — reject
+        return False
+    return True
+
+
+def parse_hackernews_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
+    """Parse Algolia response into normalized item dicts.
+
+    Args:
+        response: Algolia search response
+        query: Original search query for token-overlap relevance scoring
+
+    Returns:
+        List of item dicts ready for normalization.
+    """
+    hits = response.get("hits", [])
+    # Post-filter: remove items where query only matched an HN prefix like "Tell HN:"
+    if query:
+        before = len(hits)
+        hits = [
+            h for h in hits
+            if _title_matches_query(h.get("title", ""), query, h.get("author", ""))
+        ]
+        dropped = before - len(hits)
+        if dropped:
+            _log(f"Prefix filter removed {dropped}/{before} false-positive hits for '{query}'")
+    items = []
+
+    for i, hit in enumerate(hits):
+        object_id = hit.get("objectID", "")
+        points = hit.get("points") or 0
+        num_comments = hit.get("num_comments") or 0
+        created_at_i = hit.get("created_at_i")
+
+        date_str = None
+        if created_at_i:
+            date_str = _unix_to_date(created_at_i)
+
+        # Article URL vs HN discussion URL
+        article_url = hit.get("url") or ""
+        hn_url = f"https://news.ycombinator.com/item?id={object_id}"
+
+        # Relevance: blend Algolia rank with token-overlap content matching
+        rank_score = max(0.3, 1.0 - (i * 0.02))  # 1.0 -> 0.3 over 35 items
+        engagement_boost = min(0.2, math.log1p(points) / 40)
+        if query:
+            content_score = token_overlap_relevance(query, hit.get("title", ""))
+            relevance = min(1.0, 0.6 * rank_score + 0.4 * content_score + engagement_boost)
+        else:
+            relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
+
+        items.append({
+            "id": object_id,
+            "title": hit.get("title", ""),
+            "url": article_url,
+            "hn_url": hn_url,
+            "author": hit.get("author", ""),
+            "date": date_str,
+            "engagement": {
+                "points": points,
+                "comments": num_comments,
+            },
+            "relevance": round(relevance, 2),
+            "why_relevant": f"HN story about {hit.get('title', 'topic')[:60]}",
+        })
+
+    return items
+
+
+def _fetch_item_comments(object_id: str, max_comments: int = 5) -> Dict[str, Any]:
+    """Fetch top-level comments for a story from Algolia items endpoint.
+
+    Args:
+        object_id: HN story ID
+        max_comments: Max comments to return
+
+    Returns:
+        Dict with 'comments' list and 'comment_insights' list.
+    """
+    url = f"{ALGOLIA_ITEM_URL}/{object_id}"
+
+    try:
+        data = http.request("GET", url, timeout=15)
+    except Exception as e:
+        _log(f"Failed to fetch comments for {object_id}: {e}")
+        return {"comments": [], "comment_insights": []}
+
+    children = data.get("children", [])
+
+    # Sort by points (highest first), filter to actual comments
+    real_comments = [
+        c for c in children
+        if c.get("text") and c.get("author")
+    ]
+    real_comments.sort(key=lambda c: c.get("points") or 0, reverse=True)
+
+    comments = []
+    insights = []
+    for c in real_comments[:max_comments]:
+        text = _strip_html(c.get("text", ""))
+        excerpt = text[:300] + "..." if len(text) > 300 else text
+        comments.append({
+            "author": c.get("author", ""),
+            "text": excerpt,
+            "points": c.get("points") or 0,
+        })
+        # First sentence as insight
+        first_sentence = text.split(". ")[0].split("\n")[0][:200]
+        if first_sentence:
+            insights.append(first_sentence)
+
+    return {"comments": comments, "comment_insights": insights}
+
+
+def enrich_top_stories(
+    items: List[Dict[str, Any]],
+    depth: str = "default",
+) -> List[Dict[str, Any]]:
+    """Fetch comments for top N stories by points.
+
+    Args:
+        items: Parsed HN items
+        depth: Research depth (controls how many to enrich)
+
+    Returns:
+        Items with top_comments and comment_insights added.
+    """
+    if not items:
+        return items
+
+    limit = ENRICH_LIMITS.get(depth, ENRICH_LIMITS["default"])
+
+    # Sort by points to enrich the most popular stories
+    by_points = sorted(
+        range(len(items)),
+        key=lambda i: items[i].get("engagement", {}).get("points", 0),
+        reverse=True,
+    )
+    to_enrich = by_points[:limit]
+
+    _log(f"Enriching top {len(to_enrich)} stories with comments")
+
+    with ThreadPoolExecutor(max_workers=5) as executor:
+        futures = {
+            executor.submit(
+                _fetch_item_comments,
+                items[idx]["id"],
+            ): idx
+            for idx in to_enrich
+        }
+
+        for future in as_completed(futures):
+            idx = futures[future]
+            try:
+                result = future.result(timeout=15)
+                items[idx]["top_comments"] = result["comments"]
+                items[idx]["comment_insights"] = result["comment_insights"]
+            except (KeyError, TypeError, OSError) as exc:
+                _log(f"Comment enrichment failed for story {items[idx].get('id', '?')}: {type(exc).__name__}: {exc}")
+                items[idx]["top_comments"] = []
+                items[idx]["comment_insights"] = []
+
+    return items

scripts/lib/http.py

@@ -1,26 +1,28 @@
 """HTTP utilities for last30days skill (stdlib only)."""
 
 import json
-import os
+import re
 import sys
 import time
 import urllib.error
 import urllib.request
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Union
 from urllib.parse import urlencode
 
+from . import log as _log
+
 DEFAULT_TIMEOUT = 30
-DEBUG = os.environ.get("LAST30DAYS_DEBUG", "").lower() in ("1", "true", "yes")
 
 
 def log(msg: str):
     """Log debug message to stderr."""
-    if DEBUG:
-        sys.stderr.write(f"[DEBUG] {msg}\n")
-        sys.stderr.flush()
-MAX_RETRIES = 3
-RETRY_DELAY = 1.0
-USER_AGENT = "last30days-skill/1.0 (Claude Code Skill)"
+    _log.debug(msg)
+
+
+MAX_RETRIES = 5
+MAX_429_RETRIES = 2
+RETRY_DELAY = 2.0
+USER_AGENT = "last30days-skill/3.0 (Assistant Skill)"
 
 
 class HTTPError(Exception):
@@ -36,9 +38,12 @@ def request(
     url: str,
     headers: Optional[Dict[str, str]] = None,
     json_data: Optional[Dict[str, Any]] = None,
+    params: Optional[Dict[str, Any]] = None,
     timeout: int = DEFAULT_TIMEOUT,
     retries: int = MAX_RETRIES,
-) -> Dict[str, Any]:
+    max_429_retries: int = MAX_429_RETRIES,
+    raw: bool = False,
+) -> Union[Dict[str, Any], str]:
     """Make an HTTP request and return JSON response.
 
     Args:
@@ -46,11 +51,15 @@ def request(
         url: Request URL
         headers: Optional headers dict
         json_data: Optional JSON body (for POST)
+        params: Optional query-string params. Values are stringified. None values
+            are dropped. If ``url`` already has a query string, ``params`` is appended.
         timeout: Request timeout in seconds
         retries: Number of retries on failure
+        max_429_retries: Maximum 429 retries before giving up (separate cap)
+        raw: If True, return raw response text instead of parsed JSON
 
     Returns:
-        Parsed JSON response
+        Parsed JSON response as dict, or raw text string if raw=True.
 
     Raises:
         HTTPError: On request failure
@@ -58,6 +67,12 @@ def request(
     headers = headers or {}
     headers.setdefault("User-Agent", USER_AGENT)
 
+    if params:
+        filtered = {k: str(v) for k, v in params.items() if v is not None}
+        if filtered:
+            separator = "&" if ("?" in url) else "?"
+            url = f"{url}{separator}{urlencode(filtered)}"
+
     data = None
     if json_data is not None:
         data = json.dumps(json_data).encode('utf-8')
@@ -65,34 +80,56 @@ def request(
 
     req = urllib.request.Request(url, data=data, headers=headers, method=method)
 
-    log(f"{method} {url}")
-    if json_data:
-        log(f"Payload keys: {list(json_data.keys())}")
+    safe_url = re.sub(r'([?&])(key|api_key|token|secret)=[^&]*', r'\1\2=***', url)
+    log(f"{method} {safe_url}")
 
     last_error = None
+    rate_limit_count = 0
     for attempt in range(retries):
         try:
             with urllib.request.urlopen(req, timeout=timeout) as response:
                 body = response.read().decode('utf-8')
                 log(f"Response: {response.status} ({len(body)} bytes)")
+                if raw:
+                    return body
                 return json.loads(body) if body else {}
         except urllib.error.HTTPError as e:
             body = None
             try:
                 body = e.read().decode('utf-8')
-            except:
+            except (OSError, UnicodeDecodeError):
                 pass
             log(f"HTTP Error {e.code}: {e.reason}")
             if body:
-                log(f"Error body: {body[:500]}")
+                snippet = " ".join(body.split())
+                log(f"Error body: {snippet[:200]}")
             last_error = HTTPError(f"HTTP {e.code}: {e.reason}", e.code, body)
 
             # Don't retry client errors (4xx) except rate limits
             if 400 <= e.code < 500 and e.code != 429:
                 raise last_error
 
+            # Cap 429 retries separately to avoid wasting latency
+            if e.code == 429:
+                rate_limit_count += 1
+                if rate_limit_count >= max_429_retries:
+                    raise last_error
+
             if attempt < retries - 1:
-                time.sleep(RETRY_DELAY * (attempt + 1))
+                if e.code == 429:
+                    # Respect Retry-After header, fall back to exponential backoff
+                    retry_after = e.headers.get("Retry-After") if hasattr(e, 'headers') else None
+                    if retry_after:
+                        try:
+                            delay = float(retry_after)
+                        except ValueError:
+                            delay = RETRY_DELAY * (2 ** attempt) + 1
+                    else:
+                        delay = RETRY_DELAY * (2 ** attempt) + 1  # 3s, 5s, 9s...
+                    log(f"Rate limited (429). Waiting {delay:.1f}s before retry {attempt + 2}/{retries}")
+                else:
+                    delay = RETRY_DELAY * (2 ** attempt)
+                time.sleep(delay)
         except urllib.error.URLError as e:
             log(f"URL Error: {e.reason}")
             last_error = HTTPError(f"URL Error: {e.reason}")
@@ -102,6 +139,12 @@ def request(
             log(f"JSON decode error: {e}")
             last_error = HTTPError(f"Invalid JSON response: {e}")
             raise last_error
+        except (OSError, TimeoutError, ConnectionResetError) as e:
+            # Handle socket-level errors (connection reset, timeout, etc.)
+            log(f"Connection error: {type(e).__name__}: {e}")
+            last_error = HTTPError(f"Connection error: {type(e).__name__}: {e}")
+            if attempt < retries - 1:
+                time.sleep(RETRY_DELAY * (attempt + 1))
 
     if last_error:
         raise last_error
@@ -118,11 +161,26 @@ def post(url: str, json_data: Dict[str, Any], headers: Optional[Dict[str, str]]
     return request("POST", url, headers=headers, json_data=json_data, **kwargs)
 
 
-def get_reddit_json(path: str) -> Dict[str, Any]:
+def post_raw(url: str, json_data: Dict[str, Any], headers: Optional[Dict[str, str]] = None, **kwargs) -> str:
+    """Make a POST request with JSON body and return raw text."""
+    return request("POST", url, headers=headers, json_data=json_data, raw=True, **kwargs)
+
+
+def scrapecreators_headers(token: str) -> Dict[str, str]:
+    """Build ScrapeCreators request headers (x-api-key + JSON content type)."""
+    return {
+        "x-api-key": token,
+        "Content-Type": "application/json",
+    }
+
+
+def get_reddit_json(path: str, timeout: int = DEFAULT_TIMEOUT, retries: int = MAX_RETRIES) -> Dict[str, Any]:
     """Fetch Reddit thread JSON.
 
     Args:
         path: Reddit path (e.g., /r/subreddit/comments/id/title)
+        timeout: HTTP timeout per attempt in seconds
+        retries: Number of retries on failure
 
     Returns:
         Parsed JSON response
@@ -143,4 +201,4 @@ def get_reddit_json(path: str) -> Dict[str, Any]:
         "Accept": "application/json",
     }
 
-    return get(url, headers=headers)
+    return get(url, headers=headers, timeout=timeout, retries=retries)

scripts/lib/instagram.py

@@ -0,0 +1,500 @@
+"""Instagram Reels search via ScrapeCreators API for /last30days.
+
+Uses ScrapeCreators REST API to search Instagram Reels by keyword, extract
+engagement metrics (views, likes, comments), and fetch video transcripts.
+
+Requires SCRAPECREATORS_API_KEY in config. 100 free API calls, then PAYG.
+API docs: https://scrapecreators.com/docs
+"""
+
+import re
+import sys
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Set
+
+try:
+    import requests as _requests
+except ImportError:
+    _requests = None
+
+from . import dates, http, log
+
+SCRAPECREATORS_BASE = "https://api.scrapecreators.com"
+
+# Depth configurations: how many results to fetch / captions to extract
+DEPTH_CONFIG = {
+    "quick":   {"results_per_page": 10, "max_captions": 3},
+    "default": {"results_per_page": 20, "max_captions": 5},
+    "deep":    {"results_per_page": 40, "max_captions": 8},
+}
+
+# Max words to keep from each caption
+CAPTION_MAX_WORDS = 500
+
+from .relevance import token_overlap_relevance as _compute_relevance
+
+
+def _extract_core_subject(topic: str) -> str:
+    """Extract core subject from verbose query for Instagram search."""
+    from .query import extract_core_subject
+    _INSTAGRAM_NOISE = frozenset({
+        'best', 'top', 'good', 'great', 'awesome', 'killer',
+        'latest', 'new', 'news', 'update', 'updates',
+        'trending', 'hottest', 'popular', 'viral',
+        'practices', 'features',
+        'recommendations', 'advice',
+        'prompt', 'prompts', 'prompting',
+        'methods', 'strategies', 'approaches',
+    })
+    return extract_core_subject(topic, noise=_INSTAGRAM_NOISE)
+
+
+def _infer_query_intent(topic: str) -> str:
+    """Tiny local intent classifier for Instagram query expansion."""
+    text = topic.lower().strip()
+    if re.search(r"\b(vs|versus|compare|difference between)\b", text):
+        return "comparison"
+    if re.search(r"\b(how to|tutorial|guide|setup|step by step|deploy|install)\b", text):
+        return "how_to"
+    if re.search(r"\b(thoughts on|worth it|should i|opinion|review)\b", text):
+        return "opinion"
+    if re.search(r"\b(pricing|feature|features|best .* for)\b", text):
+        return "product"
+    return "breaking_news"
+
+
+def expand_instagram_queries(topic: str, depth: str) -> List[str]:
+    """Generate multiple Instagram search queries from a topic.
+
+    Mirrors reddit.py's expand_reddit_queries() pattern:
+    1. Extract core subject (strip noise words)
+    2. Include original topic if different from core
+    3. Add intent-specific OR-joined content-type variants
+    4. Cap by depth: 1 for quick, 2 for default, 3 for deep
+
+    Returns 1-3 query strings depending on depth.
+    """
+    core = _extract_core_subject(topic)
+    queries = [core]
+
+    # Include cleaned original topic as variant if different from core
+    original_clean = topic.strip().rstrip('?!.')
+    if core.lower() != original_clean.lower() and len(original_clean.split()) <= 8:
+        queries.append(original_clean)
+
+    qtype = _infer_query_intent(topic)
+
+    # Intent-specific Instagram content-type variants
+    if qtype == "breaking_news":
+        queries.append(f"{core} reaction OR edit")
+    elif qtype == "opinion":
+        queries.append(f"{core} reaction OR edit")
+    elif qtype == "product":
+        queries.append(f"{core} review OR haul")
+    elif qtype == "comparison":
+        queries.append(f"{core} vs OR compared")
+    elif qtype == "how_to":
+        queries.append(f"{core} tutorial OR hack")
+    else:
+        queries.append(f"{core} reaction OR edit")
+
+    # Deep depth: add viral content variant
+    if depth == "deep":
+        queries.append(f"{core} viral OR trending OR reel")
+
+    # Cap by depth budget
+    caps = {"quick": 1, "default": 2, "deep": 3}
+    cap = caps.get(depth, 2)
+    return queries[:cap]
+
+
+def _log(msg: str):
+    log.source_log("Instagram", msg)
+
+
+def _parse_date(item: Dict[str, Any]) -> Optional[str]:
+    """Parse date from ScrapeCreators Instagram item to YYYY-MM-DD.
+
+    Handles taken_at as ISO string (e.g. "2026-02-26T16:00:00.000Z")
+    or unix timestamp.
+    """
+    ts = item.get("taken_at")
+    if not ts:
+        return None
+
+    # Try ISO string first (ScrapeCreators reels/search returns this)
+    if isinstance(ts, str):
+        try:
+            # Handle "2026-02-26T16:00:00.000Z" format
+            dt = datetime.fromisoformat(ts.replace("Z", "+00:00"))
+            return dt.strftime("%Y-%m-%d")
+        except (ValueError, TypeError):
+            pass
+        # Try just the date portion
+        if len(ts) >= 10:
+            return ts[:10]
+
+    # Fall back to unix timestamp
+    try:
+        return dates.timestamp_to_date(int(ts))
+    except (ValueError, TypeError):
+        pass
+
+    return None
+
+
+def _extract_hashtags(caption_text: str) -> List[str]:
+    """Extract hashtags from Instagram caption text."""
+    if not caption_text:
+        return []
+    return re.findall(r'#(\w+)', caption_text)
+
+
+def _parse_items(raw_items: List[Dict[str, Any]], core_topic: str) -> List[Dict[str, Any]]:
+    """Parse raw Instagram items into normalized dicts."""
+    items = []
+    for raw in raw_items:
+        if not isinstance(raw, dict):
+            continue
+
+        # Extract reel ID and shortcode
+        reel_pk = str(raw.get("id", raw.get("pk", "")))
+        shortcode = raw.get("shortcode", raw.get("code", ""))
+
+        # Caption text -- can be a string or dict depending on endpoint
+        caption_obj = raw.get("caption", "")
+        if isinstance(caption_obj, dict):
+            text = caption_obj.get("text", "")
+        elif isinstance(caption_obj, str):
+            text = caption_obj
+        else:
+            text = raw.get("desc", raw.get("text", ""))
+
+        # Engagement metrics
+        play_count = raw.get("video_play_count") or raw.get("video_view_count") or raw.get("play_count") or 0
+        like_count = raw.get("like_count") or 0
+        comment_count = raw.get("comment_count") or 0
+
+        # Author info -- 'owner' in reels/search, 'user' in user/reels
+        owner_raw = raw.get("owner") or raw.get("user")
+        if isinstance(owner_raw, dict):
+            author_name = owner_raw.get("username", "")
+        elif isinstance(owner_raw, str):
+            author_name = owner_raw
+        else:
+            author_name = ""
+
+        # Duration
+        duration = raw.get("video_duration")
+
+        # Date
+        date_str = _parse_date(raw)
+
+        # Hashtags from caption text
+        hashtags = _extract_hashtags(text)
+
+        # Compute relevance with hashtag boost
+        relevance = _compute_relevance(core_topic, text, hashtags)
+
+        # Build URL -- prefer API-provided url, fallback to shortcode
+        url = raw.get("url", "")
+        if not url and shortcode:
+            url = f"https://www.instagram.com/reel/{shortcode}"
+
+        items.append({
+            "video_id": reel_pk,
+            "text": text,
+            "url": url,
+            "author_name": author_name,
+            "date": date_str,
+            "engagement": {
+                "views": play_count,
+                "likes": like_count,
+                "comments": comment_count,
+            },
+            "hashtags": hashtags,
+            "duration": duration,
+            "relevance": relevance,
+            "why_relevant": f"Instagram: {text[:60]}" if text else f"Instagram: {core_topic}",
+            "caption_snippet": "",  # populated by fetch_captions
+        })
+    return items
+
+
+def _user_reels(
+    handle: str,
+    token: str,
+) -> List[Dict[str, Any]]:
+    """Fetch an Instagram user's recent reels via ScrapeCreators.
+
+    Args:
+        handle: Instagram username (without @)
+        token: ScrapeCreators API key
+
+    Returns:
+        List of raw Instagram reel dicts.
+    """
+    _log(f"User reels: @{handle}")
+    reels_url = f"{SCRAPECREATORS_BASE}/v1/instagram/user/reels"
+    if not _requests:
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({"handle": handle})
+            url = f"{reels_url}?{params}"
+            headers = http.scrapecreators_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(url, headers=headers, timeout=30, retries=2)
+        except Exception as e:
+            _log(f"User reels error (urllib) for @{handle}: {e}")
+            return []
+    else:
+        try:
+            resp = _requests.get(
+                reels_url,
+                params={"handle": handle},
+                headers=http.scrapecreators_headers(token),
+                timeout=30,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+        except Exception as e:
+            _log(f"User reels error for @{handle}: {e}")
+            return []
+
+    raw_items = data.get("items") or data.get("reels") or data.get("data") or []
+    _log(f"  -> {len(raw_items)} reels from @{handle}")
+    return raw_items
+
+
+def search_instagram(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: str = None,
+) -> Dict[str, Any]:
+    """Search Instagram Reels via ScrapeCreators API.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        token: ScrapeCreators API key
+
+    Returns:
+        Dict with 'items' list and optional 'error'.
+    """
+    if not token:
+        return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
+
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    core_topic = _extract_core_subject(topic)
+
+    _log(f"Searching Instagram for '{core_topic}' (depth={depth}, count={config['results_per_page']})")
+
+    if not _requests:
+        _log("requests library not installed, falling back to urllib")
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({"query": core_topic})
+            url = f"{SCRAPECREATORS_BASE}/v2/instagram/reels/search?{params}"
+            headers = http.scrapecreators_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(url, headers=headers, timeout=30, retries=2)
+        except Exception as e:
+            _log(f"ScrapeCreators error (urllib): {e}")
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
+    else:
+        try:
+            resp = _requests.get(
+                f"{SCRAPECREATORS_BASE}/v2/instagram/reels/search",
+                params={"query": core_topic},
+                headers=http.scrapecreators_headers(token),
+                timeout=30,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+        except Exception as e:
+            _log(f"ScrapeCreators error: {e}")
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
+
+    # Items are in the 'reels' array (ScrapeCreators v2 response)
+    raw_items = data.get("reels") or data.get("items") or data.get("data") or []
+
+    # Limit to configured count
+    raw_items = raw_items[:config["results_per_page"]]
+
+    # Parse items
+    items = _parse_items(raw_items, core_topic)
+
+    # Hard date filter
+    in_range = [i for i in items if i["date"] and from_date <= i["date"] <= to_date]
+    out_of_range = len(items) - len(in_range)
+    if in_range:
+        items = in_range
+        if out_of_range:
+            _log(f"Filtered {out_of_range} reels outside date range")
+    else:
+        _log(f"No reels within date range, keeping all {len(items)}")
+
+    # Sort by views descending
+    items.sort(key=lambda x: x["engagement"]["views"], reverse=True)
+
+    _log(f"Found {len(items)} Instagram reels")
+    return {"items": items}
+
+
+def fetch_captions(
+    video_items: List[Dict[str, Any]],
+    token: str,
+    depth: str = "default",
+) -> Dict[str, str]:
+    """Fetch transcripts for top N Instagram reels via ScrapeCreators.
+
+    Strategy:
+    1. Use the 'text' field (caption) as baseline
+    2. For top N, call /v2/instagram/media/transcript for spoken-word captions
+
+    Args:
+        video_items: Items from search_instagram()
+        token: ScrapeCreators API key
+        depth: Depth level for caption limit
+
+    Returns:
+        Dict mapping video_id -> caption text (truncated to 500 words)
+    """
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    max_captions = config["max_captions"]
+
+    if not video_items or not token or not _requests:
+        return {}
+
+    top_items = video_items[:max_captions]
+    _log(f"Enriching captions for {len(top_items)} reels")
+
+    captions = {}
+
+    # First pass: use text field as caption (always available, free)
+    for item in top_items:
+        vid = item["video_id"]
+        text = item.get("text", "")
+        if text:
+            words = text.split()
+            if len(words) > CAPTION_MAX_WORDS:
+                text = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
+            captions[vid] = text
+
+    # Second pass: try to get spoken-word transcripts (1 credit each)
+    for item in top_items:
+        vid = item["video_id"]
+        url = item.get("url", "")
+        if not url:
+            continue
+        try:
+            resp = _requests.get(
+                f"{SCRAPECREATORS_BASE}/v2/instagram/media/transcript",
+                params={"url": url},
+                headers=http.scrapecreators_headers(token),
+                timeout=15,
+            )
+            if resp.status_code == 200:
+                data = resp.json()
+                transcripts = data.get("transcripts") or []
+                if transcripts and isinstance(transcripts, list):
+                    # Combine all transcript segments
+                    transcript_text = " ".join(
+                        t.get("text", "") for t in transcripts
+                        if isinstance(t, dict) and t.get("text")
+                    )
+                    if transcript_text:
+                        words = transcript_text.split()
+                        if len(words) > CAPTION_MAX_WORDS:
+                            transcript_text = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
+                        captions[vid] = transcript_text
+        except Exception as e:
+            _log(f"Transcript fetch failed for {vid}: {e}")
+
+    got = sum(1 for v in captions.values() if v)
+    _log(f"Got captions for {got}/{len(top_items)} reels")
+    return captions
+
+
+def search_and_enrich(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: str = None,
+    ig_creators: List[str] | None = None,
+) -> Dict[str, Any]:
+    """Full Instagram search: find reels, then fetch captions for top results.
+
+    Uses expand_instagram_queries() to generate multiple search queries,
+    runs ScrapeCreators for each, and merges/deduplicates results by video ID.
+
+    Args:
+        topic: Search topic (raw topic, not planner's narrowed query)
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        token: ScrapeCreators API key
+        ig_creators: Optional list of Instagram creator handles to fetch reels from
+
+    Returns:
+        Dict with 'items' list. Each item has a 'caption_snippet' field.
+    """
+    core_topic = _extract_core_subject(topic)
+    seen_ids: Set[str] = set()
+    items: List[Dict[str, Any]] = []
+    last_error = None
+
+    # Step 0: Creator reels (high-signal, runs first)
+    if ig_creators and token:
+        for creator in ig_creators:
+            raw_items = _user_reels(creator, token)
+            parsed = _parse_items(raw_items, core_topic)
+            for item in parsed:
+                vid = item.get("video_id", "")
+                if vid and vid not in seen_ids:
+                    seen_ids.add(vid)
+                    items.append(item)
+
+    # Step 1: Multi-query keyword search — run ScrapeCreators for each expanded query
+    queries = expand_instagram_queries(topic, depth)
+    for q in queries:
+        search_result = search_instagram(q, from_date, to_date, depth, token)
+        if search_result.get("error"):
+            last_error = search_result["error"]
+        for item in search_result.get("items", []):
+            vid = item.get("video_id", "")
+            if vid and vid not in seen_ids:
+                seen_ids.add(vid)
+                items.append(item)
+
+    # Sort merged results by views descending
+    items.sort(key=lambda x: x.get("engagement", {}).get("views", 0), reverse=True)
+
+    if not items:
+        return {"items": [], "error": last_error}
+
+    # Step 2: Fetch captions for top N
+    captions = fetch_captions(items, token, depth)
+
+    # Step 3: Attach captions to items
+    for item in items:
+        vid = item["video_id"]
+        caption = captions.get(vid)
+        if caption:
+            item["caption_snippet"] = caption
+
+    return {"items": items, "error": last_error}
+
+
+def parse_instagram_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse Instagram search response to normalized format.
+
+    Returns:
+        List of item dicts ready for normalization.
+    """
+    return response.get("items", [])

scripts/lib/log.py

@@ -0,0 +1,28 @@
+"""Shared logging utilities for last30days skill."""
+
+import os
+import sys
+
+DEBUG = os.environ.get("LAST30DAYS_DEBUG", "").lower() in ("1", "true", "yes")
+
+
+def debug(msg: str) -> None:
+    """Log debug message to stderr (only when LAST30DAYS_DEBUG is set)."""
+    if DEBUG:
+        sys.stderr.write(f"[DEBUG] {msg}\n")
+        sys.stderr.flush()
+
+
+def source_log(prefix: str, msg: str, *, tty_only: bool = True) -> None:
+    """Log a source module message to stderr.
+
+    Args:
+        prefix: Source label (e.g. "Reddit", "Bird").
+        msg: Message text.
+        tty_only: If True, only log when stderr is a TTY (avoids cluttering
+                  non-interactive output like Claude Code).
+    """
+    if tty_only and not sys.stderr.isatty():
+        return
+    sys.stderr.write(f"[{prefix}] {msg}\n")
+    sys.stderr.flush()

scripts/lib/models.py

@@ -1,175 +0,0 @@
-"""Model auto-selection for last30days skill."""
-
-import re
-from typing import Dict, List, Optional, Tuple
-
-from . import cache, http
-
-# OpenAI API
-OPENAI_MODELS_URL = "https://api.openai.com/v1/models"
-OPENAI_FALLBACK_MODELS = ["gpt-5.2", "gpt-5.1", "gpt-5", "gpt-4o"]
-
-# xAI API - Agent Tools API requires grok-4 family
-XAI_MODELS_URL = "https://api.x.ai/v1/models"
-XAI_ALIASES = {
-    "latest": "grok-4-1-fast",  # Required for x_search tool
-    "stable": "grok-4-1-fast",
-}
-
-
-def parse_version(model_id: str) -> Optional[Tuple[int, ...]]:
-    """Parse semantic version from model ID.
-
-    Examples:
-        gpt-5 -> (5,)
-        gpt-5.2 -> (5, 2)
-        gpt-5.2.1 -> (5, 2, 1)
-    """
-    match = re.search(r'(\d+(?:\.\d+)*)', model_id)
-    if match:
-        return tuple(int(x) for x in match.group(1).split('.'))
-    return None
-
-
-def is_mainline_openai_model(model_id: str) -> bool:
-    """Check if model is a mainline GPT model (not mini/nano/chat/codex/pro)."""
-    model_lower = model_id.lower()
-
-    # Must be gpt-5 series
-    if not re.match(r'^gpt-5(\.\d+)*$', model_lower):
-        return False
-
-    # Exclude variants
-    excludes = ['mini', 'nano', 'chat', 'codex', 'pro', 'preview', 'turbo']
-    for exc in excludes:
-        if exc in model_lower:
-            return False
-
-    return True
-
-
-def select_openai_model(
-    api_key: str,
-    policy: str = "auto",
-    pin: Optional[str] = None,
-    mock_models: Optional[List[Dict]] = None,
-) -> str:
-    """Select the best OpenAI model based on policy.
-
-    Args:
-        api_key: OpenAI API key
-        policy: 'auto' or 'pinned'
-        pin: Model to use if policy is 'pinned'
-        mock_models: Mock model list for testing
-
-    Returns:
-        Selected model ID
-    """
-    if policy == "pinned" and pin:
-        return pin
-
-    # Check cache first
-    cached = cache.get_cached_model("openai")
-    if cached:
-        return cached
-
-    # Fetch model list
-    if mock_models is not None:
-        models = mock_models
-    else:
-        try:
-            headers = {"Authorization": f"Bearer {api_key}"}
-            response = http.get(OPENAI_MODELS_URL, headers=headers)
-            models = response.get("data", [])
-        except http.HTTPError:
-            # Fall back to known models
-            return OPENAI_FALLBACK_MODELS[0]
-
-    # Filter to mainline models
-    candidates = [m for m in models if is_mainline_openai_model(m.get("id", ""))]
-
-    if not candidates:
-        # No gpt-5 models found, use fallback
-        return OPENAI_FALLBACK_MODELS[0]
-
-    # Sort by version (descending), then by created timestamp
-    def sort_key(m):
-        version = parse_version(m.get("id", "")) or (0,)
-        created = m.get("created", 0)
-        return (version, created)
-
-    candidates.sort(key=sort_key, reverse=True)
-    selected = candidates[0]["id"]
-
-    # Cache the selection
-    cache.set_cached_model("openai", selected)
-
-    return selected
-
-
-def select_xai_model(
-    api_key: str,
-    policy: str = "latest",
-    pin: Optional[str] = None,
-    mock_models: Optional[List[Dict]] = None,
-) -> str:
-    """Select the best xAI model based on policy.
-
-    Args:
-        api_key: xAI API key
-        policy: 'latest', 'stable', or 'pinned'
-        pin: Model to use if policy is 'pinned'
-        mock_models: Mock model list for testing
-
-    Returns:
-        Selected model ID
-    """
-    if policy == "pinned" and pin:
-        return pin
-
-    # Use alias system
-    if policy in XAI_ALIASES:
-        alias = XAI_ALIASES[policy]
-
-        # Check cache first
-        cached = cache.get_cached_model("xai")
-        if cached:
-            return cached
-
-        # Cache the alias
-        cache.set_cached_model("xai", alias)
-        return alias
-
-    # Default to latest
-    return XAI_ALIASES["latest"]
-
-
-def get_models(
-    config: Dict,
-    mock_openai_models: Optional[List[Dict]] = None,
-    mock_xai_models: Optional[List[Dict]] = None,
-) -> Dict[str, Optional[str]]:
-    """Get selected models for both providers.
-
-    Returns:
-        Dict with 'openai' and 'xai' keys
-    """
-    result = {"openai": None, "xai": None}
-
-    if config.get("OPENAI_API_KEY"):
-        result["openai"] = select_openai_model(
-            config["OPENAI_API_KEY"],
-            config.get("OPENAI_MODEL_POLICY", "auto"),
-            config.get("OPENAI_MODEL_PIN"),
-            mock_openai_models,
-        )
-
-    if config.get("XAI_API_KEY"):
-        result["xai"] = select_xai_model(
-            config["XAI_API_KEY"],
-            config.get("XAI_MODEL_POLICY", "latest"),
-            config.get("XAI_MODEL_PIN"),
-            mock_xai_models,
-        )
-
-    return result

scripts/lib/normalize.py

@@ -1,118 +1,498 @@
-"""Normalization of raw API data to canonical schema."""
+"""Normalization of source-specific payloads into the v3 generic item model."""
 
-from typing import Any, Dict, List
+from __future__ import annotations
+
+from typing import Any
+from urllib.parse import urlparse
 
 from . import dates, schema
 
 
-def normalize_reddit_items(
-    items: List[Dict[str, Any]],
+def filter_by_date_range(
+    items: list[schema.SourceItem],
     from_date: str,
     to_date: str,
-) -> List[schema.RedditItem]:
-    """Normalize raw Reddit items to schema.
-
-    Args:
-        items: Raw Reddit items from API
-        from_date: Start of date range
-        to_date: End of date range
-
-    Returns:
-        List of RedditItem objects
-    """
-    normalized = []
-
+    require_date: bool = False,
+) -> list[schema.SourceItem]:
+    """Keep only items within the requested window."""
+    filtered: list[schema.SourceItem] = []
     for item in items:
-        # Parse engagement
-        engagement = None
-        eng_raw = item.get("engagement")
-        if isinstance(eng_raw, dict):
-            engagement = schema.Engagement(
-                score=eng_raw.get("score"),
-                num_comments=eng_raw.get("num_comments"),
-                upvote_ratio=eng_raw.get("upvote_ratio"),
-            )
-
-        # Parse comments
-        top_comments = []
-        for c in item.get("top_comments", []):
-            top_comments.append(schema.Comment(
-                score=c.get("score", 0),
-                date=c.get("date"),
-                author=c.get("author", ""),
-                excerpt=c.get("excerpt", ""),
-                url=c.get("url", ""),
-            ))
-
-        # Determine date confidence
-        date_str = item.get("date")
-        date_confidence = dates.get_date_confidence(date_str, from_date, to_date)
-
-        normalized.append(schema.RedditItem(
-            id=item.get("id", ""),
-            title=item.get("title", ""),
-            url=item.get("url", ""),
-            subreddit=item.get("subreddit", ""),
-            date=date_str,
-            date_confidence=date_confidence,
-            engagement=engagement,
-            top_comments=top_comments,
-            comment_insights=item.get("comment_insights", []),
-            relevance=item.get("relevance", 0.5),
-            why_relevant=item.get("why_relevant", ""),
-        ))
-
-    return normalized
+        if not item.published_at:
+            if not require_date:
+                filtered.append(item)
+            continue
+        if item.published_at < from_date or item.published_at > to_date:
+            continue
+        filtered.append(item)
+    return filtered
 
 
-def normalize_x_items(
-    items: List[Dict[str, Any]],
+def normalize_source_items(
+    source: str,
+    items: list[dict[str, Any]],
     from_date: str,
     to_date: str,
-) -> List[schema.XItem]:
-    """Normalize raw X items to schema.
+    freshness_mode: str = "balanced_recent",
+) -> list[schema.SourceItem]:
+    """Normalize raw source items, filter by date range, with evergreen fallback for how_to queries."""
+    source = source.lower()
+    normalizers = {
+        "reddit": _normalize_reddit,
+        "x": _normalize_x,
+        "youtube": _normalize_youtube,
+        "tiktok": lambda s, i, idx, fd, td: _normalize_shortform_video(s, i, idx, fd, td, "TK", "TikTok post"),
+        "instagram": lambda s, i, idx, fd, td: _normalize_shortform_video(s, i, idx, fd, td, "IG", "Instagram reel"),
+        "hackernews": _normalize_hackernews,
+        "bluesky": lambda s, i, idx, fd, td: _normalize_microblog(s, i, idx, fd, td, "BS", "Bluesky post"),
+        "truthsocial": lambda s, i, idx, fd, td: _normalize_microblog(s, i, idx, fd, td, "TS", "Truth Social post"),
+        "threads": lambda s, i, idx, fd, td: _normalize_microblog(s, i, idx, fd, td, "TH", "Threads post"),
+        "xquik": _normalize_x,
+        "pinterest": _normalize_pinterest,
+        "polymarket": _normalize_polymarket,
+        "grounding": _normalize_grounding,
+        "xiaohongshu": _normalize_grounding,
+        "github": _normalize_github,
+        "perplexity": _normalize_grounding,
+    }
+    normalizer = normalizers.get(source)
+    if normalizer is None:
+        raise ValueError(f"Unsupported source: {source}")
+    normalized = [normalizer(source, item, index, from_date, to_date) for index, item in enumerate(items)]
+    require_date = source == "grounding"
+    filtered = filter_by_date_range(normalized, from_date, to_date, require_date=require_date)
+    if filtered:
+        return filtered
+    if freshness_mode == "evergreen_ok" and source == "youtube":
+        if require_date:
+            return [item for item in normalized if item.published_at]
+        return normalized
+    return filtered
 
-    Args:
-        items: Raw X items from API
-        from_date: Start of date range
-        to_date: End of date range
 
-    Returns:
-        List of XItem objects
+def _remap_comments(
+    raw: list[Any],
+    score_keys: tuple[str, ...],
+    excerpt_keys: tuple[str, ...],
+) -> list[dict[str, Any]]:
+    """Normalize comments from any source into the shared Reddit-compatible shape.
+
+    Downstream code (signals._top_comment_score, render._top_comments_list,
+    entity_extract, rerank) all expect `score` and `excerpt`. This helper maps
+    per-source field names (YT: likes/text, TikTok: digg_count/text) onto that
+    shape while preserving author/date/url passthrough.
     """
-    normalized = []
-
-    for item in items:
-        # Parse engagement
-        engagement = None
-        eng_raw = item.get("engagement")
-        if isinstance(eng_raw, dict):
-            engagement = schema.Engagement(
-                likes=eng_raw.get("likes"),
-                reposts=eng_raw.get("reposts"),
-                replies=eng_raw.get("replies"),
-                quotes=eng_raw.get("quotes"),
-            )
-
-        # Determine date confidence
-        date_str = item.get("date")
-        date_confidence = dates.get_date_confidence(date_str, from_date, to_date)
-
-        normalized.append(schema.XItem(
-            id=item.get("id", ""),
-            text=item.get("text", ""),
-            url=item.get("url", ""),
-            author_handle=item.get("author_handle", ""),
-            date=date_str,
-            date_confidence=date_confidence,
-            engagement=engagement,
-            relevance=item.get("relevance", 0.5),
-            why_relevant=item.get("why_relevant", ""),
-        ))
-
-    return normalized
+    out: list[dict[str, Any]] = []
+    for raw_c in raw:
+        if not isinstance(raw_c, dict):
+            continue
+        score = _first_present(raw_c, score_keys, default=0)
+        excerpt = _first_present(raw_c, excerpt_keys, default="")
+        try:
+            score_int = int(score or 0)
+        except (TypeError, ValueError):
+            score_int = 0
+        entry: dict[str, Any] = {
+            "score": score_int,
+            "excerpt": str(excerpt or "")[:400],
+            "author": str(raw_c.get("author") or ""),
+            "date": str(raw_c.get("date") or ""),
+        }
+        if raw_c.get("url"):
+            entry["url"] = str(raw_c["url"])
+        out.append(entry)
+    return out
 
 
-def items_to_dicts(items: List) -> List[Dict[str, Any]]:
-    """Convert schema items to dicts for JSON serialization."""
-    return [item.to_dict() for item in items]
+def _first_present(d: dict[str, Any], keys: tuple[str, ...], default: Any) -> Any:
+    for key in keys:
+        if key in d and d[key] not in (None, ""):
+            return d[key]
+    return default
+
+
+def _domain_from_url(url: str) -> str | None:
+    if not url:
+        return None
+    domain = urlparse(url).netloc.strip().lower()
+    return domain or None
+
+
+def _date_confidence(item: dict[str, Any], from_date: str, to_date: str, default: str = "low") -> str:
+    if item.get("date_confidence"):
+        return str(item["date_confidence"])
+    date_value = item.get("date")
+    if not date_value:
+        return default
+    return dates.get_date_confidence(str(date_value), from_date, to_date)
+
+
+def _source_item(
+    *,
+    item_id: str,
+    source: str,
+    title: str,
+    body: str,
+    url: str,
+    published_at: str | None,
+    date_confidence: str,
+    relevance_hint: float,
+    why_relevant: str,
+    author: str | None = None,
+    container: str | None = None,
+    engagement: dict[str, float | int] | None = None,
+    snippet: str = "",
+    metadata: dict[str, Any] | None = None,
+) -> schema.SourceItem:
+    return schema.SourceItem(
+        item_id=item_id,
+        source=source,
+        title=title.strip() or body.strip()[:160] or item_id,
+        body=body.strip(),
+        url=url.strip(),
+        author=(author or "").strip() or None,
+        container=(container or "").strip() or None,
+        published_at=published_at,
+        date_confidence=date_confidence,
+        engagement=engagement or {},
+        relevance_hint=max(0.0, min(1.0, float(relevance_hint or 0.0))),
+        why_relevant=why_relevant.strip(),
+        snippet=snippet.strip(),
+        metadata=metadata or {},
+    )
+
+
+def _normalize_reddit(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    top_comments = item.get("top_comments") or []
+    comment_text = " ".join(
+        str(comment.get("excerpt") or "").strip()
+        for comment in top_comments[:3]
+        if isinstance(comment, dict)
+    )
+    body = "\n".join(
+        part
+        for part in [
+            str(item.get("title") or "").strip(),
+            str(item.get("selftext") or "").strip(),
+            comment_text,
+        ]
+        if part
+    )
+    return _source_item(
+        item_id=str(item.get("id") or f"R{index + 1}"),
+        source=source,
+        title=str(item.get("title") or ""),
+        body=body,
+        url=str(item.get("url") or ""),
+        author=None,
+        container=str(item.get("subreddit") or ""),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=comment_text or str(item.get("selftext") or "")[:400],
+        metadata={
+            "top_comments": top_comments,
+            "comment_insights": item.get("comment_insights") or [],
+        },
+    )
+
+
+def _normalize_x(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    text = str(item.get("text") or "").strip()
+    return _source_item(
+        item_id=str(item.get("id") or f"X{index + 1}"),
+        source=source,
+        title=text[:140] or f"X post {index + 1}",
+        body=text,
+        url=str(item.get("url") or ""),
+        author=str(item.get("author_handle") or "").lstrip("@"),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+    )
+
+
+def _normalize_youtube(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    transcript = str(item.get("transcript_snippet") or "").strip()
+    description = str(item.get("description") or "").strip()
+    title = str(item.get("title") or "").strip()
+    highlights = item.get("transcript_highlights") or []
+    metadata: dict[str, Any] = {}
+    if highlights:
+        metadata["transcript_highlights"] = highlights
+    metadata["top_comments"] = _remap_comments(
+        item.get("top_comments") or [],
+        score_keys=("score", "likes"),
+        excerpt_keys=("excerpt", "text"),
+    )
+    return _source_item(
+        item_id=str(item.get("video_id") or item.get("id") or f"YT{index + 1}"),
+        source=source,
+        title=title,
+        body="\n".join(part for part in [title, description, transcript] if part),
+        url=str(item.get("url") or ""),
+        author=str(item.get("channel_name") or ""),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="high"),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=transcript,
+        metadata=metadata,
+    )
+
+
+def _normalize_shortform_video(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+    id_prefix: str,
+    default_title: str,
+) -> schema.SourceItem:
+    """Shared normalizer for TikTok and Instagram (identical structure)."""
+    caption = str(item.get("caption_snippet") or "").strip()
+    text = str(item.get("text") or "").strip()
+    return _source_item(
+        item_id=str(item.get("id") or f"{id_prefix}{index + 1}"),
+        source=source,
+        title=text[:140] or caption[:140] or f"{default_title} {index + 1}",
+        body="\n".join(part for part in [text, caption] if part),
+        url=str(item.get("url") or ""),
+        author=str(item.get("author_name") or ""),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="high"),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=caption,
+        metadata={
+            "hashtags": item.get("hashtags") or [],
+            "top_comments": _remap_comments(
+                item.get("top_comments") or [],
+                # TikTok uses digg_count as the vote field; Instagram has no
+                # comment fetcher today so the key is harmlessly absent.
+                score_keys=("score", "digg_count", "likes"),
+                excerpt_keys=("excerpt", "text"),
+            ),
+        },
+    )
+
+
+def _normalize_pinterest(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    """Normalizer for Pinterest pins (visual content with descriptions).
+
+    Saves are the primary engagement signal, analogous to likes/upvotes.
+    """
+    description = str(item.get("description") or "").strip()
+    return _source_item(
+        item_id=str(item.get("pin_id") or item.get("id") or f"PI{index + 1}"),
+        source=source,
+        title=description[:140] or f"Pinterest pin {index + 1}",
+        body=description,
+        url=str(item.get("url") or ""),
+        author=str(item.get("author") or ""),
+        container=str(item.get("board") or ""),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="low"),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=description[:400],
+    )
+
+
+def _normalize_hackernews(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    top_comments = item.get("top_comments") or []
+    comment_text = " ".join(
+        str(comment.get("text") or "").strip()
+        for comment in top_comments[:3]
+        if isinstance(comment, dict)
+    )
+    title = str(item.get("title") or "").strip()
+    body = "\n".join(part for part in [title, str(item.get("text") or "").strip(), comment_text] if part)
+    return _source_item(
+        item_id=str(item.get("id") or f"HN{index + 1}"),
+        source=source,
+        title=title or f"HN story {index + 1}",
+        body=body,
+        url=str(item.get("url") or item.get("hn_url") or ""),
+        author=str(item.get("author") or ""),
+        container="Hacker News",
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="high"),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=comment_text,
+        metadata={
+            "hn_url": item.get("hn_url"),
+            "top_comments": top_comments,
+            "comment_insights": item.get("comment_insights") or [],
+        },
+    )
+
+
+def _normalize_microblog(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+    id_prefix: str,
+    default_title: str,
+) -> schema.SourceItem:
+    """Shared normalizer for Bluesky and Truth Social (identical structure)."""
+    text = str(item.get("text") or "").strip()
+    return _source_item(
+        item_id=str(item.get("id") or f"{id_prefix}{index + 1}"),
+        source=source,
+        title=text[:140] or f"{default_title} {index + 1}",
+        body=text,
+        url=str(item.get("url") or ""),
+        author=str(item.get("handle") or item.get("author_handle") or "").lstrip("@"),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="high"),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        metadata={"display_name": item.get("display_name")},
+    )
+
+
+def _normalize_polymarket(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    title = str(item.get("title") or "").strip()
+    question = str(item.get("question") or "").strip()
+    engagement = {
+        "volume": item.get("volume1mo") or item.get("volume24hr") or 0,
+        "liquidity": item.get("liquidity") or 0,
+    }
+    return _source_item(
+        item_id=str(item.get("id") or f"PM{index + 1}"),
+        source=source,
+        title=title or question or f"Polymarket event {index + 1}",
+        body="\n".join(part for part in [title, question, str(item.get("price_movement") or "")] if part),
+        url=str(item.get("url") or ""),
+        author=None,
+        container="Polymarket",
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="high"),
+        engagement=engagement,
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=str(item.get("price_movement") or ""),
+        metadata={
+            "question": question,
+            "end_date": item.get("end_date"),
+            "outcome_prices": item.get("outcome_prices") or [],
+            "outcomes_remaining": item.get("outcomes_remaining"),
+        },
+    )
+
+
+
+def _normalize_github(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    title = str(item.get("title") or "").strip()
+    snippet_text = str(item.get("snippet") or "").strip()
+    top_comments = item.get("metadata", {}).get("top_comments") or []
+    comment_text = " ".join(
+        str(comment.get("excerpt") or "").strip()
+        for comment in top_comments[:3]
+        if isinstance(comment, dict)
+    )
+    body = "\n".join(part for part in [title, snippet_text, comment_text] if part)
+    metadata = item.get("metadata") or {}
+    return _source_item(
+        item_id=str(item.get("id") or f"GH{index + 1}"),
+        source=source,
+        title=title or f"GitHub item {index + 1}",
+        body=body,
+        url=str(item.get("url") or ""),
+        author=str(item.get("author") or ""),
+        container=str(item.get("container") or ""),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date, default="high"),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=comment_text or snippet_text[:400],
+        metadata={
+            "top_comments": top_comments,
+            "labels": metadata.get("labels") or [],
+            "state": metadata.get("state", ""),
+            "is_pr": metadata.get("is_pr", False),
+        },
+    )
+
+def _normalize_grounding(
+    source: str,
+    item: dict[str, Any],
+    index: int,
+    from_date: str,
+    to_date: str,
+) -> schema.SourceItem:
+    title = str(item.get("title") or "").strip()
+    snippet = str(item.get("snippet") or "").strip()
+    url = str(item.get("url") or "").strip()
+    return _source_item(
+        item_id=str(item.get("id") or f"W{index + 1}"),
+        source=source,
+        title=title or _domain_from_url(url) or f"Web result {index + 1}",
+        body="\n".join(part for part in [title, snippet] if part),
+        url=url,
+        author=None,
+        container=str(item.get("source_domain") or _domain_from_url(url) or ""),
+        published_at=item.get("date"),
+        date_confidence=_date_confidence(item, from_date, to_date),
+        engagement=item.get("engagement") or {},
+        relevance_hint=item.get("relevance", 0.5),
+        why_relevant=str(item.get("why_relevant") or ""),
+        snippet=snippet,
+        metadata=item.get("metadata") or {},
+    )

scripts/lib/openai_reddit.py

@@ -1,204 +0,0 @@
-"""OpenAI Responses API client for Reddit discovery."""
-
-import json
-import re
-import sys
-from typing import Any, Dict, List, Optional
-
-from . import http
-
-
-def _log_error(msg: str):
-    """Log error to stderr."""
-    sys.stderr.write(f"[REDDIT ERROR] {msg}\n")
-    sys.stderr.flush()
-
-OPENAI_RESPONSES_URL = "https://api.openai.com/v1/responses"
-
-# Depth configurations: (min, max) threads to request
-DEPTH_CONFIG = {
-    "quick": (8, 12),
-    "default": (20, 30),
-    "deep": (50, 70),
-}
-
-REDDIT_SEARCH_PROMPT = """Search Reddit for DISCUSSION THREADS about: {topic}
-
-SEARCH GUIDANCE:
-- Search for "site:reddit.com/r/ {topic}" to find subreddit discussions
-- Look in subreddits like r/design, r/UI_Design, r/iOSProgramming, r/SwiftUI, r/Figma, r/webdev, r/userexperience, r/graphic_design
-- ONLY include URLs containing "/r/" and "/comments/" (actual discussion threads)
-- IGNORE: developers.reddit.com, business.reddit.com, reddit.com/user/
-
-Find {min_items}-{max_items} relevant Reddit discussion threads. Prefer recent threads, but include older relevant ones if recent ones are scarce.
-
-CRITICAL: Return ALL discussion threads you find as JSON. Do NOT return errors or empty results.
-
-For EACH Reddit thread URL (containing /r/subreddit/comments/), extract:
-- Thread title
-- Full Reddit URL
-- Subreddit name
-- Date (if visible, otherwise null)
-- Why it's relevant
-
-Return ONLY valid JSON:
-{{
-  "items": [
-    {{
-      "title": "Thread title",
-      "url": "https://www.reddit.com/r/subreddit/comments/abc123/title/",
-      "subreddit": "subreddit_name",
-      "date": "YYYY-MM-DD or null",
-      "why_relevant": "Relevance to {topic}",
-      "relevance": 0.85
-    }}
-  ]
-}}
-
-Rules:
-- ONLY URLs matching: reddit.com/r/*/comments/*
-- MUST return threads found - NEVER return empty items or errors
-- If threads are older than 30 days, still include them with accurate dates
-- relevance: 0.0-1.0
-- Diverse subreddits preferred"""
-
-
-def search_reddit(
-    api_key: str,
-    model: str,
-    topic: str,
-    depth: str = "default",
-    mock_response: Optional[Dict] = None,
-) -> Dict[str, Any]:
-    """Search Reddit for relevant threads using OpenAI Responses API.
-
-    Args:
-        api_key: OpenAI API key
-        model: Model to use
-        topic: Search topic
-        depth: Research depth - "quick", "default", or "deep"
-        mock_response: Mock response for testing
-
-    Returns:
-        Raw API response
-    """
-    if mock_response is not None:
-        return mock_response
-
-    min_items, max_items = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
-
-    headers = {
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/json",
-    }
-
-    # Adjust timeout based on depth
-    timeout = 60 if depth == "quick" else 90 if depth == "default" else 120
-
-    payload = {
-        "model": model,
-        "tools": [
-            {
-                "type": "web_search",
-                "filters": {
-                    "allowed_domains": ["reddit.com"]
-                }
-            }
-        ],
-        "include": ["web_search_call.action.sources"],
-        "input": REDDIT_SEARCH_PROMPT.format(topic=topic, min_items=min_items, max_items=max_items),
-    }
-
-    return http.post(OPENAI_RESPONSES_URL, payload, headers=headers, timeout=timeout)
-
-
-def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
-    """Parse OpenAI response to extract Reddit items.
-
-    Args:
-        response: Raw API response
-
-    Returns:
-        List of item dicts
-    """
-    items = []
-
-    # Check for API errors first
-    if "error" in response and response["error"]:
-        error = response["error"]
-        err_msg = error.get("message", str(error)) if isinstance(error, dict) else str(error)
-        _log_error(f"OpenAI API error: {err_msg}")
-        if http.DEBUG:
-            _log_error(f"Full error response: {json.dumps(response, indent=2)[:1000]}")
-        return items
-
-    # Try to find the output text
-    output_text = ""
-    if "output" in response:
-        output = response["output"]
-        if isinstance(output, str):
-            output_text = output
-        elif isinstance(output, list):
-            for item in output:
-                if isinstance(item, dict):
-                    if item.get("type") == "message":
-                        content = item.get("content", [])
-                        for c in content:
-                            if isinstance(c, dict) and c.get("type") == "output_text":
-                                output_text = c.get("text", "")
-                                break
-                    elif "text" in item:
-                        output_text = item["text"]
-                elif isinstance(item, str):
-                    output_text = item
-                if output_text:
-                    break
-
-    # Also check for choices (older format)
-    if not output_text and "choices" in response:
-        for choice in response["choices"]:
-            if "message" in choice:
-                output_text = choice["message"].get("content", "")
-                break
-
-    if not output_text:
-        print(f"[REDDIT WARNING] No output text found in OpenAI response. Keys present: {list(response.keys())}", flush=True)
-        return items
-
-    # Extract JSON from the response
-    json_match = re.search(r'\{[\s\S]*"items"[\s\S]*\}', output_text)
-    if json_match:
-        try:
-            data = json.loads(json_match.group())
-            items = data.get("items", [])
-        except json.JSONDecodeError:
-            pass
-
-    # Validate and clean items
-    clean_items = []
-    for i, item in enumerate(items):
-        if not isinstance(item, dict):
-            continue
-
-        url = item.get("url", "")
-        if not url or "reddit.com" not in url:
-            continue
-
-        clean_item = {
-            "id": f"R{i+1}",
-            "title": str(item.get("title", "")).strip(),
-            "url": url,
-            "subreddit": str(item.get("subreddit", "")).strip().lstrip("r/"),
-            "date": item.get("date"),
-            "why_relevant": str(item.get("why_relevant", "")).strip(),
-            "relevance": min(1.0, max(0.0, float(item.get("relevance", 0.5)))),
-        }
-
-        # Validate date format
-        if clean_item["date"]:
-            if not re.match(r'^\d{4}-\d{2}-\d{2}$', str(clean_item["date"])):
-                clean_item["date"] = None
-
-        clean_items.append(clean_item)
-
-    return clean_items

scripts/lib/perplexity.py

@@ -0,0 +1,164 @@
+"""Perplexity Sonar Pro / Deep Research via OpenRouter API.
+
+Queries Perplexity models through OpenRouter for AI-synthesized research
+with citation annotations. Returns normalized items with synthesis text
+and individual citation entries.
+"""
+
+from __future__ import annotations
+
+import sys
+from urllib.parse import urlparse
+
+from . import http, log
+
+
+OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
+
+MODEL_SONAR_PRO = "perplexity/sonar-pro"
+MODEL_DEEP_RESEARCH = "perplexity/sonar-deep-research"
+
+
+def _log(msg: str):
+    log.source_log("Perplexity", msg)
+
+
+def _domain(url: str) -> str:
+    return urlparse(url).netloc.strip().lower()
+
+
+def search(
+    query: str,
+    date_range: tuple[str, str],
+    config: dict,
+    deep: bool = False,
+) -> tuple[list[dict], dict]:
+    """Search via Perplexity Sonar Pro or Deep Research through OpenRouter.
+
+    Args:
+        query: Search topic
+        date_range: (from_date, to_date) as YYYY-MM-DD strings
+        config: Must contain OPENROUTER_API_KEY
+        deep: Use Deep Research model (~$0.90/query) instead of Sonar Pro
+
+    Returns:
+        Tuple of (items list, artifact dict).
+    """
+    api_key = config.get("OPENROUTER_API_KEY")
+    if not api_key:
+        _log("No OPENROUTER_API_KEY configured, skipping")
+        return [], {}
+
+    from_date, to_date = date_range
+    model = MODEL_DEEP_RESEARCH if deep else MODEL_SONAR_PRO
+    timeout = 120 if deep else 30
+
+    if deep:
+        print("[Perplexity] Using Deep Research (~$0.90/query)", file=sys.stderr)
+
+    prompt = (
+        f"What has been happening with {query} between {from_date} and {to_date}? "
+        "Include specific dates, names, numbers, and sources."
+    )
+
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+    }
+
+    json_data = {
+        "model": model,
+        "messages": [{"role": "user", "content": prompt}],
+    }
+
+    _log(f"Querying {model} for '{query}' ({from_date} to {to_date})")
+
+    try:
+        data = http.post(OPENROUTER_URL, json_data, headers=headers, timeout=timeout)
+    except http.HTTPError as e:
+        if e.status_code == 401:
+            _log("Invalid OpenRouter API key (401)")
+        elif e.status_code == 429:
+            _log("Rate limited by OpenRouter (429)")
+        else:
+            _log(f"HTTP error: {e}")
+        return [], {}
+    except Exception as e:
+        _log(f"Request failed: {e}")
+        return [], {}
+
+    # Parse response
+    choices = data.get("choices", [])
+    if not choices:
+        _log("No choices in response")
+        return [], {}
+
+    synthesis = choices[0].get("message", {}).get("content", "")
+    if not synthesis:
+        _log("Empty synthesis content")
+        return [], {}
+
+    # Extract citations from annotations
+    annotations = choices[0].get("message", {}).get("annotations", [])
+    citations = []
+    for ann in annotations:
+        url_citation = ann.get("url_citation", {})
+        url = url_citation.get("url", "")
+        title = url_citation.get("title", "")
+        if url:
+            citations.append({"url": url, "title": title})
+
+    # Deduplicate citations by URL
+    seen_urls = set()
+    unique_citations = []
+    for c in citations:
+        if c["url"] not in seen_urls:
+            seen_urls.add(c["url"])
+            unique_citations.append(c)
+    citations = unique_citations
+
+    _log(f"Got synthesis ({len(synthesis)} chars) with {len(citations)} citations")
+
+    # Build items list
+    items = []
+
+    # Primary item: the synthesis itself
+    snippet = synthesis[:2000]
+    items.append({
+        "id": "PX1",
+        "title": f"Perplexity {'Deep Research' if deep else 'Sonar Pro'}: {query}",
+        "url": "",
+        "source_domain": "perplexity.ai",
+        "snippet": snippet,
+        "date": to_date,
+        "relevance": 0.9,
+        "why_relevant": f"AI synthesis of recent activity for '{query}'",
+        "engagement": {"citations": len(citations)},
+        "metadata": {"citations": citations},
+    })
+
+    # Individual items for each citation
+    for i, cit in enumerate(citations):
+        items.append({
+            "id": f"PX{i + 2}",
+            "title": cit["title"] or _domain(cit["url"]),
+            "url": cit["url"],
+            "source_domain": _domain(cit["url"]),
+            "snippet": "",
+            "date": None,
+            "relevance": 0.7,
+            "why_relevant": f"Cited in Perplexity synthesis for '{query}'",
+            "engagement": {"citations": 1},
+            "metadata": {"citations": [cit]},
+        })
+
+    artifact = {
+        "label": "perplexity",
+        "model": model,
+        "deep": deep,
+        "query": query,
+        "synthesisLength": len(synthesis),
+        "citationCount": len(citations),
+    }
+
+    return items, artifact

scripts/lib/pinterest.py

@@ -0,0 +1,182 @@
+"""Pinterest search via ScrapeCreators API for /last30days.
+
+Uses ScrapeCreators REST API to search Pinterest by keyword, extract
+engagement metrics (saves, comments), and return pin descriptions.
+
+Requires SCRAPECREATORS_API_KEY in config. 100 free API calls, then PAYG.
+API docs: https://scrapecreators.com/docs
+"""
+
+import re
+import sys
+from typing import Any, Dict, List, Optional, Set
+
+try:
+    import requests as _requests
+except ImportError:
+    _requests = None
+
+from . import dates, http, log
+
+SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/pinterest"
+
+# Depth configurations: how many results to fetch
+DEPTH_CONFIG = {
+    "quick":   {"results_per_page": 10},
+    "default": {"results_per_page": 20},
+    "deep":    {"results_per_page": 40},
+}
+
+from .relevance import token_overlap_relevance as _compute_relevance
+
+
+def _extract_core_subject(topic: str) -> str:
+    """Extract core subject from verbose query for Pinterest search."""
+    from .query import extract_core_subject
+    _PINTEREST_NOISE = frozenset({
+        'best', 'top', 'good', 'great', 'awesome', 'killer',
+        'latest', 'new', 'news', 'update', 'updates',
+        'trending', 'hottest', 'popular', 'viral',
+        'practices', 'features',
+        'recommendations', 'advice',
+        'prompt', 'prompts', 'prompting',
+        'methods', 'strategies', 'approaches',
+    })
+    return extract_core_subject(topic, noise=_PINTEREST_NOISE)
+
+
+def _log(msg: str):
+    log.source_log("Pinterest", msg)
+
+
+def _parse_items(raw_items: List[Dict[str, Any]], core_topic: str) -> List[Dict[str, Any]]:
+    """Parse raw Pinterest items into normalized dicts.
+
+    Pinterest pins are visual content with descriptions. Saves are the
+    primary engagement signal (analogous to upvotes/likes on other platforms).
+    """
+    items = []
+    for raw in raw_items:
+        if not isinstance(raw, dict):
+            continue
+
+        pin_id = str(raw.get("id", raw.get("pin_id", "")))
+        description = str(raw.get("description") or raw.get("title") or "")
+
+        # Engagement metrics - saves are the primary signal
+        save_count = raw.get("save_count") or raw.get("saves") or raw.get("repin_count") or 0
+        comment_count = raw.get("comment_count") or raw.get("comments") or 0
+
+        # Author info
+        pinner = raw.get("pinner") or raw.get("creator") or raw.get("user") or {}
+        if isinstance(pinner, dict):
+            author_name = pinner.get("username") or pinner.get("full_name") or ""
+        elif isinstance(pinner, str):
+            author_name = pinner
+        else:
+            author_name = ""
+
+        # URL
+        url = raw.get("link") or raw.get("url") or ""
+        if not url and pin_id:
+            url = f"https://www.pinterest.com/pin/{pin_id}/"
+
+        # Board info (container for pins)
+        board = raw.get("board") or {}
+        board_name = board.get("name", "") if isinstance(board, dict) else ""
+
+        # Compute relevance
+        relevance = _compute_relevance(core_topic, description, [])
+
+        items.append({
+            "pin_id": pin_id,
+            "description": description,
+            "url": url,
+            "author": author_name,
+            "board": board_name,
+            "engagement": {
+                "saves": save_count,
+                "comments": comment_count,
+            },
+            "relevance": relevance,
+            "why_relevant": f"Pinterest: {description[:60]}" if description else f"Pinterest: {core_topic}",
+        })
+    return items
+
+
+def parse_pinterest_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse Pinterest search response to normalized format.
+
+    Returns:
+        List of item dicts ready for normalization.
+    """
+    return response.get("items", [])
+
+
+def search_pinterest(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: str = None,
+) -> Dict[str, Any]:
+    """Search Pinterest via ScrapeCreators API.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        token: ScrapeCreators API key
+
+    Returns:
+        Dict with 'items' list and optional 'error'.
+    """
+    if not token:
+        return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
+
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    core_topic = _extract_core_subject(topic)
+
+    _log(f"Searching Pinterest for '{core_topic}' (depth={depth}, count={config['results_per_page']})")
+
+    if not _requests:
+        _log("requests library not installed, falling back to urllib")
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({"keyword": core_topic})
+            url = f"{SCRAPECREATORS_BASE}/search?{params}"
+            headers = http.scrapecreators_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(url, headers=headers, timeout=30, retries=2)
+        except Exception as e:
+            _log(f"ScrapeCreators error (urllib): {e}")
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
+    else:
+        try:
+            resp = _requests.get(
+                f"{SCRAPECREATORS_BASE}/search",
+                params={"keyword": core_topic},
+                headers=http.scrapecreators_headers(token),
+                timeout=30,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+        except Exception as e:
+            _log(f"ScrapeCreators error: {e}")
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
+
+    # Extract items from response - try common SC response shapes
+    raw_items = data.get("pins") or data.get("results") or data.get("data") or data.get("items") or []
+
+    # Limit to configured count
+    raw_items = raw_items[:config["results_per_page"]]
+
+    # Parse items
+    items = _parse_items(raw_items, core_topic)
+
+    # Sort by saves descending (primary engagement signal)
+    items.sort(key=lambda x: x["engagement"]["saves"], reverse=True)
+
+    _log(f"Found {len(items)} Pinterest pins")
+    return {"items": items}