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>
2026-04-22 21:48:02 -07:00
237 changed files with 3558 additions and 8810 deletions
@@ -1,20 +0,0 @@
 {
  "name": "last30days-skill",
  "interface": {
    "displayName": "Last 30 Days"
  },
  "plugins": [
    {
      "name": "last30days",
      "source": {
        "source": "local",
        "path": "./"
      },
      "policy": {
        "installation": "AVAILABLE",
        "authentication": "ON_INSTALL"
      },
      "category": "Research"
    }
  ]
 }
@@ -1,17 +1,16 @@
 {
  "$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"
  },
  "metadata": {
    "description": "Marketplace hosting the Last 30 Days research plugin."
  },
  "plugins": [
    {
      "name": "last30days",
-      "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.",
+      "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, GitHub, and 5+ more sources.",
-      "version": "3.3.0",
+      "version": "3.0.9",
      "author": {
        "name": "Matt Van Horn",
        "url": "https://github.com/mvanhorn"
@@ -1,6 +1,6 @@
 {
  "name": "last30days",
-  "version": "3.3.0",
+  "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",
@@ -10,5 +10,6 @@
  "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"]
+  "keywords": ["research", "reddit", "twitter", "youtube", "tiktok", "instagram", "trends", "prompts", "polymarket", "github", "perplexity", "threads", "pinterest", "eli5", "hacker-news"],
  "hooks": {}
 }
@@ -0,0 +1,3 @@
 {
  "name": "last30days"
 }
@@ -1,6 +1,5 @@
 # Exclude non-runtime files from `git archive` output.
-# Used by skills/last30days/scripts/build-skill.sh to produce a
+# Used by scripts/build-skill.sh to produce a claude.ai-upload-ready .skill file.
 # claude.ai-upload-ready .skill file from the canonical skills/last30days tree.
 # See docs/plans/2026-04-14-001-fix-skill-upload-200-file-limit-plan.md.
 # Anthropic canonical skill-packaging excludes
@@ -33,8 +32,9 @@ release-notes.md   export-ignore
 CHANGELOG.md       export-ignore
 uv.lock            export-ignore
-# Platform adapters are kept in git archives because Claude Code and Codex
+# Platform adapters - skill-upload path is platform-agnostic
-# plugin installs use the same repository archive as their source payload.
+.agents/         export-ignore
 .codex-plugin/   export-ignore
 .hermes-plugin/  export-ignore
 # CI workflows - repo-only, not needed at skill runtime
@@ -1,53 +0,0 @@
 name: Bug Report
 description: Report a bug or unexpected behavior
 labels: [bug]
 body:
  - type: textarea
    id: summary
    attributes:
      label: Summary
      description: What happened?
      placeholder: Describe the bug in 1-2 sentences.
    validations:
      required: true
  - type: textarea
    id: repro
    attributes:
      label: Steps to Reproduce
      description: How can we reproduce this?
      placeholder: |
        1. Run `python3 skills/last30days/scripts/last30days.py "topic" --emit=compact`
        2. ...
    validations:
      required: true
  - type: textarea
    id: expected
    attributes:
      label: Expected Behavior
      description: What should have happened?
    validations:
      required: true
  - type: textarea
    id: traceback
    attributes:
      label: Error / Traceback
      description: Paste the full traceback or error output.
      render: text
  - type: dropdown
    id: install
    attributes:
      label: Install Method
      options:
        - Claude Code plugin
        - Gemini CLI extension
        - Codex plugin
        - Hermes skill
        - Manual (git clone)
        - Other
    validations:
      required: true
  - type: input
    id: os
    attributes:
      label: OS
      placeholder: macOS 15.4, Ubuntu 24.04, Windows 11, etc.
@@ -1,24 +0,0 @@
 name: Feature Request
 description: Suggest a new feature or improvement
 labels: [enhancement]
 body:
  - type: textarea
    id: problem
    attributes:
      label: Problem
      description: What problem does this solve?
      placeholder: When I try to ..., I can't ...
    validations:
      required: true
  - type: textarea
    id: solution
    attributes:
      label: Proposed Solution
      description: How should this work?
    validations:
      required: true
  - type: textarea
    id: alternatives
    attributes:
      label: Alternatives Considered
      description: Other approaches you thought of (optional).
@@ -1,19 +0,0 @@
 ## Summary
 <!-- What does this PR do? 1-3 sentences. -->
 ## Changes
 <!-- Bullet list of what changed. Reference files if helpful. -->
 -
 ## Testing
 <!-- How did you verify this works? -->
 - [ ] Ran `uv run python -m pytest -q --tb=short`
 ## Related Issues
 <!-- Link issues: Fixes #123 or Relates to #456 -->
@@ -19,7 +19,7 @@ jobs:
      - name: Build .skill artifact
        run: |
-          bash skills/last30days/scripts/build-skill.sh
+          bash scripts/build-skill.sh
          test -f dist/last30days.skill
      - name: Create GitHub release
@@ -1,67 +0,0 @@
 name: Security
 on:
  pull_request:
  push:
    branches:
      - main
  workflow_dispatch:
 permissions:
  contents: read
 jobs:
  dependency-audit:
    name: Dependency audit
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      - name: Install uv
        uses: astral-sh/setup-uv@v5
      - name: Set up Python
        run: uv python install 3.12
      - name: Export locked dependency set
        run: |
          uv export \
            --locked \
            --all-groups \
            --no-hashes \
            --format requirements.txt \
            --output-file /tmp/last30days-requirements.txt
      # Advisory-first: visibility before enforcement. This repo handles API keys,
      # cookies, browser tokens, and local env files, so dependency CVEs should be
      # visible in CI logs even before the project has a clean blocking baseline.
      # Set continue-on-error: false once a clean baseline run is confirmed.
      - name: Run pip-audit against locked dependencies
        continue-on-error: true
        run: uvx --python 3.12 pip-audit -r /tmp/last30days-requirements.txt --progress-spinner=off
  secret-scan:
    name: Secret scan
    runs-on: ubuntu-latest
    steps:
      - name: Checkout full history for diff-aware scanning
        uses: actions/checkout@v4
        with:
          fetch-depth: 0
      # Advisory-first: this reports verified secrets in pull requests and pushes to
      # main, but does not block merges until maintainers confirm a clean baseline.
      # The TruffleHog action automatically scans the PR range for pull_request
      # events and the pushed commit range for push events.
      # Set continue-on-error: false once a clean baseline run is confirmed.
      # Contributor policy: never commit real secrets in fixtures, tests, docs, or
      # examples; use obvious dummy values and env-based auth patterns instead.
      - name: Run TruffleHog OSS secret scan
        if: github.event_name == 'pull_request' || github.event_name == 'push' || github.event_name == 'workflow_dispatch'
        uses: trufflesecurity/trufflehog@v3.95.2
        continue-on-error: true
        with:
          path: ./
          version: v3.95.2
          extra_args: --only-verified
@@ -1,26 +0,0 @@
 name: Validate
 on:
  pull_request:
  push:
    branches:
      - main
 permissions:
  contents: read
 jobs:
  tests:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      - name: Install uv
        uses: astral-sh/setup-uv@v5
      - name: Set up Python
        run: uv python install 3.12
      - name: Run test suite
        run: uv run pytest
@@ -28,7 +28,9 @@ htmlcov/
 # Internal planning docs (ce:plan output) — keep local, don't publish
 docs/plans/
 .context/
-/work
+# Marketing video build artifacts
-/print
+marketing/v3.1-launch/node_modules/
 marketing/v3.1-launch/out/
 marketing/*/node_modules/
 marketing/*/out/
@@ -1,54 +0,0 @@
 # last30days Skill
 Agent Skills package for researching any topic across Reddit, X, YouTube, and web. Installable across Claude Code (most common host), Codex, Cursor, GitHub Copilot, Gemini CLI, and 50+ other [Agent Skills](https://agentskills.io) hosts. Python scripts with multi-source search aggregation.
 ## Structure
 - `skills/last30days/SKILL.md` — canonical skill definition
 - `skills/last30days/scripts/last30days.py` — main research engine
 - `skills/last30days/scripts/lib/` — search, enrichment, rendering modules
 - `skills/last30days/scripts/lib/vendor/bird-search/` — vendored X search client
 - `docs/solutions/` — documented solutions to past problems (bugs, best practices, workflow patterns), organized by category with YAML frontmatter (`module`, `tags`, `problem_type`)
 - `CONCEPTS.md` — shared domain vocabulary (Skill, Engine, Harness, Beta channel) — relevant when orienting to the codebase or discussing project terminology
 ## Orientation
 - This is an Agent Skills package, not a CLI tool. The product is the slash-command-invoked skill (`/last30days <topic>` in most harnesses); `scripts/last30days.py` is implementation. Claude Code is the most common host but not the only one — features must work across every harness the skill installs into.
 - Feature design starts from the slash-command UX. A new engine flag with no SKILL.md integration is incomplete — the model invoking the skill won't know the flag exists.
 - README and PR examples show `/last30days <topic>` first. Direct CLI invocation (`python3 scripts/last30days.py ...`) is a fallback for scripting, cron, and dev-time engine testing; label it as such, never as the primary path.
 - Slash commands don't pass shell mechanics through. `/last30days OpenClaw --emit=html | pbcopy` is invalid in any harness — either use the slash form (no flags or pipes; let the model translate user intent into engine flags) or use the direct CLI form (full `python3 ...` with explicit flags and a real shell).
 ## Commands
 ```bash
 # Dev/fallback: direct engine invocation (scripting, cron, or engine testing only)
 python3 skills/last30days/scripts/last30days.py "test query" --emit=compact
 npx skills add . -g -y   # one-time: symlink this repo into every detected harness's skill dir
 ## Rules
 - `lib/__init__.py` must be bare package marker (comment only, NO eager imports)
 - One-time setup: `npx skills add . -g -y` creates symlinks from each detected harness's skill dir to this repo. Edits in the working tree propagate live to every harness — no re-deploy step needed.
 - Git remote: origin = public (`mvanhorn/last30days-skill`)
 ## Security hygiene
 - Never commit real API keys, browser cookies, auth tokens, app passwords, access tokens, or `.env` contents.
 - Use the env-based auth patterns in `skills/last30days/scripts/lib/env.py`; tests and fixtures must use obvious dummy values only.
 - Keep examples safe by redacting secrets and avoiding copy/pasteable live credentials in docs, fixtures, and test data.
 - Do not weaken or disable the advisory security workflow (`.github/workflows/security.yml`) without explaining why in the PR description or review thread.
 ## Maintaining CONFIGURATION.md
 `CONFIGURATION.md` is the user-facing configuration reference — save paths, per-source API keys, web-search backend priority, trend-monitoring stack, per-client install patterns. Distinct from `SKILL.md` (the canonical runtime spec).
 Update `CONFIGURATION.md` when:
 - adding a new env var (e.g. `LAST30DAYS_*`, `BSKY_*`, `*_API_KEY`)
 - adding a new CLI flag that affects configuration (e.g. `--store`, `--web-backend`)
 - adding a new per-client install pattern (Claude Code, Gemini, Codex, Cursor, Hermes…)
 - adding a new optional source that requires its own credential
 - changing the priority order of config layers (per-run flag > env > `.env` file > defaults)
 Keep the existing structure organized by how often each layer is touched: per-run flags → env vars / `.env` → optional trend-monitoring stack → per-client patterns. Add new content into the right section rather than appending at the end.
 When a new config concept lands in `SKILL.md` or `AGENTS.md`, mirror the user-facing knob in `CONFIGURATION.md` so non-agent readers can configure the skill without reverse-engineering it from the runtime spec.
 ## 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`.
@@ -5,186 +5,6 @@ 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).
 ## [Unreleased]
 ## [3.3.0] - 2026-05-17
 A week-long shipping cycle: ~75 PRs merged plus 7 community fixes salvaged through PR triage. Big themes: install story modernized for the multi-harness world (Claude Code, Codex, Cursor, Gemini CLI, Copilot, Windsurf, and 50+ Agent Skills hosts), new emit and source modes, and a substantial reliability sweep across Reddit, X, Windows, YouTube, and the planner.
 ### Added
 **Emit modes and sources**
 - `--emit=html` for shareable, print-friendly HTML research briefs ([#332](https://github.com/mvanhorn/last30days-skill/pull/332)).
 - **Digg AI 1000 source**, auto-enabled when `digg-pp-cli` is on PATH ([#370](https://github.com/mvanhorn/last30days-skill/pull/370)). Surfaces curated story clusters from the AI 1000 leaderboard and pulls attributable X-post quotes into the brief.
 **Configuration knobs**
 - `EXCLUDE_SOURCES` env var — the inverse of `INCLUDE_SOURCES`, honored in source count and pipeline filter ([#399](https://github.com/mvanhorn/last30days-skill/pull/399)).
 - `LAST30DAYS_YOUTUBE_SSH_HOST` — opt-in SSH routing for `yt-dlp` through a residential-IP host, for users on datacenter VPS hit by YouTube's bot-wall ([#376](https://github.com/mvanhorn/last30days-skill/pull/376)). Host validated against `^[a-zA-Z0-9._-]+$` to reject SSH option-injection. Transcript path unchanged (uses HTTP fallback).
 - macOS Keychain as a credential source — reads from the system keychain when env vars and config files aren't set ([#407](https://github.com/mvanhorn/last30days-skill/pull/407)).
 - Configuration enablement: env-var defaults and source-resilience patterns across the config layer ([#344](https://github.com/mvanhorn/last30days-skill/pull/344)).
 **Pipeline and storage**
 - Reddit URL auto-enrichment from web search via the public JSON API ([#366](https://github.com/mvanhorn/last30days-skill/pull/366)).
 - Per-run finding sightings recorded in the SQLite store ([#373](https://github.com/mvanhorn/last30days-skill/pull/373)).
 - Brave browser support for X/Twitter cookie extraction ([#320](https://github.com/mvanhorn/last30days-skill/pull/320)).
 **Tests and CI**
 - Full pytest suite restored to CI; 13 rotted tests repaired ([#416](https://github.com/mvanhorn/last30days-skill/pull/416)).
 - `greptile.json` added with `triggerOnUpdates` + `statusCheck` ([#418](https://github.com/mvanhorn/last30days-skill/pull/418)).
 - Advisory security workflow ([#368](https://github.com/mvanhorn/last30days-skill/pull/368)).
 - Parallel grounding backend test coverage ([#355](https://github.com/mvanhorn/last30days-skill/pull/355)).
 **Docs**
 - New `CONFIGURATION.md` with README pointers ([#339](https://github.com/mvanhorn/last30days-skill/pull/339)).
 - `docs/solutions/` learning capture for release-time consistency-test cascades ([#413](https://github.com/mvanhorn/last30days-skill/pull/413)) and the eval-not-in-CI design decision ([#417](https://github.com/mvanhorn/last30days-skill/pull/417)).
 ### Changed
 **Install story modernized**
 - `npx skills add` is now the canonical install path for every harness ([#405](https://github.com/mvanhorn/last30days-skill/pull/405)). README and SKILL.md flipped to recommend `npx skills add . -g -y` over per-harness manual instructions. Surfaces Gemini CLI, Copilot, Windsurf, and 50+ other Agent Skills hosts that the install pattern reaches.
 - README dropped the Gemini CLI native-extension install path (now covered by `npx skills add`).
 - `hooks.json` made polyglot for Gemini CLI + Claude Code compatibility ([#318](https://github.com/mvanhorn/last30days-skill/pull/318)).
 **Skill semantics and multi-harness reframe**
 - `AGENTS.md` is now canonical; `CLAUDE.md` points at it ([#410](https://github.com/mvanhorn/last30days-skill/pull/410)). Reframes the project as a multi-harness Agent Skills package rather than a Claude-Code-specific tool.
 - SKILL.md path resolution rewritten: STEP 0 narrows to a Claude-Code-marketplaces-only stale-clone guard; Step 1 walks a single `SKILL_DIR` substitution pattern ([#400](https://github.com/mvanhorn/last30days-skill/pull/400), [#409](https://github.com/mvanhorn/last30days-skill/pull/409)). Removes ~80 lines of bash and fixes a real spec-vs-engine divergence where the previous resolver could pick a different install than the SKILL.md the model loaded from.
 - SKILL.md version regex consolidated into `lib/skill_meta.py` ([#412](https://github.com/mvanhorn/last30days-skill/pull/412)).
 - `--plan` / `--competitors-plan` invocation templates switched from inline single-quoted JSON to heredoc-written tmpfiles ([#404](https://github.com/mvanhorn/last30days-skill/pull/404), fixes [#403](https://github.com/mvanhorn/last30days-skill/issues/403)). Apostrophes in resolved context strings ("McDonald's", "people's choice") no longer break shell parsing.
 - `POSTS_PER_CLUSTER` raised 3→5 and render-side display limit 2→3 to match the per-source enrichment caps used by Reddit, HN, YouTube, TikTok, and GitHub. The previous caps routinely truncated cluster context.
 - Digg AI 1000 renamed to "Digg" in user-facing output ([#372](https://github.com/mvanhorn/last30days-skill/pull/372)) — footer line, source label, inline-quote suffix, why_relevant, container attribution. Internal references retain the upstream product name.
 - GitHub repo resolution canonicalized for ambiguous product comparisons ([#302](https://github.com/mvanhorn/last30days-skill/pull/302)).
 **Dependencies and tooling**
 - Dropped `requests` runtime dependency. All providers route through stdlib `urllib` via the `lib/http` wrapper ([#393](https://github.com/mvanhorn/last30days-skill/pull/393)).
 - Migrated to `gemini-3.1-flash-lite` GA model ([#378](https://github.com/mvanhorn/last30days-skill/pull/378)).
 - Aligned Codex/Claude plugin manifests + added Codex `AGENTS.md` ([#321](https://github.com/mvanhorn/last30days-skill/pull/321)).
 - pytest dev dep bumped 9.0.2 → 9.0.3 ([#414](https://github.com/mvanhorn/last30days-skill/pull/414)).
 ### Removed
 - **BREAKING for Codex native-plugin users:** `.codex-plugin/plugin.json` and the matching SKILL_ROOT resolver branch in SKILL.md Step 1 ([#400](https://github.com/mvanhorn/last30days-skill/pull/400)). Codex users should install via `npx skills add mvanhorn/last30days-skill` or copy the skill to `~/.codex/skills/last30days/`.
 - **`skills/last30days/scripts/sync.sh`** — maintainer dev-deploy script ([#405](https://github.com/mvanhorn/last30days-skill/pull/405)). Replaced by `npx skills add . -g -y` (live-symlink into every detected harness's skill dir — better than sync.sh's copy model since edits propagate live). Hermes uses `hermes skills install mvanhorn/last30days-skill --force`; OpenClaw uses `clawhub install last30days-official`.
 - Orphaned `SPEC.md` and `TASKS.md` ([#419](https://github.com/mvanhorn/last30days-skill/pull/419)).
 ### Fixed
 **Reddit**
 - `lstrip("r/")` mangled subreddits starting with `r` (`r/robotics` → `obotics`, `r/ruby` → `uby`); replaced with `removeprefix("r/")` at 4 sites (Alex Key, salvaged from #288).
 - Browser-like User-Agent + `Accept-Language`/`Accept-Encoding`/`Connection` headers + gzip decompression to fix `urllib` 403s on Reddit's public JSON endpoint (Franco Carballar, salvaged from #199).
 - HTTP 402 re-raised across all three ScrapeCreators paths (`_global_search`, `_subreddit_search`, `fetch_post_comments`) so the OpenAI/public-JSON fallback chain triggers when credits are exhausted (Jonathan Oppenheim, salvaged from #170).
 **Authentication and credentials**
 - Restored multi-key rotation for `SCRAPECREATORS_API_KEY` accidentally dropped in v3.0.6 (Eric Oberhofer, salvaged from #287). Comma-separated keys round-robin via `random.choice` per run.
 **Windows compatibility**
 - `os.killpg` in `_cleanup_children()` guarded with `hasattr(os, "killpg")`, falls back to `os.kill(SIGTERM)` (gujishh, salvaged from #226).
 - POSIX-style secret-permission warning skipped on Windows ([#357](https://github.com/mvanhorn/last30days-skill/pull/357)).
 - Render uses forward slashes in save-path footer for Windows ([#338](https://github.com/mvanhorn/last30days-skill/pull/338)).
 **xAI / X / xurl**
 - `parse_x_response` now raises `http.HTTPError` on empty output, missing JSON, or decode failure — surfaces in `errors_by_source` instead of silently returning an empty result list (Kaustav Mishra, salvaged from #155).
 - `xurl` treats `PermissionError` from PATH lookup as unavailable ([#322](https://github.com/mvanhorn/last30days-skill/pull/322)).
 **YouTube**
 - SC YouTube + multi-token HN searches unblocked ([#388](https://github.com/mvanhorn/last30days-skill/pull/388)).
 - Transcript-fetch ratio surfaced + degraded-run nudge for stale `yt-dlp` ([#340](https://github.com/mvanhorn/last30days-skill/pull/340)).
 **bird_x / HTTP**
 - Subprocess retry on non-JSON stdout to handle X anti-bot HTML interstitials ([#383](https://github.com/mvanhorn/last30days-skill/pull/383)).
 - HTTP retry budget expanded + exponential backoff on DNS resolution failure ([#382](https://github.com/mvanhorn/last30days-skill/pull/382)).
 - Parallel AI search aligned with current API schema ([#341](https://github.com/mvanhorn/last30days-skill/pull/341)).
 - Parallel web backend routed through grounding ([#354](https://github.com/mvanhorn/last30days-skill/pull/354)).
 **Planner and sources**
 - `xquik` registered in `SOURCE_CAPABILITIES` ([#336](https://github.com/mvanhorn/last30days-skill/pull/336), fixes [#319](https://github.com/mvanhorn/last30days-skill/issues/319)).
 - Honor explicit optional source requests ([#356](https://github.com/mvanhorn/last30days-skill/pull/356)).
 - ScrapeCreators source-gating aligned between code and docs ([#415](https://github.com/mvanhorn/last30days-skill/pull/415)).
 - OpenClaw works without ScrapeCreators key ([#392](https://github.com/mvanhorn/last30days-skill/pull/392), by @thinkun).
 **Render, version display, hosting paths**
 - Hardcoded `v3.0.0` in render replaced with dynamic `_skill_version()` ([#365](https://github.com/mvanhorn/last30days-skill/pull/365)).
 - Comparison HTML artifacts saved correctly ([#389](https://github.com/mvanhorn/last30days-skill/pull/389)).
 - `OPENROUTER_DEFAULT` model ID corrected ([#323](https://github.com/mvanhorn/last30days-skill/pull/323)).
 - OpenClaw poll-timing initialized once ([#358](https://github.com/mvanhorn/last30days-skill/pull/358)).
 - Prefer sandboxed Safari cookie path ([#343](https://github.com/mvanhorn/last30days-skill/pull/343)).
 - Preserve clean mode for last-run state ([#334](https://github.com/mvanhorn/last30days-skill/pull/334)).
 - Replaced hardcoded `/Users/mvanhorn/...` paths in `test-v1-vs-v2.sh` with portable env-var overrides (Dave Morin, salvaged from #297).
 **Hooks**
 - `check-config.sh` path-quoting fix for paths with spaces ([#337](https://github.com/mvanhorn/last30days-skill/pull/337)).
 - Replaced unsafe `eval` with `declare` in `check-config.sh` ([#364](https://github.com/mvanhorn/last30days-skill/pull/364)).
 **Sync and version metadata**
 - `sync.sh` pointed at this repo's plugin cache, not the private repo's ([#402](https://github.com/mvanhorn/last30days-skill/pull/402)).
 - Sync cache target bumped to 3.2.1 to match SKILL.md ([#397](https://github.com/mvanhorn/last30days-skill/pull/397)).
 - ScrapeCreators free-tier credit count corrected to 100 in docs ([#369](https://github.com/mvanhorn/last30days-skill/pull/369), fixes [#367](https://github.com/mvanhorn/last30days-skill/issues/367)).
 - Gemini extension version synced ([#349](https://github.com/mvanhorn/last30days-skill/pull/349)).
 - Various stale path/link fixes ([#345](https://github.com/mvanhorn/last30days-skill/pull/345), [#346](https://github.com/mvanhorn/last30days-skill/pull/346), [#347](https://github.com/mvanhorn/last30days-skill/pull/347), [#348](https://github.com/mvanhorn/last30days-skill/pull/348), [#351](https://github.com/mvanhorn/last30days-skill/pull/351)).
 ### Contributors
 First-time contributors whose fixes shipped in this release (most via PR triage salvage — fix re-applied directly to main with co-author credit when path migration made the original branch un-rebaseable):
 - Dave Morin — portable test-harness paths
 - Alex Key — `removeprefix("r/")` for subreddit names
 - Eric Oberhofer — multi-key rotation restored
 - gujishh — Windows process cleanup
 - Franco Carballar — Reddit browser-like headers
 - Jonathan Oppenheim — Reddit 402 fallback chain
 - Kaustav Mishra — xAI error surfacing
 - [@thinkun](https://github.com/thinkun) ([#363](https://github.com/mvanhorn/last30days-skill/pull/363)) — OpenClaw ScrapeCreators-key-optional fix
 Full PR list at [github.com/mvanhorn/last30days-skill/releases/tag/v3.3.0](https://github.com/mvanhorn/last30days-skill/releases/tag/v3.3.0).
 ## [3.2.0] - 2026-05-09
 ### Added
 - Add `--emit=html` for shareable, print-friendly HTML research briefs.
 - **Digg AI 1000 source** (auto-enabled when `digg-pp-cli` is on PATH). Surfaces curated story clusters from the AI 1000 leaderboard and pulls attributable X-post quotes into the brief as `[@handle](xUrl) via Digg AI 1000: ...` lines. Footer line: `⛏️ Digg AI 1000: N clusters │ K posts │ M authors`. No X auth required for the inline quotes since they flow through Digg's read-only endpoints.
 ## [3.1.1] - 2026-04-24
 ### Fixed
 - **Codex plugin layout.** Move the canonical runtime payload under `skills/last30days/` and update Codex/Claude plugin metadata and tests for the relocated engine path.
 - **Claude Code cache resolution.** Resolve Claude plugin installs to `skills/last30days/scripts/last30days.py` after the plugin-layout restructure.
 ## [3.1.0] - 2026-04-22
 Consolidates the 3.0.10 to 3.0.14 dev cycle (commenter handles, `--competitors`, per-entity Step 0.55, vs-mode N passes, comparison title attribution) and republishes the OpenClaw bundle, which had been frozen on ClawHub at `3.0.0-open` since April 8.
 ### Added
 - **OpenClaw republish.** `clawhub install last30days-official` now resolves to `3.1.0-open`, matching current main. Closes [#307](https://github.com/mvanhorn/last30days-skill/issues/307), [#195](https://github.com/mvanhorn/last30days-skill/issues/195), [#236](https://github.com/mvanhorn/last30days-skill/issues/236). The ClawHub bundle had shipped a broken `env.py get_config()` and stale SKILL.md path references since April; both are fixed at source on main and the republish carries the fixes to installers.
 ### Fixed
 - **Claude Code plugin manifest path-escape.** The `.claude-plugin/plugin.json` `skills` key was removed in commit `93fbed2` but never shipped in a tagged release. Installing via `/plugin install last30days-skill` could hit `/doctor`'s `Path escapes plugin directory: ./ (skills)` error. This release ships the fix. Closes [#306](https://github.com/mvanhorn/last30days-skill/issues/306).
 - **Broken README link.** The README's "source of truth" link pointed at root `SKILL.md`, which is no longer maintained after the plugin-layout restructure. Fixed to point at `skills/last30days/SKILL.md`.
 ### Dev cycle journal (3.0.10 - 3.0.14, not separately tagged)
 Individual changelog entries for 3.0.10 through 3.0.14 below document the incremental work consolidated into this release.
 ## [3.0.14] - 2026-04-22
 ### Changed
@@ -241,7 +61,6 @@ Individual changelog entries for 3.0.10 through 3.0.14 below document the increm
 ### 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.
 - **Single plugin payload layout.** The canonical runtime moved to `skills/last30days/` for both Claude Code and Codex plugin loading. Root-level `SKILL.md`, `scripts/`, `agents/`, and `assets/` are no longer maintained as duplicate copies.
 ### Behavior fallback
@@ -1 +1,25 @@
-@AGENTS.md
+# 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`.
@@ -1,23 +0,0 @@
 # Concepts
 Shared vocabulary for `last30days-skill`. Terms here have a precise project-specific meaning — distinct enough from their general technical sense that a new contributor would need them defined to follow conversations, PR descriptions, or the SKILL.md contract.
 ## The package
 ### Skill
 A self-contained agent-instructions package consisting of a `SKILL.md` prose contract plus a sibling `scripts/` directory containing the executable code the SKILL.md invokes. The package conforms to the [Agent Skills](https://agentskills.io) open format and installs across every major harness (Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, and 50+ others) via `npx skills add`, harness-native plugin installers, or per-harness skill directories. A Skill is the unit of distribution; the Skill is the product.
 ### Engine
 The Python script (`scripts/last30days.py`) the Skill's SKILL.md invokes to do the actual research work. The Engine and SKILL.md have a contract: SKILL.md tells the model which flags to pass (`--plan`, `--competitors-plan`, `--x-handle`, `--subreddits`, `--emit=compact`, etc.), and the Engine produces a specific output shape (badge line, ranked evidence clusters, emoji-tree footer) that the model is contractually required to pass through. The Engine is implementation; the SKILL.md prose is the agent-facing surface.
 ### Harness
 The agent runtime that loads Skills and invokes them on the user's behalf. Claude Code is the most common Harness for this Skill but not the only one — Codex, Cursor, GitHub Copilot, Gemini CLI, and the rest of the Agent Skills ecosystem also count. "Multi-harness" describes a Skill that works correctly across every Harness it installs into; features written without multi-harness awareness (e.g., engine flags with no SKILL.md integration, or paths hardcoded to one Harness's install layout) regress on Harnesses other than the one they were tested against.
 ## Distribution
 ### Beta channel
 A parallel install of the Skill, sourced from the private `mvanhorn/last30days-skill-private` repo and installed as `/last30days-beta` rather than `/last30days`. The Beta channel exists so experimental changes can be tested by real users before they ship to the public `/last30days`. Promotion from Beta to public happens via a review PR against this (public) repo — Beta-only changes never ship to public without that PR. The Beta channel workflow guide lives in `BETA.md` in the private repo.
@@ -1,268 +0,0 @@
 # Configuration
 Everything you can tune in `/last30days` without editing the engine source.
 Three layers, in order of how often you'll touch them:
 1. **Per-run flags** - what you pass on the command line.
 2. **Environment variables and `.env`** - what's enabled across all runs.
 3. **Optional trend-monitoring stack** - SQLite store, watchlist, briefings.
 Per-client patterns and the experimental beta channel are at the bottom.
 > Skip ahead: [Where output is saved](#where-output-is-saved) - [API keys](#api-keys-env) - [Reasoning provider](#reasoning-provider-priority) - [Web search backend](#web-search-backend-priority) - [Trend monitoring](#trend-monitoring-store--watchlist--briefings) - [Per-client patterns](#per-client-patterns) - [Beta channel](#beta-channel)
 ## Why this document exists
 This is a focused **configuration reference** maintained alongside the engine. The runtime contract (the voice rules, the planner protocol, the LAWs the synthesizing model follows) lives in [`skills/last30days/SKILL.md`](skills/last30days/SKILL.md) - that file is authoritative when the two ever differ. This file's job is narrower: surface every knob a user or operator can turn, in one place, kept current with the code so client-facing setups stay reliable. New configuration knobs added to the engine should be reflected here in the same PR.
 ---
 ## Where output is saved
 | Platform | Default path | Override |
 |---|---|---|
 | Linux / macOS | `LAST30DAYS_MEMORY_DIR` defaults to `~/Documents/Last30Days/` | set `LAST30DAYS_MEMORY_DIR=/path` |
 | Windows | `LAST30DAYS_MEMORY_DIR` defaults to `C:\Users\<you>\Documents\Last30Days\` | set `LAST30DAYS_MEMORY_DIR=C:\path` |
 Each run produces one file per topic, slug-named:
 `<slug>-raw[-suffix].md`. Same topic + same suffix on the same day overwrites; same topic + same suffix on different days appends a date stamp.
 **Per-run overrides:**
 - `--save-dir <path>` - one-off output location.
 - `--save-suffix <name>` - distinguish runs of the same topic (e.g. per client: `--save-suffix=acme`).
 The footer line `📎 Raw results saved to ${LAST30DAYS_MEMORY_DIR:-$HOME/Documents/Last30Days}/<slug>-raw.md` is the canonical pointer; if it shows backslashes on Windows update past v3.1.1.
 ---
 ## API keys (`.env`)
 The skill reads keys from a `.env` file. Two locations are supported, in priority order:
 1. **`.claude/last30days.env`** in the current project directory (project-scoped) - takes precedence when present.
 2. **`~/.config/last30days/.env`** at the user level (global default) - the fallback.
 Override the global location with `LAST30DAYS_CONFIG_DIR=/path` (or `LAST30DAYS_CONFIG_DIR=""` for no-config mode). File permissions should be `600` on POSIX hosts - the engine warns on every run if they aren't.
 The project-scoped file is the cleanest pattern for **per-client setups**: drop a `.claude/last30days.env` into each client folder (`SCRAPECREATORS_API_KEY`, `INCLUDE_SOURCES`, `LAST30DAYS_MEMORY_DIR`, `BSKY_HANDLE`, etc), `cd` into that folder, and the skill picks up that client's configuration automatically. No wrapper scripts needed for the common case.
 **Source-by-source** - what each key unlocks:
 | Source | Key(s) | Required for | Free tier |
 |---|---|---|---|
 | Reddit (public) | none | always on | yes |
 | Hacker News | none | always on | yes |
 | Polymarket | none | always on | yes |
 | GitHub | `gh` CLI installed (uses your GitHub auth) | always on if `gh` present | yes |
 | YouTube | `yt-dlp` CLI installed | always on if `yt-dlp` present | yes |
 | X / Twitter | one of: `AUTH_TOKEN` + `CT0` (browser cookies, Bird CLI), `XAI_API_KEY`, `SCRAPECREATORS_API_KEY`, or `FROM_BROWSER` (cookie-jar auth) | X items in results | cookie-jar / Bird = free; xAI / ScrapeCreators = paid |
 | TikTok | `SCRAPECREATORS_API_KEY` + `INCLUDE_SOURCES` contains `tiktok` | TikTok items | 10K free calls |
 | Instagram | `SCRAPECREATORS_API_KEY` + `INCLUDE_SOURCES` contains `instagram` | Instagram Reels | 10K free calls; raise `LAST30DAYS_TRANSCRIPT_TIMEOUT` (default 30s) if SC is slow on your network |
 | Threads | `SCRAPECREATORS_API_KEY` + `INCLUDE_SOURCES` contains `threads` | Threads items | 10K free calls |
 | Pinterest | `SCRAPECREATORS_API_KEY` + `INCLUDE_SOURCES` contains `pinterest` | Pinterest items | 10K free calls |
 | Bluesky | `BSKY_HANDLE` + `BSKY_APP_PASSWORD` | Bluesky items | yes (app password at bsky.app) |
 | TruthSocial | `TRUTHSOCIAL_TOKEN` | TruthSocial items | yes |
 | Web search | one of: `BRAVE_API_KEY`, `EXA_API_KEY`, `SERPER_API_KEY`, `PARALLEL_API_KEY` | `--auto-resolve` and Step 2 supplements | Brave has a free tier; native WebSearch on Claude Code / Codex / Gemini works as a fallback |
 | Perplexity Deep Research | `OPENROUTER_API_KEY` | `--deep-research` flag (~$0.90/query) | no |
 | Apify (alternate scraper) | `APIFY_API_TOKEN` | fallback for Reddit/TikTok/Instagram when ScrapeCreators is exhausted | yes (limited) |
 **Example `.env` skeleton** (placeholders only - replace with your own values):
 ```bash
 # Reasoning + planning (one provider; see priority below)
 GOOGLE_API_KEY=<your-gemini-key>
 # Web search backend (one is enough; Brave is the cheapest)
 BRAVE_API_KEY=<your-brave-key>
 # Optional sources
 SCRAPECREATORS_API_KEY=<your-scrapecreators-key>
 INCLUDE_SOURCES=tiktok,instagram
 # X authentication (one option only)
 XAI_API_KEY=<your-xai-key>
 # OR cookie-jar (no key needed; logs in via your browser session)
 # FROM_BROWSER=firefox
 # Bluesky
 BSKY_HANDLE=<your-handle>.bsky.social
 BSKY_APP_PASSWORD=<your-app-password>
 ```
 After editing: `chmod 600 ~/.config/last30days/.env` (or `chmod 600 .claude/last30days.env` if using the project-scoped variant).
 **Troubleshooting:** if a source you expected to see isn't appearing in results, run `python3 scripts/last30days.py --diagnose`. It prints a per-source availability report (which keys were detected, which CLIs are installed, which backends are reachable) without running a full search.
 ### Bluesky app-password format and search host
 `BSKY_APP_PASSWORD` should be a 19-char app password in `xxxx-xxxx-xxxx-xxxx` format (lowercase alphanumeric, three hyphens). Generate one at <https://bsky.app/settings/app-passwords>. The AT Protocol's `createSession` endpoint also accepts your main account login password, but that's bad hygiene — main passwords have no scope (an app password can be limited to non-DM access) and can't be revoked individually.
 The skill defaults to `api.bsky.app` for `searchPosts`, which is the canonical authenticated AppView. The previous default `public.api.bsky.app` is the unauthenticated public mirror and is currently blocked by BunnyCDN for `searchPosts` regardless of auth header (verified 2026-05-04). If Bluesky migrates infrastructure again, override the host without a code change by setting `BSKY_SEARCH_HOST` in your `.env`:
 ```bash
 BSKY_SEARCH_HOST=api.bsky.app   # default — change only if Bluesky moves
 ```
 ---
 ## Reasoning provider priority
 `/last30days` needs one reasoning model for planning + reranking when you don't pass `--plan` yourself. Auto-detect priority (set `LAST30DAYS_REASONING_PROVIDER=<name>` to pin one):
 1. **Gemini** - `GOOGLE_API_KEY` / `GEMINI_API_KEY` / `GOOGLE_GENAI_API_KEY`
 2. **OpenAI** - `OPENAI_API_KEY` (or Codex auth at `~/.codex/auth.json`)
 3. **xAI** - `XAI_API_KEY`
 4. **OpenRouter** - `OPENROUTER_API_KEY` (also unlocks `--deep-research`)
 5. **Local / deterministic** - always available, lowest quality
 When you invoke `/last30days` from Claude Code, Codex, or Gemini, the host model **is** the reasoning provider for plan + synthesis - you don't need any of the keys above unless you also run the script headlessly (cron, CI, watchlist).
 ---
 ## Web search backend priority
 Used by `--auto-resolve` (when WebSearch isn't available from the host) and Step 2 supplements. Auto-detect priority (override per-run with `--web-backend=<name>`):
 1. **Brave** - `BRAVE_API_KEY`
 2. **Exa** - `EXA_API_KEY`
 3. **Serper** - `SERPER_API_KEY`
 4. **Parallel** - `PARALLEL_API_KEY`
 5. **Host's native WebSearch** - Claude Code, Codex, Gemini all have one built in
 Visible quality difference between hosts with vs without a configured backend. If your client setup produces thinner results than yours, this is usually why.
 ---
 ## Trend monitoring (`--store` + watchlist + briefings)
 The default behavior - one slug-named file per topic, overwritten on rerun - is the snapshot mode. For continuous monitoring, the repo ships three components most users miss:
 ### `--store` flag
 Adding `--store` to any run persists every finding to a SQLite database (default at `~/.local/share/last30days/research.db`). Findings dedupe on the `source_url` column (UNIQUE constraint), so the same URL across runs updates the existing row instead of creating a duplicate. The markdown file still saves; the SQLite is the time-series substrate.
 **Always-on alternative:** set `LAST30DAYS_STORE=1` in your `.env` instead of remembering `--store` on every invocation. The flag still works as before; the env var is purely additive. Same hybrid pattern as `LAST30DAYS_DEBUG` — works whether shell-exported or in `.env`.
 Relevant tables: `topics`, `research_runs`, `findings`, `settings`. Schema: [`scripts/store.py`](skills/last30days/scripts/store.py).
 ### `watchlist.py` - recurring topics
 [`scripts/watchlist.py`](skills/last30days/scripts/watchlist.py) manages topics that should be researched on a schedule. Subcommands: `add`, `remove`, `list`, `run-one`, `run-all`, `config`. Built-in delivery to Slack incoming webhooks (`hooks.slack.com/...`) or any HTTPS endpoint, fired only when new findings appear.
 Two-step flow (the watchlist holds the topic; an external scheduler invokes the run):
 ```bash
 # 1. Add the topic to the watchlist
 #    Default schedule daily 8am; --weekly switches to Mondays 8am
 python3 scripts/watchlist.py add "british airways middle east" --weekly
 # 2. Configure delivery and budget (optional)
 python3 scripts/watchlist.py config delivery "https://hooks.slack.com/services/..."
 python3 scripts/watchlist.py config budget 5.00
 # 3. Trigger via cron / Task Scheduler / GitHub Actions
 python3 scripts/watchlist.py run-one "british airways middle east"
 # or run every enabled topic, gated by daily_budget
 python3 scripts/watchlist.py run-all
 ```
 The schedule field stored on each topic is metadata - the actual cron / Task Scheduler invocation is your responsibility. Watchlist runs hardcode `--quick` and `--lookback-days 90` when spawning the underlying engine.
 ### `briefing.py` - daily / weekly digests
 [`scripts/briefing.py`](skills/last30days/scripts/briefing.py) reads the SQLite store and emits structured data the agent then synthesizes into prose. Modes: `generate` (daily), `generate --weekly`, `show [--date DATE]` (display a saved briefing). Briefs save to `~/.local/share/last30days/briefs/`.
 ### Recommended cadence pattern
 | Step | Cadence | Command |
 |---|---|---|
 | Baseline | one-time per topic | `/last30days "<topic>" --days=30 --store` |
 | Add to watchlist | one-time per topic | `python3 scripts/watchlist.py add "<topic>" --weekly` |
 | Recurring run | daily or weekly (external scheduler) | `python3 scripts/watchlist.py run-all` |
 | Digest | weekly | `python3 scripts/briefing.py generate --weekly` |
 ---
 ## Per-client patterns
 The skill is built to flex around different client environments. Four patterns that compose well:
 ### 1. Per-client `.claude/last30days.env` (preferred when you cd into client folders)
 The simplest pattern when each client has its own working directory: drop a `.claude/last30days.env` into the client folder. The skill picks it up automatically (see [API keys](#api-keys-env) for the lookup priority). Typical contents:
 ```bash
 LAST30DAYS_MEMORY_DIR=C:\Users\<you>\Clients\acme\Research\Last30Days
 SCRAPECREATORS_API_KEY=<acme-scoped-key-or-shared>
 INCLUDE_SOURCES=tiktok,instagram
 BSKY_HANDLE=<acme-bluesky-handle>.bsky.social
 ```
 `cd` into the client folder, run `/last30days <topic>` as normal, no flags or wrappers. Combine with `--save-suffix=<client-slug>` per run if you also need to differentiate filenames within that folder.
 ### 2. Per-client save dir + suffix wrapper
 For workflows where you don't `cd` into a client folder (running from anywhere, scripted batches), a tiny shell function isolates each client's research without engine changes.
 PowerShell example:
 ```powershell
 function Run-L30D-Client {
    param([string]$ClientSlug, [Parameter(ValueFromRemainingArguments=$true)]$Args)
    $env:LAST30DAYS_MEMORY_DIR = "C:\Users\$env:USERNAME\Clients\$ClientSlug\Research\Last30Days"
    /last30days @Args --save-suffix=$ClientSlug
 }
 # Usage: Run-L30D-Client acme "british airways middle east"
 ```
 Bash example:
 ```bash
 l30d-client() {
    local client=$1; shift
    LAST30DAYS_MEMORY_DIR="$HOME/Clients/$client/Research/Last30Days" \
        /last30days "$@" --save-suffix="$client"
 }
 # Usage: l30d-client acme "british airways middle east"
 ```
 ### 3. Custom category-peer subreddits
 [`scripts/lib/categories.py`](skills/last30days/scripts/lib/categories.py) holds a table of `(category_id, trigger_keywords, peer_subreddits)`. If a client lives in a vertical that isn't covered (legal-tech, real-estate-tech, B2B HR SaaS), add a row. Pure data, no logic.
 Section 2a of `SKILL.md` documents the merging rule the skill applies when your topic matches a category.
 ### 4. Pre-built `--competitors-plan` JSON
 For competitor-vs-comparisons that recur, a pre-written JSON skeleton per client industry saves real time:
 ```json
 {
  "Competitor B": {
    "x_handle": "competitor_b_handle",
    "subreddits": ["sub1", "sub2"],
    "github_user": "competitor-b-org",
    "context": "Founded 2019, focused on ..."
  },
  "Competitor C": { ... }
 }
 ```
 Pass as `--competitors-plan @client/competitors-plan.json` (or as a string). See `SKILL.md` section "If QUERY_TYPE = COMPARISON" for the full schema.
 ---
 ## Beta channel
 Experimental customizations live on a private companion repo (`mvanhorn/last30days-skill-private`) installed as `/last30days-beta`. Never ship beta-only changes to the public marketplace without a review PR against the public repo. Workflow guide: `BETA.md` in the private repo.
 This is the right home for client-specific changes you don't intend to upstream - custom category rows, internal subreddit lists, per-vertical plan templates.
 ---
 ## Cross-references
 - The CLI flag surface: `python3 scripts/last30days.py --help`
 - The skill contract (voice, LAWs, pre-flight protocol): [`skills/last30days/SKILL.md`](skills/last30days/SKILL.md)
 - Engine spec (some sections stale; SKILL.md wins on conflicts): [`SPEC.md`](SPEC.md)
 - Contributor guidance: [`CONTRIBUTORS.md`](CONTRIBUTORS.md)
@@ -23,7 +23,7 @@ v3 has full GitHub search: issues, PRs, person-mode profiles, project-mode repos
 ### @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.
-> Thinker, technologist, AI expert, music-tinkerer. Founder of [Thinkun](https://thinkun.com). [@thinkun on GitHub](https://github.com/thinkun) · [@unthink on X](https://x.com/unthink)
+> _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
@@ -10,20 +10,28 @@ This guide covers installing last30days on Hermes AI Agent.
 ## Installation
 ### Option 1: Via sync.sh (Recommended)
 ```bash
-hermes skills install mvanhorn/last30days-skill --force
+# Clone the repo
 git clone https://github.com/mvanhorn/last30days-skill.git
 cd last30days-skill
 # Run the sync script
 bash scripts/sync.sh
 ```
-This pulls the latest release from GitHub and deploys to `~/.hermes/skills/research/last30days/`. `--force` reinstalls over any existing copy.
+This will auto-detect Hermes and deploy to `~/.hermes/skills/research/last30days/`
-### Developer / live-edit alternative
+### Option 2: Manual Copy
 If you're hacking on the skill locally and want edits to propagate to Hermes without re-installing, symlink your working tree:
 ```bash
-git clone https://github.com/mvanhorn/last30days-skill.git
+# Create directory
-mkdir -p ~/.hermes/skills/research
+mkdir -p ~/.hermes/skills/research/last30days
-ln -s "$(pwd)/last30days-skill/skills/last30days" ~/.hermes/skills/research/last30days
+
 # Copy files
 cp -r scripts ~/.hermes/skills/research/last30days/
 cp .hermes-plugin/SKILL.md ~/.hermes/skills/research/last30days/
 ```
 ## Usage
@@ -51,7 +59,7 @@ On first run, the skill will guide you through setup:
 2. **Optional: ScrapeCreators**
   - Adds TikTok, Instagram, Reddit backup
-   - 100 free credits (no expiration)
+   - 10,000 free API calls
   - Sign up at scrapecreators.com
 3. **Optional: API Keys**
@@ -98,11 +106,13 @@ python3.12 scripts/last30days.py --diagnose
 ## Updating
-```bash
+To update to the latest version:
 hermes skills install mvanhorn/last30days-skill --force
 ```
-If you symlinked your working tree (developer alternative above), just `git pull` in the repo — edits propagate live, no re-install step.
+```bash
 cd last30days-skill
 git pull
 bash scripts/sync.sh
 ```
 ## Support
@@ -14,19 +14,21 @@
 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 (recommended — auto-updates via marketplace):**
+Claude Code:
 ```
 /plugin marketplace add mvanhorn/last30days-skill
 /plugin install last30days
 ```
-**Codex, Cursor, Copilot, Gemini CLI, or any of 50+ [Agent Skills](https://agentskills.io) hosts:**
+OpenClaw:
 ```
-npx skills add mvanhorn/last30days-skill -g
+clawhub install last30days-official
 ```
 (`-g` installs globally for your user, available across all projects. Drop it to scope per-project.)
-More install options (claude.ai web, OpenClaw, manual) in the [Install](#install) section below.
+Hermes:
 ```
 # The skill auto-deploys when you run sync.sh
 # Or manually copy to ~/.hermes/skills/research/last30days/
 ```
 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.
@@ -66,7 +68,6 @@ If you're meeting with a CEO, have you read all their tweets and YouTube transcr
 | **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. |
 | **Digg** | Curated story clusters from Digg's AI 1000 leaderboard (~1000 high-signal AI accounts on X), with attributable inline quotes (no X auth required). Auto-enabled when `digg-pp-cli` is on PATH. |
 | **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. |
@@ -95,28 +96,6 @@ The synthesis ranks by what real people actually engaged with. Social relevancy,
 ## What v3 Changed
 ### Shareable HTML briefs
 Ask for an HTML brief and the skill saves a self-contained, dark-mode, print-friendly file you can drop into Slack, email, or Notion. No raw markdown leaks. Inline CSS, system-font fallbacks behind Inter and JetBrains Mono. No JavaScript. Works offline.
 ```
 /last30days OpenClaw --emit=html
 ```
 or just ask in plain language:
 ```
 /last30days OpenClaw, give me a shareable HTML brief
 /last30days Cursor IDE for slack
 /last30days Anthropic earnings export as html
 ```
 The skill emits the synthesis in chat as usual AND saves a brief to `${LAST30DAYS_MEMORY_DIR}/{topic}-brief.html` (defaults to `~/Documents/Last30Days/`). The chat response ends with the file path so you can `open` it or drag it into a message.
 What's in the file: badge, inline metadata line, the model's synthesis verbatim with all citations, the engine footer (✅ All agents reported back! tree), and a colophon noting the topic + how to re-run. Data quality warnings (degraded run, thin evidence, etc.) stay in the engine's stderr logs; they never leak into the shareable artifact.
 For direct CLI use without the model in the loop, the engine also accepts `--synthesis-file PATH` to convert any markdown synthesis to HTML.
 ### 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.
@@ -153,10 +132,8 @@ Say "eli5 on" after any research run. The synthesis rewrites in plain language.
 - **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.
- **TikTok, Instagram, Threads.** All three activate automatically once `SCRAPECREATORS_API_KEY` is set — same key, same per-call cost. Suppress any of them with `EXCLUDE_SOURCES=tiktok,instagram,threads` (any comma-separated subset).
+- **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.
- **Pinterest.** Per-query opt-in (visual pins, narrow utility): the model passes `--search=pinterest` for the runs that need it. Requires `SCRAPECREATORS_API_KEY`.
+- **Perplexity Sonar.** Grounded web search with citations via OpenRouter. Add `OPENROUTER_API_KEY` to unlock.
 - **YouTube + TikTok comments.** Persistent opt-in via `INCLUDE_SOURCES=youtube_comments,tiktok_comments` because each video pulls N extra ScrapeCreators calls on top of the base search. Surface top comments with vote counts the same way Reddit does.
 - **Perplexity Sonar.** Grounded web search with citations via OpenRouter. Add `OPENROUTER_API_KEY` and `INCLUDE_SOURCES=perplexity` (it's a separate paid API — opt-in keeps you from being surprise-billed).
 - **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.
@@ -168,61 +145,12 @@ Say "eli5 on" after any research run. The synthesis rewrites in plain language.
 ## Install
-| Surface | Install | Updates |
+| Surface | Install |
-|---------|---------|---------|
+|---------|---------|
-| **Claude Code** (recommended) | `/plugin marketplace add mvanhorn/last30days-skill` | Auto via marketplace, or `claude plugin update last30days@last30days-skill` |
+| **claude.ai** (web) | [Download `last30days.skill`](https://github.com/mvanhorn/last30days-skill/releases/latest/download/last30days.skill) and upload via Settings > Capabilities > Skills > + |
-| **Codex, Cursor, Copilot, Gemini CLI, GitHub Copilot, or any of 50+ [Agent Skills](https://agentskills.io) hosts** | `npx skills add mvanhorn/last30days-skill -g` | `npx skills update last30days -g` |
+| **Claude Code** | `/plugin marketplace add mvanhorn/last30days-skill` |
-| **claude.ai** (web) | [Download `last30days.skill`](https://github.com/mvanhorn/last30days-skill/releases/latest/download/last30days.skill) and upload via Settings > Capabilities > Skills > + | Re-download and re-upload |
+| **OpenClaw** | `clawhub install last30days-official` |
-| **OpenClaw** | `clawhub install last30days-official` | `clawhub update last30days-official` |
+| **Gemini CLI** | Clone then `gemini extensions install ./last30days-skill` (see below) |
 ### Claude Code (recommended)
 ```
 /plugin marketplace add mvanhorn/last30days-skill
 ```
 Recommended because the Claude Code marketplace handles updates for you — the plugin cache is versioned and auto-refreshes when a new release publishes. Run `claude plugin update last30days@last30days-skill` to force a check.
 If you'd rather use the agent-skills install path on Claude Code, that's also supported:
 ```
 npx skills add mvanhorn/last30days-skill -g -a claude-code
 ```
 The native plugin and the `npx skills` install can coexist; Claude Code dedupes the slash command.
 ### Codex, Cursor, Copilot, Gemini CLI, and other Agent Skills hosts
 Install via the open [Agent Skills](https://agentskills.io) CLI — supports 50+ harnesses including `codex`, `cursor`, `github-copilot`, `gemini-cli`, `claude-code`, `windsurf`, `cline`, `continue`, `roo`, `aider-desk`, `opencode`, `goose`, and more (full list on the [vercel-labs/skills repo](https://github.com/vercel-labs/skills)).
 ```bash
 npx skills add mvanhorn/last30days-skill -g
 ```
 The `-g` (global) flag installs to your user directory so the skill is available across all projects. Without `-g`, `npx skills` installs project-locally into `./.skills/` (committed with the repo). For a research-the-world tool, global is what you want.
 By default this installs for whichever harness `npx skills` detects. To target a specific one (or multiple):
 ```bash
 npx skills add mvanhorn/last30days-skill -g -a codex
 npx skills add mvanhorn/last30days-skill -g -a cursor
 npx skills add mvanhorn/last30days-skill -g -a gemini-cli
 npx skills add mvanhorn/last30days-skill -g -a codex -a cursor
 ```
 Update later with:
 ```bash
 npx skills update last30days -g
 ```
 Or update everything you've installed globally via `npx skills`:
 ```bash
 npx skills update -g
 ```
 List and remove with `npx skills list -g` and `npx skills remove last30days -g`.
 ### claude.ai (web)
@@ -230,7 +158,15 @@ List and remove with `npx skills list -g` and `npx skills remove last30days -g`.
 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.
+Enable "Code execution and file creation" under Capabilities first - skills won't run without it.
 ### Claude Code
 ```
 /plugin marketplace add mvanhorn/last30days-skill
 ```
 Update later with `claude plugin update last30days@last30days-skill`.
 ### OpenClaw
@@ -238,14 +174,22 @@ Enable "Code execution and file creation" under Capabilities first — skills wo
 clawhub install last30days-official
 ```
 ### Gemini CLI
 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:
 ```bash
 git clone https://github.com/mvanhorn/last30days-skill
 gemini extensions install ./last30days-skill
 ```
 ### Manual (developer)
 ```bash
-git clone https://github.com/mvanhorn/last30days-skill.git
+git clone https://github.com/mvanhorn/last30days-skill.git ~/.claude/skills/last30days
 ln -s "$(pwd)/last30days-skill/skills/last30days" ~/.claude/skills/last30days
 ```
-The symlink keeps the install in sync with your working tree as you edit — no re-copy needed. For `claude.ai`, build the `.skill` file from source: `bash skills/last30days/scripts/build-skill.sh` produces `dist/last30days.skill`.
+Or build the claude.ai `.skill` file from source: `bash scripts/build-skill.sh` produces `dist/last30days.skill`.
 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.
@@ -259,40 +203,10 @@ These platforms don't have relationships with each other. X doesn't know what Re
 | 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 | 100 free credits, then PAYG |
+| 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 |
 ### macOS Keychain (optional)
 On macOS you can store keys in the system Keychain instead of a `.env` file. The skill picks them up automatically as the lowest-priority source — `.env` files and process environment still win on collision.
 ```bash
 # Interactive setup — prompts for each known key, skip with empty input
 skills/last30days/scripts/setup-keychain.sh
 # Or store a single key by hand
 security add-generic-password -a "$USER" -s last30days-XAI_API_KEY -w "xai-..."
 # Inspect / clean up
 skills/last30days/scripts/setup-keychain.sh --list
 skills/last30days/scripts/setup-keychain.sh --delete XAI_API_KEY
 ```
 Items are stored under service name `last30days-<KEY>` for the current user. On non-Darwin platforms the loader is a no-op, so there is no behaviour change for Linux/Windows users.
 See [CONFIGURATION.md](CONFIGURATION.md) for the full per-source key matrix, reasoning provider priority, and web-search backend priority.
 ## Configuration
 Two things you'll likely want to know on day one:
 **Where research files are saved.** `LAST30DAYS_MEMORY_DIR` defaults to `~/Documents/Last30Days/` (Windows: `C:\Users\<you>\Documents\Last30Days\`). Override by setting that env var to any path in your shell, or `--save-dir <path>` per run. Use `--save-suffix=<name>` to keep multiple variations of the same topic separate (e.g. per client). Each run produces `<slug>-raw[-suffix].md`.
 **Trend monitoring across runs.** The default mode produces a fresh markdown snapshot per run. To accumulate findings over time, add `--store` to persist into a SQLite database, then use [`scripts/watchlist.py`](skills/last30days/scripts/watchlist.py) for scheduled runs (with optional Slack / webhook delivery on new findings) and [`scripts/briefing.py`](skills/last30days/scripts/briefing.py) for daily / weekly digests. The full cadence pattern is in [CONFIGURATION.md](CONFIGURATION.md#trend-monitoring-store--watchlist--briefings).
 Per-client wrapper scripts, custom category-peer subreddits, and the experimental beta channel for in-progress customizations are also documented in [CONFIGURATION.md](CONFIGURATION.md).
 ## How it works
 1. **You type a topic.** Person, company, product, technology, "X vs Y." Anything.
@@ -1,6 +1,6 @@
 ---
 name: last30days
-version: "3.3.0"
+version: "3.0.1"
 description: "Research what people actually say about any topic in the last 30 days. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, and the web."
 argument-hint: 'last30days nvidia earnings reaction | last30days AI video tools | last30days what users want in react'
 allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
@@ -13,9 +13,9 @@ metadata:
  openclaw:
    emoji: "📰"
    requires:
-      env: []
+      env:
      optionalEnv:
        - SCRAPECREATORS_API_KEY
      optionalEnv:
        - OPENAI_API_KEY
        - XAI_API_KEY
        - OPENROUTER_API_KEY
@@ -45,7 +45,6 @@ metadata:
      - instagram
      - hackernews
      - polymarket
      - digg
      - bluesky
      - truthsocial
      - trends
@@ -60,32 +59,23 @@ metadata:
      - clawhub
 ---
-# STEP 0: STALE-CLONE SELF-CHECK — RUN BEFORE READING BELOW
+# STEP 0: CANONICAL PATH SELF-CHECK — RUN BEFORE READING BELOW
-Before reading anything else in this file, check whether you loaded SKILL.md from the one known stale-clone location: Claude Code's marketplaces directory.
+Before reading anything else in this file, verify that the SKILL.md you just loaded is the versioned cache copy, not a stale marketplace clone:
 **Run this check:**
 ```bash
-CLAUDE_CACHE_LATEST=$(find "$HOME/.claude/plugins/cache/last30days-skill/last30days" -mindepth 1 -maxdepth 1 -type d 2>/dev/null | sort -V | tail -1)
+CANONICAL_ROOT="$HOME/.claude/plugins/cache/last30days-skill/last30days"
-# Two cache layouts ship in the wild — nested ({version}/skills/last30days/SKILL.md)
+CANONICAL_LATEST=$(ls -d "$CANONICAL_ROOT"/*/ 2>/dev/null | sort -V | tail -1)
-# and flat ({version}/SKILL.md). Resolve to whichever shape actually exists.
+CANONICAL_LATEST="${CANONICAL_LATEST%/}"
 CLAUDE_CACHE_SKILL_MD=""
 if [ -n "$CLAUDE_CACHE_LATEST" ]; then
  if [ -f "$CLAUDE_CACHE_LATEST/skills/last30days/SKILL.md" ]; then
    CLAUDE_CACHE_SKILL_MD="$CLAUDE_CACHE_LATEST/skills/last30days/SKILL.md"
  elif [ -f "$CLAUDE_CACHE_LATEST/SKILL.md" ]; then
    CLAUDE_CACHE_SKILL_MD="$CLAUDE_CACHE_LATEST/SKILL.md"
  fi
 fi
 echo "CLAUDE_CACHE_SKILL_MD=$CLAUDE_CACHE_SKILL_MD"
 ```
-If the SKILL.md path you just Read contains `/.claude/plugins/marketplaces/` AND `$CLAUDE_CACHE_SKILL_MD` is non-empty, STOP and re-read `$CLAUDE_CACHE_SKILL_MD` before proceeding. Otherwise the SKILL.md you have is fine — continue.
+If the SKILL.md file you just read is NOT under `$CANONICAL_LATEST/`, STOP and re-read `$CANONICAL_LATEST/SKILL.md` before proceeding. Do not run `--help`, do not plan, do not invoke the engine against any other path.
-**Why this specific check:** `~/.claude/plugins/marketplaces/last30days-skill/` is a git clone Claude Code auto-restores to `origin/main` on session start. It can lag the versioned cache by one or more releases. Three 2026-04-22 test runs (Linear, Coinbase) loaded SKILL.md from `marketplaces/`, ran `--help` from the same stale path, did not see the `--competitors` flag that existed in the cache, and fell back to a manual comparison plan. Result: 2 of 3 windows never invoked the feature they were asked to test. STEP 0 defends against that one Claude Code-specific bug.
+**Why:** `~/.claude/plugins/marketplaces/last30days-skill/` is a git clone Claude Code auto-restores to `origin/main` on session start. It can lag the versioned cache by one or more releases. Three 2026-04-22 test runs (Linear, Coinbase) loaded SKILL.md from `marketplaces/`, ran `--help` from the same stale path, did not see the `--competitors` flag that existed in the cache, and fell back to a manual comparison plan. Result: 2 of 3 windows never invoked the feature they were asked to test.
-**Other install paths are fine:** `~/.codex/skills/`, `~/.agents/skills/`, an `npx skills add` install dir, or a repo checkout are all valid load points - the resolver in Step 1 picks them up. Do NOT abort or hop on those paths.
+**How to self-check:** the file path you used in your last Read tool call should match `$CANONICAL_LATEST/SKILL.md`. If it contains `marketplaces/` or any other prefix, that is the stale-path failure mode. Re-read from `$CANONICAL_LATEST/SKILL.md` and restart this contract from the top.
 The same pinned resolver appears later in Step 1 for the engine Bash invocation. That guard is necessary but insufficient — by the time you reach Step 1, you may have already internalized an out-of-date flag list from the stale SKILL.md above it. This STEP 0 runs first so the CONTRACT itself is read from the right file.
 ---
@@ -97,7 +87,7 @@ You are inside the `/last30days` SKILL. This is a specific research tool with a
 **How v3.0.7 fixes it:** three structural anchors.
 1. **The MANDATORY first-line badge** (`🌐 last30days v{VERSION} · synced {YYYY-MM-DD}`) at the top of every response is the LAW 2 / LAW 4 enforcement anchor. See "BADGE (MANDATORY, FIRST LINE OF OUTPUT)" in the synthesis section.
-2. **The SKILL_DIR substitution** in the engine Bash calls uses the directory of the SKILL.md the model just Read — no resolver list, no precedence walk. Whichever install the harness loaded SKILL.md from is the install whose engine runs. Aligns spec-with-code and works for any harness without enumerating its install path.
+2. **The pinned SKILL_ROOT resolution** in the engine Bash calls always points to the public plugin cache, never `~/.openclaw/` or other stale copies.
 3. **This preface** tells you plainly: do NOT improvise. Follow SKILL.md top to bottom.
 If you catch yourself about to write a `##` section header in a GENERAL-query body, a custom title line, a `Sources:` bullet list, a `for dir in ...` path-discovery loop, or a bare `python3 scripts/last30days.py "{TOPIC}"` engine call with no pre-flight flags — stop. Those are the exact failure modes the LAWs and this contract exist to prevent. The 10/10 beta validation from 2026-04-18 and the 0/8 public v3.0.6 regression from the same day had THE SAME MODEL and SIMILAR SKILL.md CONTENT; the delta is the three anchors this release restores. Read SKILL.md top to bottom before emitting your first response.
@@ -114,7 +104,7 @@ These anchors used to live at line 1094 of this file. Three independent Opus 4.7
 🌐 last30days v{VERSION} · synced {YYYY-MM-DD}
 ```
-Replace `{VERSION}` with the installed plugin version (`jq -r '.version' "$SKILL_DIR/../../.claude-plugin/plugin.json" 2>/dev/null || awk '/^version:/{gsub(/"/,"",$2); print $2; exit}' "$SKILL_DIR/SKILL.md"`) and `{YYYY-MM-DD}` with today's date. No other text on this line. One blank line after, then the synthesis begins.
+Replace `{VERSION}` with the installed plugin version (`jq -r '.version' "$SKILL_ROOT/.claude-plugin/plugin.json"`) and `{YYYY-MM-DD}` with today's date. No other text on this line. One blank line after, then the synthesis begins.
 **Why the badge is MANDATORY:** it is the structural anchor for the canonical output shape. Without it the model drifts into blog-post narrative format with `##` section headers and invented titles, violating LAW 2 and LAW 4. The 2026-04-18 public v3.0.6 0/8 regression produced outputs with section headers like "The headline", "Why he is everywhere", "1. gstack dominates", "The 'Homecoming' peak". Direct cause: this anchor was absent. Do NOT skip the badge. Do NOT describe it. Do NOT paraphrase it. Emit it verbatim as line 1.
@@ -186,13 +176,13 @@ The self-evolving loop is the sticky use case. Every 15 tool calls Hermes pauses
 Cron-scheduled autonomous briefings are the most-cited concrete workflow. r/TunisiaTech's "Use cases of OpenClaw, Hermes Agent" thread says it plainly: "Currently I have daily cron jobs for news briefing, but I know there's much more I can do."
 ```
-**LAW 7 - YOU ARE THE PLANNER. `--plan` IS MANDATORY ON NAMED-ENTITY TOPICS.** If you are the reasoning model hosting this skill (Claude Code, Codex, Hermes, Gemini, or any agent runtime that invoked `/last30days`), YOU generate the JSON query plan. You do not need an API key, "LLM provider" credentials, or an external planning service - you ARE the LLM. The `--plan` flag exists precisely so a reasoning model generates its own plan upstream and passes it to the engine. The engine's internal planner and deterministic fallback are headless/cron paths only; on any reasoning-model path, bypass them by passing `--plan "$QUERY_PLAN_FILE"` (the path to a tmpfile you wrote via heredoc — see Step 1 for the pattern; never inline `--plan '$JSON'`, apostrophes in search/ranking strings break shell parsing).
+**LAW 7 - YOU ARE THE PLANNER. `--plan` IS MANDATORY ON NAMED-ENTITY TOPICS.** If you are the reasoning model hosting this skill (Claude Code, Codex, Hermes, Gemini, or any agent runtime that invoked `/last30days`), YOU generate the JSON query plan. You do not need an API key, "LLM provider" credentials, or an external planning service - you ARE the LLM. The `--plan` flag exists precisely so a reasoning model generates its own plan upstream and passes it to the engine. The engine's internal planner and deterministic fallback are headless/cron paths only; on any reasoning-model path, bypass them by passing `--plan '$JSON'`.
-Named-entity topics (capitalized proper nouns, product names, person names, project names, or any topic that would benefit from handle resolution in Step 0.55) REQUIRE `--plan`. Your invocation of `scripts/last30days.py` MUST contain `--plan "$QUERY_PLAN_FILE"` (or any path the engine can read). A bare `python3 scripts/last30days.py "$TOPIC" --emit=compact` on a named-entity topic is a LAW 7 violation. Before you invoke Bash, self-check: does my command contain `--plan`? If no, STOP and generate a plan first (see Step 0.75 for the schema).
+Named-entity topics (capitalized proper nouns, product names, person names, project names, or any topic that would benefit from handle resolution in Step 0.55) REQUIRE `--plan`. Your invocation of `scripts/last30days.py` MUST contain `--plan '$JSON'`. A bare `python3 scripts/last30days.py "$TOPIC" --emit=compact` on a named-entity topic is a LAW 7 violation. Before you invoke Bash, self-check: does my command contain `--plan`? If no, STOP and generate a plan first (see Step 0.75 for the schema).
 **Observed LAW 7 violation (2026-04-19, Hermes Agent Use Cases Run 1):** the model called the engine bare with no `--plan`, no pre-flight handle resolution. The engine emitted a stderr warning ("No --plan and no LLM provider configured. Using deterministic fallback...") which the model read as a capability constraint ("I don't have a key, I can't do LLM stuff") instead of as what it actually was: a reminder that the reasoning model skipped its own planning step. The misread came from the word "provider" - the engine uses "provider" to mean "the key for the engine's INTERNAL planner," but the model parsed it as "I need a provider to plan at all." You do not. You ARE the provider. Run 2 of the same topic (2026-04-19, framed as "best workflows") with the same model and same cache generated the plan itself via `--plan` and produced clean results - the delta was this step.
-**Self-check before Bash:** re-read your pending `scripts/last30days.py` command. Does it contain `--plan "$QUERY_PLAN_FILE"` (or another path the engine can read)? If no, and the topic is a named entity, STOP. Return to Step 0.75 and generate the plan, then write it to a tmpfile per the Step 1 pattern. Do not interpret the word "provider" in any engine message as "you need credentials" - you are the provider.
+**Self-check before Bash:** re-read your pending `scripts/last30days.py` command. Does it contain `--plan '$JSON'`? If no, and the topic is a named entity, STOP. Return to Step 0.75 and generate the plan. Do not interpret the word "provider" in any engine message as "you need credentials" - you are the provider.
 **LAW 8 - EVERY CITATION IN THE NARRATIVE IS AN INLINE MARKDOWN LINK `[name](url)`. NEVER A RAW URL STRING. NEVER A PLAIN NAME WHEN A URL IS AVAILABLE.** Applies to every query type. In the "What I learned:" narrative, in KEY PATTERNS, and in the COMPARISON body sections, every cited @handle, r/subreddit, publication, YouTube channel, TikTok creator, Instagram creator, and Polymarket market is wrapped as `[name](url)` at first mention. The URL comes from the raw research dump — every engine item carries a URL; WebSearch supplements carry URLs in their own output. Claude Code renders `[text](url)` as blue CMD-clickable text; the URL is hidden in the rendering, only the link text shows. The stats footer (emoji-tree block) is engine-emitted per LAW 5 and passes through verbatim — do NOT reformat its links yourself.
@@ -243,7 +233,7 @@ If your Bash call to `last30days.py` does NOT include the FULL pre-flight checkl
 ---
-# last30days v3.3.0: Research Any Topic from the Last 30 Days
+# last30days v3.0.1: Research Any Topic from the Last 30 Days
 > **Permissions overview:** Reads public web/platform data and optionally saves research briefings to `LAST30DAYS_MEMORY_DIR` (defaults to `~/Documents/Last30Days`). X/Twitter search uses optional user-provided tokens (AUTH_TOKEN/CT0 env vars). Bluesky search uses optional app password (BSKY_HANDLE/BSKY_APP_PASSWORD env vars - create at bsky.app/settings/app-passwords). All credential usage and data writes are documented in the [Security & Permissions](#security--permissions) section.
@@ -327,14 +317,14 @@ Common patterns:
 - Always active: Reddit, Hacker News, Polymarket
 - If gh CLI is installed (check `which gh`): add GitHub
 - If digg-pp-cli is installed (check `which digg-pp-cli`): add Digg
 - If AUTH_TOKEN/CT0 or XAI_API_KEY or FROM_BROWSER is set, or xurl CLI is installed and authenticated: add X
 - If yt-dlp is installed (check `which yt-dlp`): add YouTube
- If SCRAPECREATORS_API_KEY is set: add TikTok, Instagram, Threads (suppress any of these via EXCLUDE_SOURCES)
+- If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains tiktok: add TikTok
- If SCRAPECREATORS_API_KEY is set and the user explicitly requested pinterest for this query (e.g. via `--search=pinterest`): add Pinterest
+- If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains instagram: add Instagram
 - If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains threads: add Threads
 - If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains pinterest: add Pinterest
 - If BSKY_HANDLE and BSKY_APP_PASSWORD are set: add Bluesky
- If OPENROUTER_API_KEY is set and INCLUDE_SOURCES contains perplexity: add Perplexity
+- If OPENROUTER_API_KEY is set: add Perplexity
 - If EXCLUDE_SOURCES is set (comma-separated, case-insensitive): drop any matching source from the list above before displaying
 Then display (use "and more" if 5+ sources, otherwise list all with Oxford comma):
@@ -591,50 +581,18 @@ When the user asks "X vs Y" (or "X vs Y vs Z"), the engine fans out N full `pipe
 **Invocation:**
 ```bash
-# SKILL_DIR = absolute path of the directory containing THIS SKILL.md you just Read.
+"${LAST30DAYS_PYTHON}" "${SKILL_ROOT}/scripts/last30days.py" "{TOPIC_A} vs {TOPIC_B} vs {TOPIC_C}" \
 # Substitute the actual path below — your harness told you where this file lives via
 # the Read tool result. Examples:
 #   Read ~/.claude/skills/last30days/SKILL.md      → SKILL_DIR=$HOME/.claude/skills/last30days
 #   Read ~/.codex/skills/last30days/SKILL.md       → SKILL_DIR=$HOME/.codex/skills/last30days
 #   Read ~/.claude/plugins/cache/last30days-skill/last30days/3.3.0/skills/last30days/SKILL.md
 #     → SKILL_DIR=$HOME/.claude/plugins/cache/last30days-skill/last30days/3.3.0/skills/last30days
 # scripts/last30days.py is always a direct child of SKILL_DIR (every install layout
 # packages SKILL.md and scripts/ as siblings).
 SKILL_DIR="<absolute path of the directory containing the SKILL.md you Read>"
 if [ ! -f "$SKILL_DIR/scripts/last30days.py" ]; then
  echo "ERROR: scripts/last30days.py not found under SKILL_DIR=$SKILL_DIR" >&2
  echo "Re-check the directory of the SKILL.md you Read and substitute it as SKILL_DIR above." >&2
  exit 1
 fi
 # Write the per-entity plan to a tmpfile and pass the path to the engine.
 # The engine's parse_competitors_plan() reads file paths transparently. This
 # avoids the inline-single-quoted-JSON apostrophe trap (resolved context
 # strings like "people's choice" or "McDonald's" otherwise close the outer
 # single-quote and break shell parsing before the engine is even invoked).
 # Trailing XXXXXX (no .json suffix) so BSD/macOS mktemp works the same as
 # GNU; BSD only substitutes X's at the end of the template.
 COMPETITORS_PLAN_FILE=$(mktemp "${TMPDIR:-/tmp}/last30days-competitors.XXXXXX")
 trap 'rm -f "$COMPETITORS_PLAN_FILE"' EXIT
 cat > "$COMPETITORS_PLAN_FILE" <<'PLAN_EOF'
 {
  "{TOPIC_B}": {"x_handle":"{TOPIC_B_HANDLE}","subreddits":["{TOPIC_B_SUB_1}","{TOPIC_B_SUB_2}"],"github_user":"{TOPIC_B_GH}","context":"{TOPIC_B_CONTEXT}"},
  "{TOPIC_C}": {"x_handle":"{TOPIC_C_HANDLE}","subreddits":["{TOPIC_C_SUB_1}"],"github_user":"{TOPIC_C_GH}","context":"{TOPIC_C_CONTEXT}"}
 }
 PLAN_EOF
 "${LAST30DAYS_PYTHON}" "${SKILL_DIR}/scripts/last30days.py" "{TOPIC_A} vs {TOPIC_B} vs {TOPIC_C}" \
  --emit=compact \
  --save-dir="${LAST30DAYS_MEMORY_DIR}" \
  --save-suffix=v3 \
  --x-handle={TOPIC_A_HANDLE} \
  --subreddits={TOPIC_A_SUBS} \
-  --competitors-plan "$COMPETITORS_PLAN_FILE"
+  --competitors-plan '{
    "{TOPIC_B}": {"x_handle":"{TOPIC_B_HANDLE}","subreddits":["{TOPIC_B_SUB_1}","{TOPIC_B_SUB_2}"],"github_user":"{TOPIC_B_GH}","context":"{TOPIC_B_CONTEXT}"},
    "{TOPIC_C}": {"x_handle":"{TOPIC_C_HANDLE}","subreddits":["{TOPIC_C_SUB_1}"],"github_user":"{TOPIC_C_GH}","context":"{TOPIC_C_CONTEXT}"}
  }'
 ```
 **The quoted heredoc marker `'PLAN_EOF'` is load-bearing** — quoting suppresses shell interpolation so apostrophes, `$`, backticks, etc. pass through verbatim. If you ever switch to an unquoted `<<PLAN_EOF`, every variable reference and apostrophe inside the JSON becomes a parse hazard.
 Topic A (the main topic, first in the vs-string) uses outer `--x-handle`, `--x-related`, `--subreddits`, `--github-user`, `--github-repo`, `--tiktok-*`, `--ig-creators` as usual. Topics B and C get their targeting from `--competitors-plan` entries (keyed by entity name, case-insensitive).
 **Step 0.55 for N entities.** The same pre-research protocol that applies to a single-entity topic applies to EACH entity in a vs-run. For N=3, that means 3 WebSearches for X handles, 3 for subreddits, 3 for GitHub, 3 for news context — or equivalent batched queries. A `## Resolved Entities` block with dashes for any entity means you skipped Step 0.55 for that one. Re-run with a corrected plan.
@@ -866,7 +824,7 @@ Only show lines for platforms where something was resolved. Skip empty lines. On
 - For how_to: prioritize YouTube (tutorials) and Reddit (guides)
 - Primary subquery weight = 1.0, secondary = 0.6-0.8, peripheral = 0.3-0.5
-**Available sources (include ALL in primary subquery):** reddit, x, youtube, tiktok, instagram, hackernews, polymarket. Optional: bluesky, truthsocial, threads, pinterest, grounding (web search - only if user has Brave/Exa/Serper key), digg (Digg clusters - only if `digg-pp-cli` is on PATH)
+**Available sources (include ALL in primary subquery):** reddit, x, youtube, tiktok, instagram, hackernews, polymarket. Optional: bluesky, truthsocial, threads, pinterest, grounding (web search - only if user has Brave/Exa/Serper key)
 **Intent → freshness_mode mapping:**
 - breaking_news, prediction → `strict_recent`
@@ -909,45 +867,34 @@ Store your plan as `QUERY_PLAN_JSON` - you'll pass it to the script in the next
 **IMPORTANT: Include `--x-handle={RESOLVED_HANDLE}` in the command. For comparison mode: Pass `--x-handle={TOPIC_A_HANDLE}` to the first pass, `--x-handle={TOPIC_B_HANDLE}` to the second pass, and both to the head-to-head pass. Also include `--subreddits={RESOLVED_SUBREDDITS}`, `--tiktok-hashtags={RESOLVED_HASHTAGS}`, `--tiktok-creators={RESOLVED_TIKTOK_CREATORS}`, and `--ig-creators={RESOLVED_IG_CREATORS}` from Step 0.55. Omit any flag where the value was not resolved (empty).**
 ```bash
-# SKILL_DIR = absolute path of the directory containing THIS SKILL.md you just Read.
+# PIN SKILL_ROOT to the public plugin cache (highest-version dir wins on upgrade).
-# Substitute the actual path below — your harness told you where this file lives via
+# DO NOT write your own path-discovery loop. The 2026-04-18 Peter Steinberger run 1
-# the Read tool result. Examples:
+# regression was caused by a custom discovery loop landing on ~/.openclaw/skills/last30days/
-#   Read ~/.claude/skills/last30days/SKILL.md      → SKILL_DIR=$HOME/.claude/skills/last30days
+# (a stale copy from a private-repo sync pattern). That path contains a pre-plan-007
-#   Read ~/.codex/skills/last30days/SKILL.md       → SKILL_DIR=$HOME/.codex/skills/last30days
+# engine and produces non-canonical output. This pinned resolution ignores every stale
-#   Read ~/.claude/plugins/cache/last30days-skill/last30days/3.3.0/skills/last30days/SKILL.md
+# copy (~/.openclaw/, ~/.agents/, ~/.codex/) and picks the plugin cache exclusively.
-#     → SKILL_DIR=$HOME/.claude/plugins/cache/last30days-skill/last30days/3.3.0/skills/last30days
+SKILL_ROOT="$(ls -d "$HOME/.claude/plugins/cache/last30days-skill/last30days/"*/ 2>/dev/null | sort -V | tail -1)"
-# scripts/last30days.py is always a direct child of SKILL_DIR (every install layout
+SKILL_ROOT="${SKILL_ROOT%/}"
 # packages SKILL.md and scripts/ as siblings).
 SKILL_DIR="<absolute path of the directory containing the SKILL.md you Read>"
-if [ ! -f "$SKILL_DIR/scripts/last30days.py" ]; then
+# Fallback for repo checkout / Gemini / Codex hosts where the plugin cache does not exist.
-  echo "ERROR: scripts/last30days.py not found under SKILL_DIR=$SKILL_DIR" >&2
+# Only runs if the public plugin cache is missing entirely.
-  echo "Re-check the directory of the SKILL.md you Read and substitute it as SKILL_DIR above." >&2
+if [ -z "$SKILL_ROOT" ] || [ ! -f "$SKILL_ROOT/scripts/last30days.py" ]; then
  for dir in "." "${CLAUDE_PLUGIN_ROOT:-}" "${GEMINI_EXTENSION_DIR:-}"; do
    [ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
  done
 fi
 if [ -z "${SKILL_ROOT:-}" ] || [ ! -f "$SKILL_ROOT/scripts/last30days.py" ]; then
  echo "ERROR: Could not find scripts/last30days.py in public plugin cache or repo checkout" >&2
  echo "Expected: $HOME/.claude/plugins/cache/last30days-skill/last30days/{VERSION}/scripts/last30days.py" >&2
  exit 1
 fi
-"${LAST30DAYS_PYTHON}" "${SKILL_DIR}/scripts/last30days.py" $ARGUMENTS --emit=compact --save-dir="${LAST30DAYS_MEMORY_DIR}" --save-suffix=v3
+"${LAST30DAYS_PYTHON}" "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --emit=compact --save-dir="${LAST30DAYS_MEMORY_DIR}" --save-suffix=v3
 ```
-**If you ran Steps 0.55 and 0.75 (agent planning), pass the plan via a tmpfile and add the targeting flags:**
+**If you ran Steps 0.55 and 0.75 (agent planning), add these flags:**
-
+- `--plan 'QUERY_PLAN_JSON'` (replace with actual JSON from Step 0.75)
 ```bash
 # Write QUERY_PLAN_JSON to a tmpfile before the engine invocation above.
 # parse_plan() reads file paths transparently; this avoids inline-JSON
 # shell-quoting hazards (apostrophes in search_query / ranking_query
 # strings break single-quoted command-line JSON). Trailing XXXXXX (no
 # .json suffix) for BSD/macOS portability — BSD mktemp only substitutes
 # X's at the end of the template.
 QUERY_PLAN_FILE=$(mktemp "${TMPDIR:-/tmp}/last30days-plan.XXXXXX")
 trap 'rm -f "$QUERY_PLAN_FILE"' EXIT
 cat > "$QUERY_PLAN_FILE" <<'PLAN_EOF'
 {QUERY_PLAN_JSON_FROM_STEP_0.75}
 PLAN_EOF
 ```
 Then add to the engine command:
 - `--plan "$QUERY_PLAN_FILE"` (path to the file you just wrote)
 - `--x-handle={RESOLVED_HANDLE}` (from Step 0.5)
 - `--subreddits={RESOLVED_SUBREDDITS}` (from Step 0.55)
 - `--tiktok-hashtags={RESOLVED_HASHTAGS}` (from Step 0.55)
@@ -1550,33 +1497,6 @@ Close with `I have all the links to the {N} {source list} I pulled from. Just as
 ---
 ## SHAREABLE HTML BRIEF (when the user asked for one)
 **This section fires if EITHER trigger is true:**
 - `$ARGUMENTS` contains `--emit=html`, `--emit:html`, or `--html` as a flag
 - The user's natural-language request asks for an HTML brief, shareable doc, or file for sharing (Slack, email, Notion, "export as HTML", etc). Use your judgment for phrasing variants.
 **If neither trigger fires, skip this entire section and proceed to WAIT FOR USER'S RESPONSE.** No HTML save flow, no reference read needed.
 **When triggered, you MUST:**
 - Read `references/save-html-brief.md` BEFORE proceeding to WAIT FOR USER'S RESPONSE
 - Follow that file's instructions exactly - it is the canonical source for the save flow
 - Append the confirmation line (`📎 Shareable brief saved to <path>`) to your already-emitted chat response
 **You MUST NOT:**
 - Improvise the HTML save flow from memory or from instructions you've seen before
 - Skip the reference read because the steps "look familiar"
 - Save to a different path than the reference specifies
 - Add data quality warnings, debug headers, or safety notes to the saved HTML
 - Re-research the topic for the HTML render - the engine cache covers the second invocation
 **Why the directive is forceful:** the reference file is the only source of truth for the save flow. Skipping it produces broken artifacts - wrong path conventions, missing synthesis content, leaked engine debug output, or warnings that don't belong in shareable docs.
 ---
 ## WAIT FOR USER'S RESPONSE
 **STOP and wait** for the user to respond. Do NOT call any tools after displaying the invitation. Do NOT append a `Sources:` section (see override above - WebSearch's mandate does not apply here). The research script already saved raw data to `LAST30DAYS_MEMORY_DIR` (defaults to `~/Documents/Last30Days`) via `--save-dir`.
@@ -1688,7 +1608,7 @@ Want another prompt? Just tell me what you're creating next.
 - Sends search queries to Algolia HN Search API (`hn.algolia.com`) for Hacker News story and comment discovery (free, no auth)
 - Sends search queries to Polymarket Gamma API (`gamma-api.polymarket.com`) for prediction market discovery (free, no auth)
 - Runs `yt-dlp` locally for YouTube search and transcript extraction (no API key, public data)
- Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for TikTok and Instagram search, transcript/caption extraction (PAYG after 100 free credits)
+- Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for TikTok and Instagram search, transcript/caption extraction (PAYG after 10,000 free API calls)
 - Optionally sends search queries to Brave Search API, Parallel AI API, or OpenRouter API for web search
 - Fetches public Reddit thread data from `reddit.com` for engagement metrics
 - Stores research findings in local SQLite database (watchlist mode only)
@@ -1701,7 +1621,7 @@ Want another prompt? Just tell me what you're creating next.
 - Does not log, cache, or write API keys to output files
 - Does not send data to any endpoint not listed above
 - Hacker News and Polymarket sources are always available (no API key, no binary dependency)
- TikTok and Instagram sources require SCRAPECREATORS_API_KEY (100 free credits one-time, then PAYG). Reddit uses ScrapeCreators only as a backup when public Reddit is unavailable.
+- TikTok and Instagram sources require SCRAPECREATORS_API_KEY (10,000 free API calls, then PAYG). Reddit uses ScrapeCreators only as a backup when public Reddit is unavailable.
 - Can be invoked autonomously by agents via the Skill tool (runs inline, not forked); pass `--agent` for non-interactive report output
 **Bundled scripts:** `scripts/last30days.py` (main research engine), `scripts/lib/` (search, enrichment, rendering modules), `scripts/lib/vendor/bird-search/` (vendored X search client, MIT licensed)
@@ -0,0 +1,77 @@
 # last30days Skill Specification
 ## 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. 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.
 ## Architecture
 The orchestrator (`last30days.py`) coordinates discovery, enrichment, normalization, scoring, deduplication, and rendering. Each concern is isolated in `scripts/lib/`:
 - **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
 - **models.py**: Auto-selection of OpenAI/xAI models with 7-day caching
 - **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
 - **render.py**: Generate markdown and JSON outputs
 - **schema.py**: Type definitions and validation
 ## Embedding in Other Skills
 Other skills can import the research context in several ways:
 ### Inline Context Injection
 ```markdown
 ## Recent Research Context
 !python3 ~/.claude/skills/last30days/scripts/last30days.py "your topic" --emit=context
 ```
 ### Read from File
 ```markdown
 ## Research Context
 !cat ~/.local/share/last30days/out/last30days.context.md
 ```
 ### Get Path for Dynamic Loading
 ```bash
 CONTEXT_PATH=$(python3 ~/.claude/skills/last30days/scripts/last30days.py "topic" --emit=path)
 cat "$CONTEXT_PATH"
 ```
 ### JSON for Programmatic Use
 ```bash
 python3 ~/.claude/skills/last30days/scripts/last30days.py "topic" --emit=json > research.json
 ```
 ## CLI Reference
 ```
 python3 ~/.claude/skills/last30days/scripts/last30days.py <topic> [options]
 Options:
  --refresh           Bypass cache and fetch fresh data
  --mock              Use fixtures instead of real API calls
  --emit=MODE         Output mode: compact|json|md|context|path (default: compact)
  --sources=MODE      Source selection: auto|reddit|x|both (default: auto)
 ```
 ## Output Files
 All outputs are written to `~/.local/share/last30days/out/`:
 - `report.md` - Human-readable full report
 - `report.json` - Normalized data with scores
 - `last30days.context.md` - Compact reusable snippet for other skills
 - `raw_openai.json` - Raw OpenAI API response
 - `raw_xai.json` - Raw xAI API response
 - `raw_reddit_threads_enriched.json` - Enriched Reddit thread data
@@ -0,0 +1,47 @@
 # last30days Implementation Tasks
 ## Setup & Configuration
 - [x] Create directory structure
 - [x] Write SPEC.md
 - [x] Write TASKS.md
 - [x] Write SKILL.md with proper frontmatter
 ## Core Library Modules
 - [x] scripts/lib/env.py - Environment and API key loading
 - [x] scripts/lib/dates.py - Date range and confidence utilities
 - [x] scripts/lib/cache.py - TTL-based caching
 - [x] scripts/lib/http.py - HTTP client with retry
 - [x] scripts/lib/models.py - Auto model selection
 - [x] scripts/lib/schema.py - Data structures
 - [x] scripts/lib/openai_reddit.py - OpenAI Responses API
 - [x] scripts/lib/xai_x.py - xAI Responses API
 - [x] scripts/lib/reddit_enrich.py - Reddit thread JSON fetcher
 - [x] scripts/lib/normalize.py - Schema normalization
 - [x] scripts/lib/score.py - Popularity scoring
 - [x] scripts/lib/dedupe.py - Near-duplicate detection
 - [x] scripts/lib/render.py - Output rendering
 ## Main Script
 - [x] scripts/last30days.py - CLI orchestrator
 ## Fixtures
 - [x] fixtures/openai_sample.json
 - [x] fixtures/xai_sample.json
 - [x] fixtures/reddit_thread_sample.json
 - [x] fixtures/models_openai_sample.json
 - [x] fixtures/models_xai_sample.json
 ## Tests
 - [x] tests/test_dates.py
 - [x] tests/test_cache.py
 - [x] tests/test_models.py
 - [x] tests/test_score.py
 - [x] tests/test_dedupe.py
 - [x] tests/test_normalize.py
 - [x] tests/test_render.py
 ## Validation
 - [x] Run tests in mock mode
 - [x] Demo --emit=compact
 - [x] Demo --emit=context
 - [x] Verify file tree
@@ -142,7 +142,7 @@ The repo vendors a search-only subset of Bird's Twitter GraphQL client and shell
 | 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 relevance.py) | AI-assessed 0.0-1.0 |
+| Relevance score | Default 0.7 (re-ranked by score.py) | AI-assessed 0.0-1.0 |
 ### Depth settings
@@ -183,14 +183,13 @@ After both searches complete:
 | File | Purpose |
 |---|---|
-| `skills/last30days/scripts/last30days.py` | Main CLI entry point |
+| `scripts/last30days.py` | Main orchestrator, concurrent execution |
-| `skills/last30days/scripts/lib/pipeline.py` | Multi-source retrieval orchestration |
+| `scripts/lib/openai_reddit.py` | Reddit search via OpenAI Responses API |
-| `skills/last30days/scripts/lib/reddit_public.py` | Reddit public JSON search |
+| `scripts/lib/reddit_enrich.py` | Fetch real engagement data from Reddit JSON API |
-| `skills/last30days/scripts/lib/reddit_enrich.py` | Fetch real engagement data from Reddit JSON API |
+| `scripts/lib/xai_x.py` | X search via xAI API |
-| `skills/last30days/scripts/lib/xai_x.py` | X search via xAI API |
+| `scripts/lib/bird_x.py` | X search via bundled Bird client (free) |
-| `skills/last30days/scripts/lib/bird_x.py` | X search via bundled Bird client (free) |
+| `scripts/lib/models.py` | Auto-select best available model |
-| `skills/last30days/scripts/lib/providers.py` | Reasoning provider and model selection |
+| `scripts/lib/env.py` | API key loading, source detection |
-| `skills/last30days/scripts/lib/env.py` | API key loading, source detection |
+| `scripts/lib/http.py` | HTTP transport with retries |
-| `skills/last30days/scripts/lib/http.py` | HTTP transport with retries |
+| `scripts/lib/score.py` | Relevance scoring |
-| `skills/last30days/scripts/lib/relevance.py` | Query matching and relevance scoring |
+| `scripts/lib/dedupe.py` | URL-based deduplication |
 | `skills/last30days/scripts/lib/dedupe.py` | URL-based deduplication |
@@ -1,7 +1,4 @@
 ---
 > **NOTE (added 2026-05-16):** This plan references `bash scripts/sync.sh`. That script was deleted in [PR #405](https://github.com/mvanhorn/last30days-skill/pull/405); the install workflow is now `npx skills add . -g -y` (symlinks the working tree across every detected harness). For context on why sync.sh went away, see [docs/solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md](../solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md). The decisions captured in this plan remain accurate; only the deploy mechanism changed.
 title: "feat: --competitors flag for auto-discovered comparison fan-out"
 type: feat
 status: active
@@ -1,7 +1,4 @@
 ---
 > **NOTE (added 2026-05-16):** This plan references `bash scripts/sync.sh`. That script was deleted in [PR #405](https://github.com/mvanhorn/last30days-skill/pull/405); the install workflow is now `npx skills add . -g -y` (symlinks the working tree across every detected harness). For context on why sync.sh went away, see [docs/solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md](../solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md). The decisions captured in this plan remain accurate; only the deploy mechanism changed.
 title: "fix: per-entity resolution, default-2, and stale-path guard for --competitors"
 type: fix
 status: active
@@ -1,7 +1,4 @@
 ---
 > **NOTE (added 2026-05-16):** This plan references `bash scripts/sync.sh`. That script was deleted in [PR #405](https://github.com/mvanhorn/last30days-skill/pull/405); the install workflow is now `npx skills add . -g -y` (symlinks the working tree across every detected harness). For context on why sync.sh went away, see [docs/solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md](../solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md). The decisions captured in this plan remain accurate; only the deploy mechanism changed.
 title: "feat: vs mode runs N full passes and --competitors is vs with auto-discovery"
 type: feat
 status: active
@@ -1,7 +1,4 @@
 ---
 > **NOTE (added 2026-05-16):** This plan references `bash scripts/sync.sh`. That script was deleted in [PR #405](https://github.com/mvanhorn/last30days-skill/pull/405); the install workflow is now `npx skills add . -g -y` (symlinks the working tree across every detected harness). For context on why sync.sh went away, see [docs/solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md](../solutions/workflow-issues/release-consistency-test-cascade-2026-05-16.md). The decisions captured in this plan remain accurate; only the deploy mechanism changed.
 title: "fix: comparison title says (/Last30Days) instead of (Last 30 Days)"
 type: fix
 status: active
@@ -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.
@@ -1,6 +1,6 @@
 # Search Quality Eval
-`skills/last30days/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.
+`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:
@@ -18,13 +18,13 @@ What it does:
 Recommended usage:
 ```bash
-uv run python skills/last30days/scripts/evaluate_search_quality.py
+uv run python scripts/evaluate_search_quality.py
 ```
 Useful flags:
 ```bash
-uv run python skills/last30days/scripts/evaluate_search_quality.py \
+uv run python scripts/evaluate_search_quality.py \
  --baseline-rev origin/main \
  --candidate-rev HEAD \
  --no-default-topics \
@@ -1,82 +0,0 @@
 ---
 title: Search-quality eval is manual by default, not a CI gate on every PR
 date: 2026-05-10
 category: docs/solutions/architecture
 module: skills/last30days/scripts/evaluate_search_quality.py
 problem_type: design_decision
 component: ci_policy
 severity: low
 applies_when:
  - a contributor proposes wiring search-quality eval into PR CI
  - a change affects retrieval, ranking, grounding, or synthesis quality and a reviewer asks "why aren't we testing this in CI?"
  - someone is deciding whether a new evaluator-style script belongs in the default CI workflow
 related_components:
  - search_quality_evaluation
  - ci_workflow
  - llm_judging
 tags:
  - ci-policy
  - eval
  - design-decision
  - cost-vs-signal
  - non-determinism
  - manual-gates
 ---
 # Search-quality eval is manual by default, not a CI gate on every PR
 ## Context
 `skills/last30days/scripts/evaluate_search_quality.py` compares a baseline revision against a candidate revision across a fixed pool of reviewer topics. It produces two flavors of metrics: deterministic overlap (Jaccard, retention) and LLM-judged quality scores. The natural impulse on seeing an evaluator script is to wire it into CI on every PR — "regression catcher, run it automatically." We deliberately don't.
 Three properties of this particular evaluator make CI-on-every-PR the wrong default:
 1. **Live API access.** The candidate revision typically needs the engine to actually run, which means real ScrapeCreators calls, real reddit fetches, real YouTube searches. CI runs would either need production credentials or a record/replay fixture set that drifts almost immediately as external APIs change shape.
 2. **Cost and latency.** A full eval pass runs the pipeline N times across reviewer topics. Multiplied by every PR (including doc-only PRs), the spend is meaningful and the wall-clock pushes CI from ~30s to many minutes.
 3. **Non-determinism in the judging path.** The LLM-judged metrics are valuable for review but depend on judge-model behavior on a given day. A flaky eval that fails 1 PR in 20 because the judge re-scored an item differently is a worse CI signal than no eval at all — it teaches contributors to retry rather than read the result.
 The deterministic overlap metrics are useful regression signals but they are not the same as user-facing correctness. A change that improves overlap can degrade synthesis quality; a change that drops overlap can be a deliberate improvement. So even the deterministic side isn't safe to auto-fail on.
 ## Guidance
 ### 1. Keep search-quality eval available, just not automatic
 The script stays runnable by maintainers and contributors. The pattern is:
 ```bash
 LAST30DAYS_PYTHON=python3.13 \
  python3 skills/last30days/scripts/evaluate_search_quality.py \
  --baseline main --candidate HEAD
 ```
 Reviewers can request a manual eval run when a PR is in the retrieval/ranking/synthesis path and the risk warrants it. Contributors can run it locally before submitting if they want signal upfront.
 ### 2. Standard PR CI gates remain deterministic and contract-shaped
 `pytest` (offline-safe), plugin-contract checks, version-consistency contracts, ruff/lint. Anything that returns the same answer twice for the same input. Quality-of-output assessment lives outside that loop.
 ### 3. The middle ground is `workflow_dispatch`, not auto-PR-gating
 If maintainers want a GitHub-triggered eval that doesn't make every PR pay the live-API cost, the right shape is a manually-dispatched workflow (or a label-triggered one) — not a `pull_request:` workflow that runs unconditionally. That keeps the cost knob in human hands.
 ### 4. Revisit if the eval can ever be made offline-deterministic
 The blocker is the live-API + non-determinism combination. If a future iteration of the script can compute meaningful Jaccard/retention metrics against static fixtures (no live API calls, no LLM judging), the decision flips and it becomes a candidate for default CI. The decision below tracks that condition; revisit when it's met.
 ## What this means in practice
 - Don't merge PRs that wire `evaluate_search_quality.py` into the default `validate.yml` workflow.
 - Do merge PRs that add `workflow_dispatch` triggers or label-gated runs.
 - When reviewing a retrieval/ranking change, request a manual eval if the diff suggests it could regress quality — don't expect CI to catch it.
 ## Links
 - `skills/last30days/scripts/evaluate_search_quality.py` — the evaluator script
 - `docs/search-quality-eval.md` — user-facing usage documentation
 - `.github/workflows/validate.yml` — the default CI workflow (deterministic gates only)
 ---
 *Adapted from a draft ADR proposed by @hnshah in [#374](https://github.com/mvanhorn/last30days-skill/pull/374), restructured into the `docs/solutions/` convention. The original ADR text correctly identified the constraint; this version adds the "why workflow_dispatch is the middle ground" framing and the revisit-condition.*
@@ -1,219 +0,0 @@
 ---
 title: Release-time consistency tests cause cascade CI failures across all open PRs
 date: 2026-05-16
 category: docs/solutions/workflow-issues
 module: ci-release-engineering
 problem_type: workflow_issue
 component: testing_framework
 severity: high
 applies_when:
  - a test asserts consistency between two release-time artifacts (e.g., SKILL.md version and a hardcoded pin in a shell script)
  - one artifact is updated as part of a version bump and the other requires a manual lockstep update
  - multiple long-lived PRs are open simultaneously against the same base branch
 symptoms:
  - every open PR's CI fails after a version bump even though the PRs are unrelated to versioning
  - the failing test references a stale hardcoded value that was not updated alongside the bumped version
  - PR authors must rebase and manually fix an artifact they did not touch
 root_cause: missing_workflow_step
 resolution_type: code_fix
 related_components:
  - development_workflow
  - documentation
 tags:
  - ci
  - release-engineering
  - consistency-test
  - version-pin
  - cascade-failure
  - test-design
  - workflow
 ---
 # Release-time consistency tests cause cascade CI failures across all open PRs
 ## Context
 A `tests/test_version_consistency.py::test_sync_cache_path_uses_skill_version` test was added to enforce that the version string embedded in `skills/last30days/scripts/sync.sh` (a hardcoded plugin-cache path segment) matched the version frontmatter in `skills/last30days/SKILL.md`. The intention was sound: the cache path had to stay in lockstep with the skill version or the sync would silently pull stale files.
 The test worked as designed until a release shipped. At that point it turned into a cascade-failure machine:
 1. A release PR bumps `SKILL.md` version (e.g., 3.2.0 → 3.2.1) **and** bumps the `sync.sh` pin. That PR's CI is green.
 2. The release PR merges to `main`.
 3. Every PR that was open at merge time was branched from pre-release `main`. Those PRs have `SKILL.md` 3.2.1 (inherited via merge-base with `main`) but their branch never touched `sync.sh`.
 4. CI for those PRs runs the consistency test against the new `main` — `SKILL.md` says 3.2.1, `sync.sh` still says 3.2.0 — and fails.
 5. All open PRs are now red simultaneously, with a failure that has nothing to do with their changes.
 This affected at least five PRs during the 2026-05-13 to 2026-05-15 window: PR #400 (caught during rebase, required a manual pin bump), PRs #390 and #392 (OpenClaw `SCRAPECREATORS_API_KEY` fix, both stalled for the same stale-pin reason), and at least two others. A follow-up hotfix PR (#397 — `fix(sync): bump cache target to 3.2.1 to match SKILL.md`) was required just to unblock the queue.
 The permanent fix was PR #405: delete `sync.sh` entirely (the install workflow made it redundant) and drop `test_sync_cache_path_uses_skill_version`. Once both were gone, no version-consistency cascade was possible.
 ## Guidance
 ### 1. Don't write consistency tests that read two files and assert one matches a substring derived from the other
 This pattern looks safe but is not:
 ```python
 def test_sync_cache_path_uses_skill_version(self) -> None:
    sync_text = (SKILL_ROOT / "scripts" / "sync.sh").read_text(encoding="utf-8")
    version = _skill_version()          # reads SKILL.md
    self.assertIn(
        f'last30days-skill/last30days/{version}"',
        sync_text,                      # asserts sync.sh contains that string
    )
 ```
 It encodes the assumption that both files are always updated together, in the same commit, on the same branch. That assumption breaks the moment two files have independent lifecycle owners — a versioned manifest and a deployment script are archetypal examples.
 ### 2. If the values genuinely need to stay in sync, derive one from the other at runtime
 Remove the hardcoded pin from `sync.sh` and compute it:
 ```bash
 # sync.sh — derive version from SKILL.md at runtime, no pin to maintain
 SKILL_VERSION=$(grep -m1 '^version:' "$(dirname "$0")/../SKILL.md" \
    | sed 's/version:[[:space:]]*"\([^"]*\)"/\1/')
 CACHE_PATH="last30days-skill/last30days/${SKILL_VERSION}"
 ```
 Now there is only one source of truth (`SKILL.md`). The test that asserted they matched becomes vacuous and should be deleted. If `SKILL.md` is wrong, the sync itself will fail loudly — which is better feedback than a CI gate on a different PR.
 ### 3. If two values must stay independent for legitimate reasons, update them together and make the test self-skip if either source is missing
 If separate versioning is genuinely required (e.g., SKILL.md versions for harness consumers, sync.sh versions a private artifact store with its own cadence), update both in the same PR — never staggered — and write the test to self-skip rather than error when either file is absent:
 ```python
 def test_sync_cache_path_uses_skill_version(self) -> None:
    sync_sh = SKILL_ROOT / "scripts" / "sync.sh"
    if not sync_sh.exists():
        self.skipTest("sync.sh not present; skipping pin consistency check")
    sync_text = sync_sh.read_text(encoding="utf-8")
    version = _skill_version()
    self.assertIn(
        f'last30days-skill/last30days/{version}"',
        sync_text,
    )
 ```
 Self-skipping means deleting the file is a non-event in CI — no cascading red, no hotfix PR to the queue.
 ### 4. Run consistency tests against the merge-base diff, not main
 If you keep a two-file consistency test, scope it so it only fails when the PR itself modifies one of the two files but not the other. A GitHub Actions step can do this:
 ```yaml
 - name: Check sync.sh version pin consistency
  run: |
    BASE=$(git merge-base HEAD origin/main)
    SKILL_CHANGED=$(git diff --name-only "$BASE" HEAD | grep -c 'SKILL\.md' || true)
    SYNC_CHANGED=$(git diff --name-only "$BASE" HEAD | grep -c 'sync\.sh' || true)
    if [ "$SKILL_CHANGED" -gt 0 ] && [ "$SYNC_CHANGED" -eq 0 ]; then
      echo "SKILL.md version bumped but sync.sh pin was not updated"
      exit 1
    fi
 ```
 This only fires when your PR touched `SKILL.md` and left `sync.sh` alone — never because a release merged to `main` after you branched.
 ### 5. Ask whether you actually need this test
 If the values are wrong, downstream tooling will fail loudly: the sync will fetch the wrong artifact, installs will break, or the harness will reject the version. A test that exists only to catch a human-bookkeeping error at release time adds cascade-fail risk without offering a meaningfully earlier signal. Weigh that cost before adding any two-file consistency gate.
 ## Why This Matters
 The damage from a stale-pin consistency test is asymmetric. It:
 - Fails on every open PR simultaneously the moment a release lands on `main` — not just the PR that forgot to update the pin.
 - Produces a failure message that points at a line in a test file with no obvious relationship to the PR's actual changes.
 - Requires either a hotfix PR (touching a file the failing PRs have no business touching) or a manual rebase of every affected branch.
 - Blocks work that has already been reviewed and approved.
 In this repo the effect was measurable: at least five PRs stalled across a two-day window, one hotfix PR was shipped just to unblock the queue, and multiple authors spent time debugging a failure completely unrelated to their changes.
 The broader principle is that tests which gate on *bookkeeping consistency between files* impose their maintenance cost on every contributor, every time, even when those contributors did nothing wrong. That cost compounds with team size and release cadence.
 ## When to Apply
 Apply this guidance whenever you find yourself:
 - Writing a test that reads two files and asserts that a string in one matches a value derived from the other.
 - Adding a CI step labeled "consistency check," "sync check," or "pin check" where the check compares a hardcoded value against a computed one from a separate file.
 - Working in a repo where a versioned manifest (e.g., `SKILL.md`, `package.json`, `pyproject.toml`) and a deployment artifact (e.g., a shell script, a Dockerfile, a Helm values file) are both maintained by hand.
 - Reviewing a PR that touches only one of two "paired" files and fails a consistency test for the other.
 It does *not* apply to tests that read a single source of truth and validate its internal structure (e.g., asserting that `SKILL.md`'s frontmatter version is double-quoted, or that `package.json`'s `version` field is a valid semver string). Those tests have one file and one assertion; they cannot cascade across branches.
 ## Examples
 ### Before — the pattern that caused the cascade
 Original `tests/test_version_consistency.py` (deleted in commit `9fb19ea`):
 ```python
 import re
 import unittest
 from pathlib import Path
 ROOT = Path(__file__).resolve().parents[1]
 SKILL_ROOT = ROOT / "skills" / "last30days"
 def _skill_version() -> str:
    text = (SKILL_ROOT / "SKILL.md").read_text(encoding="utf-8")
    match = re.search(r'^version:\s*"([^"]+)"\s*$', text, re.MULTILINE)
    if not match:
        raise AssertionError("SKILL.md version frontmatter not found")
    return match.group(1)
 class TestVersionConsistency(unittest.TestCase):
    def test_sync_cache_path_uses_skill_version(self) -> None:
        sync_text = (SKILL_ROOT / "scripts" / "sync.sh").read_text(encoding="utf-8")
        version = _skill_version()          # source 1: SKILL.md frontmatter
        self.assertIn(                      # assertion: sync.sh must contain
            f'last30days-skill/last30days/{version}"',
            sync_text,                      # source 2: hardcoded string in sync.sh
        )
 ```
 `sync.sh` contained a line like:
 ```bash
 PLUGIN_CACHE="$HOME/.cache/last30days-skill/last30days/3.2.0"
 ```
 When SKILL.md bumped to `3.2.1` in a release PR, `sync.sh` was updated in the same PR and CI stayed green. But every PR branched before that release still had `sync.sh` at `3.2.0`. Their CI failed immediately, with an assertion error pointing at the test, not at the release PR.
 ### After — what we did: delete both
 PR #405 deleted `sync.sh` (the install workflow replaced it) and dropped `test_sync_cache_path_uses_skill_version` in the same change. No consistency gate, no pin to maintain, no cascade possible.
 ### After — what we could have done instead: derive at runtime
 If `sync.sh` had still been needed, the right fix would have been to remove the hardcoded version from the script and derive it from `SKILL.md`:
 ```bash
 #!/usr/bin/env bash
 # sync.sh — no hardcoded version; reads SKILL.md as single source of truth
 SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
 SKILL_VERSION=$(grep -m1 '^version:' "${SCRIPT_DIR}/../SKILL.md" \
    | sed 's/version:[[:space:]]*"\([^"]*\)"/\1/')
 if [ -z "$SKILL_VERSION" ]; then
    echo "error: could not parse version from SKILL.md" >&2
    exit 1
 fi
 PLUGIN_CACHE="$HOME/.cache/last30days-skill/last30days/${SKILL_VERSION}"
 # ... rest of sync logic
 ```
 With this in place, `test_sync_cache_path_uses_skill_version` has no reason to exist — there is nothing to assert. Delete it. If the version parsing breaks, `sync.sh` itself exits non-zero with a clear message.
 ## Related
 - **PR #397** (merged) — `fix(sync): bump cache target to 3.2.1 to match SKILL.md`. The hotfix that unblocked the cascade temporarily by bumping the pin.
 - **PR #400** (merged) — caught the same cascade during rebase; had to bump the pin to clear CI.
 - **PR #390** (closed) and **PR #392** (rebased + merged) — OpenClaw `SCRAPECREATORS_API_KEY` fix; both blocked by the cascade until rebased onto post-#405 main.
 - **PR #405** (merged) — the permanent fix: deleted `sync.sh` + `test_sync_cache_path_uses_skill_version` together.
 - **PR #412** (merged) — adjacent work that consolidated SKILL.md version parsing into `lib/skill_meta.py`, reducing future drift risk by giving the version field one canonical reader.
@@ -1,6 +1,6 @@
 {
  "name": "last30days-skill",
-  "version": "3.2.4",
+  "version": "3.0.5",
  "description": "Research a topic from the last 30 days across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the web.",
  "settings": [
    {
@@ -1,4 +0,0 @@
 {
  "triggerOnUpdates": true,
  "statusCheck": true
 }
@@ -6,7 +6,8 @@
        "hooks": [
          {
            "type": "command",
-            "command": "bash \"${CLAUDE_PLUGIN_ROOT:-${extensionPath:-.}}/hooks/scripts/check-config.sh\""
+            "command": "bash ${CLAUDE_PLUGIN_ROOT}/hooks/scripts/check-config.sh",
            "timeout": 5
          }
        ]
      }
@@ -33,13 +33,8 @@ load_env_vars() {
      [[ -z "$key" ]] && continue
      key=$(echo "$key" | xargs)
      value=$(echo "$value" | xargs | sed 's/^["'\''"]//;s/["'\''"]$//')
      # Strip inline comments (# preceded by whitespace) to prevent
      # command substitution in backtick-containing comments
      value="${value%%[[:space:]]#*}"
      if [[ -n "$key" && -n "$value" ]]; then
-        # printf -v writes via assignment semantics (global from inside a
+        eval "ENV_${key}=\"${value}\""
        # function), works on macOS's /bin/bash 3.2 — `declare -g` is 4.2+.
        printf -v "ENV_${key}" '%s' "$value"
      fi
    done < "$file"
  fi
@@ -63,53 +58,14 @@ fi
 # Check SETUP_COMPLETE (from file or env)
 SETUP_COMPLETE="${ENV_SETUP_COMPLETE:-${SETUP_COMPLETE:-}}"
 # Compute last-run summary line (if last-run.json exists)
 if [[ "${LAST30DAYS_CONFIG_DIR+x}" == "x" ]]; then
  if [[ -n "$LAST30DAYS_CONFIG_DIR" ]]; then
    LAST_RUN_FILE="$LAST30DAYS_CONFIG_DIR/last-run.json"
  else
    LAST_RUN_FILE=""
  fi
 else
  LAST_RUN_FILE="$HOME/.config/last30days/last-run.json"
 fi
 LAST_RUN_LINE=""
 if [[ -n "$LAST_RUN_FILE" && -f "$LAST_RUN_FILE" ]] && command -v python3 &>/dev/null; then
  LAST_RUN_LINE=$(LAST_RUN_FILE="$LAST_RUN_FILE" python3 - <<'PY' 2>/dev/null || true
 import datetime
 import json
 import os
 path = os.environ["LAST_RUN_FILE"]
 try:
    with open(path) as fh:
        d = json.load(fh)
    topic = (d.get("topic") or "?")[:60]
    ts = d.get("timestamp", "")
    dt = datetime.datetime.fromisoformat(ts.replace("Z", "+00:00"))
    delta = (datetime.datetime.now(datetime.timezone.utc) - dt).total_seconds()
    if delta < 60: ago = f"{int(delta)}s ago"
    elif delta < 3600: ago = f"{int(delta//60)}m ago"
    elif delta < 86400: ago = f"{int(delta//3600)}h ago"
    else: ago = f"{int(delta//86400)}d ago"
    total = d.get("total", 0)
    print(f"  Last run: \"{topic}\" · {ago} · {total} results")
 except Exception:
    pass
 PY
 )
 fi
 # If setup has never been run, show welcome message for new users
 if [[ -z "$SETUP_COMPLETE" && -z "$CONFIG_FILE" && -z "${OPENAI_API_KEY:-}" && -z "${SCRAPECREATORS_API_KEY:-}" && -z "${AUTH_TOKEN:-}" && -z "${XAI_API_KEY:-}" ]]; then
  cat <<'EOF'
 /last30days: Ready to use. Run /last30days to get started — setup takes 30 seconds.
  Research any topic across Reddit, HN, X, YouTube, Polymarket (last 30 days).
 Reddit, Hacker News, and Polymarket work out of the box.
 The setup wizard can unlock X/Twitter, YouTube, and more.
 EOF
  [[ -n "$LAST_RUN_LINE" ]] && echo "$LAST_RUN_LINE"
  exit 0
 fi
@@ -141,33 +97,16 @@ if [[ -n "$HAS_BSKY" ]]; then
  SOURCE_COUNT=$((SOURCE_COUNT + 1))
 fi
 if [[ -n "$HAS_SCRAPECREATORS" ]]; then
-  # Start with Reddit comments + TikTok + Instagram, subtract any in EXCLUDE_SOURCES.
+  SOURCE_COUNT=$((SOURCE_COUNT + 3))  # Reddit comments + TikTok + Instagram
  # Normalise EXCLUDED (lowercase + collapse whitespace around commas + strip outer
  # whitespace) so the matching mirrors pipeline.py's .strip().lower() parsing.
  SC_ADD=3
  EXCLUDED="${ENV_EXCLUDE_SOURCES:-${EXCLUDE_SOURCES:-}}"
  EXCLUDED_NORM=$(printf '%s' "$EXCLUDED" | tr '[:upper:]' '[:lower:]' \
    | sed -E 's/[[:space:]]*,[[:space:]]*/,/g; s/^[[:space:]]+//; s/[[:space:]]+$//')
  if [[ ",$EXCLUDED_NORM," == *",tiktok,"* ]]; then
    SC_ADD=$((SC_ADD - 1))
  fi
  if [[ ",$EXCLUDED_NORM," == *",instagram,"* ]]; then
    SC_ADD=$((SC_ADD - 1))
  fi
  SOURCE_COUNT=$((SOURCE_COUNT + SC_ADD))
 fi
 if [[ -n "$HAS_SCRAPECREATORS" ]]; then
  # Fully configured — compact ready message
  echo "/last30days: Ready — ${SOURCE_COUNT} sources active."
  echo "  Research any topic across social + market + web sources (last 30 days)."
  [[ -n "$LAST_RUN_LINE" ]] && echo "$LAST_RUN_LINE"
 else
  # Setup done but missing ScrapeCreators — recommend it
  echo "/last30days: Ready — ${SOURCE_COUNT} sources active."
  echo "  Research any topic across social + market + web sources (last 30 days)."
  [[ -n "$LAST_RUN_LINE" ]] && echo "$LAST_RUN_LINE"
  echo "  Tip: Add ScrapeCreators for Reddit comments + TikTok + Instagram."
-  echo "  100 free credits, no credit card — scrapecreators.com"
+  echo "  10,000 free API calls, no credit card — scrapecreators.com"
  echo "  last30days has no affiliation with any API provider."
 fi
@@ -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.
@@ -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"
  }
 }
@@ -0,0 +1,6 @@
 import { Config } from "@remotion/cli/config";
 Config.setVideoImageFormat("jpeg");
 Config.setOverwriteOutput(true);
 Config.setConcurrency(null);
 Config.setCodec("h264");
@@ -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>
  );
 };
@@ -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}
      />
    </>
  );
 };
@@ -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>
  );
 };
@@ -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>
  );
 };
@@ -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>
  );
 };
@@ -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>
  );
 };
@@ -0,0 +1,4 @@
 import { registerRoot } from "remotion";
 import { RemotionRoot } from "./Root";
 registerRoot(RemotionRoot);
@@ -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';
@@ -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;
@@ -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>
  );
 };
@@ -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 ${COLORS.border}`,
              fontFamily: FONT_MONO,
              fontSize: 26,
              color: COLORS.fgPrimary,
              lineHeight: 1.6,
            }}
          >
            <div style={{ color: COLORS.accentGreen }}>
              ✅ All agents reported back!
            </div>
            <div style={{ color: COLORS.fgMuted, marginTop: 6 }}>
              ├─ 🟠 Reddit: 14 threads
            </div>
            <div style={{ color: COLORS.fgMuted }}>
              ├─ 🔵 X: 22 posts
            </div>
            <div style={{ color: COLORS.fgMuted }}>
              └─ 🟡 HN: 1 story
            </div>
          </div>
        </TerminalWindow>
      </div>
      <div
        style={{
          opacity: captionFade,
          marginTop: 60,
          fontFamily: FONT_SANS,
          fontSize: 38,
          color: COLORS.fgMuted,
        }}
      >
        The old way: <span style={{ color: COLORS.fgPrimary }}>one topic.</span>
      </div>
    </AbsoluteFill>
  );
 };
@@ -0,0 +1,221 @@
 import React from "react";
 import { AbsoluteFill, interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion";
 import { TerminalWindow } from "../components/TerminalWindow";
 import { TypedLine } from "../components/TypedLine";
 import { COLORS, FONT_MONO, FONT_SANS } from "../lib/colors";
 const PROGRESS_LINES = [
  { source: "Reddit", color: "#ff6a3d" },
  { source: "X", color: "#36d6f7" },
  { source: "YouTube", color: "#ff5757" },
  { source: "TikTok", color: "#5fff9f" },
  { source: "Instagram", color: "#ff55a3" },
 ];
 const ENTITIES: { label: string; tag: string; accent: string }[] = [
  { label: "OpenAI", tag: "$ /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>
  );
 };
@@ -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>
  );
 };
@@ -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>
  );
 };
@@ -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>
  );
 };
@@ -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/**/*"]
 }
@@ -1,14 +1,16 @@
 [project]
 name = "last30days-skill"
-version = "3.3.0"
+version = "3.0.0"
 description = "Multi-source last-30-days research skill"
 readme = "README.md"
 requires-python = ">=3.12"
-dependencies = []
+dependencies = [
  "requests>=2.32,<3",
 ]
 [dependency-groups]
 dev = [
-  "pytest>=9.0.3,<10",
+  "pytest>=9,<10",
  "pytest-cov>=7,<8",
 ]
@@ -22,9 +24,9 @@ addopts = [
 [tool.coverage.run]
 branch = true
-source = ["skills/last30days/scripts", "tests"]
+source = ["scripts", "tests"]
 omit = [
-  "skills/last30days/scripts/lib/vendor/*",
+  "scripts/lib/vendor/*",
  "dist/*",
 ]
@@ -32,6 +34,7 @@ omit = [
 skip_empty = true
 show_missing = true
 omit = [
-  "skills/last30days/scripts/lib/vendor/*",
+  "scripts/lib/vendor/*",
  "dist/*",
 ]
@@ -1,64 +1,52 @@
 ## v3.3.0 — install everywhere, ship the reliability sweep
 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, Digg, 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.
+`/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.
-## What's new in v3.3.0
+## v3 is the intelligent search release
-### Install everywhere with one command
+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.
-`npx skills add mvanhorn/last30days-skill -g -y` is now the canonical install path for **every harness** — Claude Code, OpenAI Codex CLI, Cursor, Gemini CLI, GitHub Copilot, Windsurf, and 50+ other Agent Skills hosts. The skill auto-detects each harness's skills directory and symlinks in place, so edits propagate live. No more per-harness manual paths in the README.
+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.
-### New emit mode: `--emit=html`
+## Headline features
-Shareable, print-friendly HTML briefs. Drop the file in Slack, mail it to a stakeholder, or print it for the meeting. Same data as compact mode, structured for human reading.
+### Intelligent pre-research
-### New source: Digg
+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.
-Digg surfaces curated story clusters from the AI 1000 leaderboard and pulls attributable X-post quotes directly into the brief. Auto-enabled when `digg-pp-cli` is on PATH. Footer line: `⛏️ Digg: N clusters │ K posts │ M authors`. No X auth required for the inline quotes.
+### Best Takes
-### YouTube residential-IP routing (`LAST30DAYS_YOUTUBE_SSH_HOST`)
+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.
-Running on a datacenter VPS (Hetzner, DigitalOcean, AWS, etc.)? YouTube's bot-wall fingerprints datacenter IP ranges before any cookie check. Set `LAST30DAYS_YOUTUBE_SSH_HOST=<ssh-alias>` and yt-dlp runs over SSH against a residential-IP host instead. One env var, no proxy service required.
+### Cross-source cluster merging
-### macOS Keychain credential source
+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.
-When env vars and config files aren't set, the engine now reads credentials from the macOS Keychain. Stores secrets where macOS expects them; nothing on disk in plaintext.
+### Single-pass comparisons
-### `EXCLUDE_SOURCES` env var
+"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.
-The inverse of `INCLUDE_SOURCES`. Useful for "everything except TikTok" or "everything except the slow ones."
+### GitHub person-mode and project-mode
-## Reliability sweep
+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.
-This release closes a long tail of platform-specific issues that have been accumulating:
+When the topic is a project, it pulls live star counts, READMEs, releases, and top issues from the GitHub API. No stale blog posts.
- **Reddit**: subreddits starting with `r` no longer get mangled by `lstrip("r/")`. Browser-like headers + gzip handling fix urllib 403s on the public JSON endpoint. HTTP 402 now triggers the OpenAI/public-JSON fallback chain when ScrapeCreators credits are exhausted.
+### ELI5 mode
 - **xAI**: empty or malformed responses now surface in `errors_by_source` instead of silently returning zero results.
 - **Windows**: process cleanup no longer crashes on `os.killpg`. POSIX-style secret-permission warnings skipped. Save-path footer uses forward slashes.
 - **Auth**: comma-separated `SCRAPECREATORS_API_KEY=key1,key2` rotation restored (accidentally dropped in v3.0.6).
 - **YouTube + HN**: SC YouTube + multi-token HN searches unblocked. Transcript-fetch ratio surfaced.
 - **HTTP**: retry budget expanded with exponential backoff on DNS failure. Parallel AI search aligned with current API schema.
 - **OpenClaw**: now works without a ScrapeCreators key. Poll-timing initialized once.
-## Multi-harness reframe
+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.
-`AGENTS.md` is now the canonical project doc; `CLAUDE.md` points at it. The skill is positioned as a multi-harness Agent Skills package, not a Claude-Code-specific tool. SKILL.md's path resolution rewrote `SKILL_ROOT` → `SKILL_DIR`, removing ~80 lines of bash and fixing a real spec-vs-engine divergence bug.
+### 13+ sources
-## Breaking change
+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.
-**`.codex-plugin/plugin.json` removed.** Codex native-plugin users should install via `npx skills add mvanhorn/last30days-skill` or copy the skill to `~/.codex/skills/last30days/`. The `npx skills add` path now reaches every harness uniformly.
+### Per-author cap and entity disambiguation
 Max 3 items per author prevents single-voice dominance. Synthesis trusts resolved handles over fuzzy keyword matches.
 ## Install
-Any harness (recommended):
+Claude Code:
 ```
 npx skills add mvanhorn/last30days-skill -g -y
 ```
 Claude Code marketplace:
 ```
 /plugin marketplace add mvanhorn/last30days-skill
@@ -70,21 +58,29 @@ 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.
-## Contributors
+## v3 Community
-First-time contributors whose fixes shipped in v3.3.0 (most via PR triage salvage — the fix re-applied directly to main with co-author credit when path migration made the original branch un-rebaseable):
+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.
- Dave Morin — portable test-harness paths
+Thanks to @uppinote20, @zerone0x, @thinkun, @thomasmktong, @fanispoulinakisai-boop, @pejmanjohn, @zl190, and @hnshah. See [CONTRIBUTORS.md](CONTRIBUTORS.md) for the full list.
 - Alex Key — `removeprefix("r/")` for subreddit names
 - Eric Oberhofer — multi-key rotation restored
 - gujishh — Windows process cleanup
 - Franco Carballar — Reddit browser-like headers
 - Jonathan Oppenheim — Reddit 402 fallback chain
 - Kaustav Mishra — xAI error surfacing
 - [@thinkun](https://github.com/thinkun) — OpenClaw ScrapeCreators-key-optional fix
-Plus every contributor who shipped one of the ~75 PRs merged this cycle. See [CHANGELOG.md](CHANGELOG.md) under `[3.3.0]` for the full PR list and `git log v3.2.0..v3.3.0` for the complete commit graph.
+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.
@@ -1,14 +1,13 @@
 #!/usr/bin/env bash
 # build-skill.sh - package this repo as a claude.ai-upload-ready .skill file
-# Usage: bash skills/last30days/scripts/build-skill.sh  (run from repo root)
+# 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 from skills/last30days.
+# directory containing SKILL.md and the scripts/ runtime. See
 # See
 # docs/plans/2026-04-14-001-fix-skill-upload-200-file-limit-plan.md.
 set -euo pipefail
-REPO_ROOT="$(cd "$(dirname "$0")/../../.." && pwd)"
+REPO_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
 cd "$REPO_ROOT"
 if ! git diff --quiet || ! git diff --cached --quiet; then
@@ -18,7 +17,14 @@ fi
 mkdir -p dist
 OUT="dist/last30days.skill"
-git archive --format=zip --prefix=last30days/ --output="$OUT" HEAD:skills/last30days
+git archive --format=zip --prefix=last30days/ --output="$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)
@@ -1,6 +1,6 @@
 #!/bin/bash
 # A/B test runner: public release vs private beta
-# Usage: bash skills/last30days/scripts/compare.sh "Kanye West"
+# 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,
@@ -9,8 +9,8 @@
 set -e
 if [ $# -eq 0 ]; then
-  echo "Usage: bash skills/last30days/scripts/compare.sh <topic>"
+  echo "Usage: bash scripts/compare.sh <topic>"
-  echo "  Example: bash skills/last30days/scripts/compare.sh Kevin Rose"
+  echo "  Example: bash scripts/compare.sh Kevin Rose"
  exit 1
 fi
 TOPIC="$*"
@@ -20,11 +20,9 @@ sys.path.insert(0, str(Path(__file__).parent))
 from lib import env as envlib
 from lib import schema
 from lib.providers import GEMINI_FLASH_LITE
-SKILL_ROOT = Path(__file__).resolve().parents[1]
+REPO_ROOT = Path(__file__).resolve().parent.parent
 REPO_ROOT = Path(__file__).resolve().parents[3]
 EVAL_TOPICS_FILE = REPO_ROOT / "fixtures" / "eval_topics.json"
@@ -44,7 +42,7 @@ def _load_default_topics() -> list[tuple[str, str]]:
 DEFAULT_TOPICS = _load_default_topics()
 DEFAULT_SEARCH = ""
-DEFAULT_JUDGE_MODEL = GEMINI_FLASH_LITE
+DEFAULT_JUDGE_MODEL = "gemini-3.1-flash-lite-preview"
 GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={api_key}"
@@ -309,10 +307,7 @@ def create_eval_env() -> dict[str, str]:
 def run_last30days(repo_dir: Path, topic: str, *, search: str, timeout_seconds: int, quick: bool, mock: bool, env: dict[str, str]) -> dict[str, Any]:
-    engine = repo_dir / "skills" / "last30days" / "scripts" / "last30days.py"
+    cmd = [sys.executable, "scripts/last30days.py", topic, "--emit=json"]
    if not engine.exists():
        engine = repo_dir / "scripts" / "last30days.py"
    cmd = [sys.executable, str(engine), topic, "--emit=json"]
    if search:
        cmd.extend(["--search", search])
    if quick:
@@ -1,12 +1,11 @@
 #!/usr/bin/env python3
 # ruff: noqa: E402
-"""last30days CLI."""
+"""last30days v3.0.0 CLI."""
 from __future__ import annotations
 import argparse
 import atexit
 import datetime
 import json
 import os
 import re
@@ -42,7 +41,7 @@ if os.name == "nt":
 SCRIPT_DIR = Path(__file__).parent.resolve()
 sys.path.insert(0, str(SCRIPT_DIR))
-from lib import env, html_render, pipeline, render, schema, ui
+from lib import env, pipeline, render, schema, ui
 _child_pids: set[int] = set()
 _child_pids_lock = threading.Lock()
@@ -63,10 +62,7 @@ def _cleanup_children() -> None:
        pids = list(_child_pids)
    for pid in pids:
        try:
-            if hasattr(os, "killpg"):
+            os.killpg(os.getpgid(pid), signal.SIGTERM)
                os.killpg(os.getpgid(pid), signal.SIGTERM)
            else:
                os.kill(pid, signal.SIGTERM)
        except (ProcessLookupError, PermissionError, OSError):
            continue
@@ -95,50 +91,30 @@ def slugify(value: str) -> str:
    return slug or "last30days"
-def save_output(
+def save_output(report: schema.Report, emit: str, save_dir: str, suffix: str = "") -> Path:
    report: schema.Report,
    emit: str,
    save_dir: str,
    suffix: str = "",
    synthesis_md: str | None = None,
    topic_override: str | None = None,
    rendered_content: str | None = None,
 ) -> Path:
    from datetime import datetime
    path = Path(save_dir).expanduser().resolve()
    path.mkdir(parents=True, exist_ok=True)
-    slug = slugify(topic_override or report.topic)
+    slug = slugify(report.topic)
-    extension = "json" if emit == "json" else "html" if emit == "html" else "md"
+    extension = "json" if emit == "json" else "md"
    raw_label = "raw-html" if emit == "html" else "raw"
    suffix_part = f"-{suffix}" if suffix else ""
-    out_path = path / f"{slug}-{raw_label}{suffix_part}.{extension}"
+    out_path = path / f"{slug}-raw{suffix_part}.{extension}"
    if out_path.exists():
-        out_path = path / f"{slug}-{raw_label}{suffix_part}-{datetime.now().strftime('%Y-%m-%d')}.{extension}"
+        out_path = path / f"{slug}-raw{suffix_part}-{datetime.now().strftime('%Y-%m-%d')}.{extension}"
-    # Markdown saves keep the complete debug artifact. JSON and HTML preserve
+    # Always save the FULL dump to disk (all items, all sources, transcripts).
-    # their requested wire format so file extensions match their content.
+    # Claude sees compact clusters via --emit=compact on stdout.
-    if rendered_content is not None:
+    # The saved file is the complete debug artifact.
-        content = rendered_content
+    if emit == "json":
-    elif emit in {"json", "html"}:
+        content = emit_output(report, emit)
        content = emit_output(report, emit, synthesis_md=synthesis_md)
    else:
        content = render.render_full(report)
    out_path.write_text(content, encoding="utf-8")
    return out_path
-def emit_output(
+def emit_output(report: schema.Report, emit: str, fun_level: str = "medium", save_path: str | None = None) -> str:
    report: schema.Report,
    emit: str,
    fun_level: str = "medium",
    save_path: str | None = None,
    synthesis_md: str | None = None,
 ) -> str:
    if emit == "json":
        return json.dumps(schema.to_dict(report), indent=2, sort_keys=True)
    if emit == "html":
        return html_render.render_html(
            report, fun_level=fun_level, save_path=save_path, synthesis_md=synthesis_md,
        )
    if emit in {"compact", "md"}:
        return render.render_compact(report, fun_level=fun_level, save_path=save_path)
    if emit == "context":
@@ -151,7 +127,6 @@ def emit_comparison_output(
    emit: str,
    fun_level: str = "medium",
    save_path: str | None = None,
    synthesis_md: str | None = None,
 ) -> str:
    if emit == "json":
        payload = {
@@ -163,13 +138,6 @@ def emit_comparison_output(
            ],
        }
        return json.dumps(payload, indent=2, sort_keys=True)
    if emit == "html":
        return html_render.render_html_comparison(
            entity_reports,
            fun_level=fun_level,
            save_path=save_path,
            synthesis_md=synthesis_md,
        )
    if emit in {"compact", "md"}:
        return render.render_comparison_multi(
            entity_reports, fun_level=fun_level, save_path=save_path,
@@ -179,10 +147,6 @@ def emit_comparison_output(
    raise SystemExit(f"Unsupported emit mode: {emit}")
 def comparison_topic(entity_reports: list[tuple[str, schema.Report]]) -> str:
    return " vs ".join(label for label, _ in entity_reports)
 def compute_save_path_display(save_dir: str, topic: str, suffix: str, emit: str) -> str:
    """Compute the user-friendly save path string that will be shown in the footer.
@@ -192,24 +156,15 @@ def compute_save_path_display(save_dir: str, topic: str, suffix: str, emit: str)
    from pathlib import Path as _Path
    path = _Path(save_dir).expanduser().resolve()
    slug = slugify(topic)
-    extension = "json" if emit == "json" else "html" if emit == "html" else "md"
+    extension = "json" if emit == "json" else "md"
    raw_label = "raw-html" if emit == "html" else "raw"
    suffix_part = f"-{suffix}" if suffix else ""
-    raw = path / f"{slug}-{raw_label}{suffix_part}.{extension}"
+    raw = path / f"{slug}-raw{suffix_part}.{extension}"
    try:
        home = _Path.home().resolve()
        relative = raw.relative_to(home)
-        return f"~/{relative.as_posix()}"
+        return f"~/{relative}"
    except ValueError:
-        return raw.as_posix()
+        return str(raw)
 def read_synthesis_file(path: str) -> str:
    try:
        return Path(path).expanduser().read_text(encoding="utf-8")
    except OSError as exc:
        sys.stderr.write(f"[last30days] Cannot read --synthesis-file: {exc}\n")
        raise SystemExit(2)
 def persist_report(report: schema.Report) -> dict[str, int]:
@@ -238,7 +193,7 @@ def persist_report(report: schema.Report) -> dict[str, int]:
 def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Research a topic across live social, market, and grounded web sources.")
    parser.add_argument("topic", nargs="*", help="Research topic")
-    parser.add_argument("--emit", default="compact", choices=["compact", "json", "context", "md", "html"])
+    parser.add_argument("--emit", default="compact", choices=["compact", "json", "context", "md"])
    parser.add_argument("--search", help="Comma-separated source list")
    parser.add_argument("--quick", action="store_true", help="Lower-latency retrieval profile")
    parser.add_argument("--deep", action="store_true", help="Higher-recall retrieval profile")
@@ -246,7 +201,6 @@ def build_parser() -> argparse.ArgumentParser:
    parser.add_argument("--mock", action="store_true", help="Use mock retrieval fixtures")
    parser.add_argument("--diagnose", action="store_true", help="Print provider and source availability")
    parser.add_argument("--save-dir", help="Optional directory for saving the rendered output")
    parser.add_argument("--synthesis-file", help="Markdown synthesis to embed in --emit=html output")
    parser.add_argument("--store", action="store_true", help="Persist ranked findings to the SQLite research store")
    parser.add_argument("--x-handle", help="X handle for targeted supplemental search")
    parser.add_argument("--x-related", help="Comma-separated related X handles (searched with lower weight)")
@@ -391,7 +345,7 @@ def subrun_kwargs_for(
    subreddits = _choose("subreddits", "subreddits")
    if isinstance(subreddits, list):
-        subreddits = [s.strip().removeprefix("r/") for s in subreddits if s.strip()] or None
+        subreddits = [s.strip().lstrip("r/") for s in subreddits if s.strip()] or None
    x_related = plan_entry.get("x_related")
    if isinstance(x_related, list):
@@ -535,24 +489,6 @@ def _show_runtime_ui(
        progress.show_promo(promo, diag=diag)
 def _write_last_run(topic: str, report: "schema.Report") -> None:
    try:
        if env.CONFIG_DIR is None:
            return
        target = env.CONFIG_DIR
        target.mkdir(parents=True, exist_ok=True)
        counts = {source: len(items) for source, items in report.items_by_source.items()}
        payload = {
            "topic": topic,
            "timestamp": datetime.datetime.now(datetime.timezone.utc).isoformat(),
            "sources": counts,
            "total": sum(counts.values()),
        }
        (target / "last-run.json").write_text(json.dumps(payload, indent=2))
    except Exception:
        pass
 def main() -> int:
    parser = build_parser()
    # Use parse_known_args so setup sub-flags (--device-auth, --github,
@@ -563,13 +499,6 @@ def main() -> int:
    config = env.get_config()
    # Surface SSH-routing config as an env var so library modules (e.g.
    # youtube_yt) can read it without taking a config dependency. This
    # routes yt-dlp through `ssh <host>` to bypass YouTube's bot-wall on
    # datacenter IPs (see lib/youtube_yt.py for details).
    if config.get("LAST30DAYS_YOUTUBE_SSH_HOST") and "LAST30DAYS_YOUTUBE_SSH_HOST" not in os.environ:
        os.environ["LAST30DAYS_YOUTUBE_SSH_HOST"] = config["LAST30DAYS_YOUTUBE_SSH_HOST"]
    # Handle setup subcommand
    topic = " ".join(args.topic).strip()
    if topic.lower() == "setup":
@@ -608,13 +537,6 @@ def main() -> int:
        parser.print_usage(sys.stderr)
        return 2
    synthesis_md = None
    if args.synthesis_file:
        if args.emit == "html":
            synthesis_md = read_synthesis_file(args.synthesis_file)
        else:
            sys.stderr.write("[last30days] Warning: --synthesis-file is only used with --emit=html; ignoring.\n")
    if not os.environ.get("LAST30DAYS_SKIP_PREFLIGHT"):
        from lib import preflight
        refuse_msg = preflight.check_class_1_trap(topic)
@@ -628,7 +550,7 @@ def main() -> int:
    depth = "deep" if args.deep else "quick" if args.quick else "default"
    try:
        x_related = [h.strip() for h in args.x_related.split(",") if h.strip()] if args.x_related else None
-        subreddits = [s.strip().removeprefix("r/") for s in args.subreddits.split(",") if s.strip()] if args.subreddits else None
+        subreddits = [s.strip().lstrip("r/") for s in args.subreddits.split(",") if s.strip()] if args.subreddits else None
        tiktok_hashtags = [h.strip().lstrip("#") for h in args.tiktok_hashtags.split(",") if h.strip()] if args.tiktok_hashtags else None
        tiktok_creators = [c.strip().lstrip("@") for c in args.tiktok_creators.split(",") if c.strip()] if args.tiktok_creators else None
        ig_creators = [c.strip().lstrip("@") for c in args.ig_creators.split(",") if c.strip()] if args.ig_creators else None
@@ -647,7 +569,6 @@ def main() -> int:
        # Auto-resolve: use web search to discover subreddits/handles before planning.
        # This is the engine-side equivalent of SKILL.md Steps 0.55/0.75 for platforms
        # without WebSearch (OpenClaw, Codex, raw CLI).
        repos_from_auto_resolve = False
        if args.auto_resolve and not external_plan:
            from lib import resolve
            resolution = resolve.auto_resolve(topic, config)
@@ -662,9 +583,6 @@ def main() -> int:
                sys.stderr.write(f"[AutoResolve] GitHub user: @{args.github_user}\n")
            if resolution.get("github_repos") and not args.github_repo:
                args.github_repo = ",".join(resolution["github_repos"])
                # auto_resolve already canonicalized via canonicalize_github_repos(cap=5);
                # mark so we don't re-canonicalize below and clobber its relevance order.
                repos_from_auto_resolve = True
                sys.stderr.write(f"[AutoResolve] GitHub repos: {args.github_repo}\n")
            if resolution.get("context"):
                # Inject context into external_plan metadata for the planner to use
@@ -677,20 +595,6 @@ def main() -> int:
        github_user = args.github_user.lstrip("@").lower() if args.github_user else None
        github_repos = [r.strip() for r in args.github_repo.split(",") if r.strip() and "/" in r.strip()] if args.github_repo else None
        # Only canonicalize when repos came from a user-supplied --github-repo flag.
        # When repos_from_auto_resolve is True, auto_resolve already ran
        # canonicalize_github_repos(cap=5) and ranked by relevance; re-running here
        # with cap=None can re-sort by topic-slug match and lose that ordering.
        if github_repos and not repos_from_auto_resolve:
            from lib import resolve as resolve_lib
            original_github_repos = github_repos[:]
            github_repos = resolve_lib.canonicalize_github_repos(topic, github_repos, cap=None)
            if github_repos != original_github_repos:
                sys.stderr.write(
                    "[GitHub] Canonicalized repos: "
                    f"{','.join(original_github_repos)} -> {','.join(github_repos)}\n"
                )
        # --deep-research: auto-enable perplexity source and set deep flag
        if args.deep_research:
            if not config.get("OPENROUTER_API_KEY"):
@@ -910,18 +814,7 @@ def main() -> int:
        report, progress, diag,
        suppress_web_promo=bool(external_plan or comp_plan),
    )
-    _write_last_run(topic, report)
+    if args.store:
    # LAST30DAYS_STORE env var = persistence default-on. Read both os.environ
    # (for shell-exported users) and config (for users who set it in
    # ~/.config/last30days/.env, which env.py loads but does not propagate
    # to os.environ). Mirrors the LAST30DAYS_DEBUG / LAST30DAYS_SKIP_PREFLIGHT
    # convention; env-var or config wins, with `--store` flag still working.
    _store_env = (
        os.environ.get("LAST30DAYS_STORE")
        or config.get("LAST30DAYS_STORE")
        or ""
    ).lower()
    if args.store or _store_env in ("1", "true", "yes"):
        counts = persist_report(report)
        sys.stderr.write(
            f"[last30days] Stored {counts['new']} new, {counts['updated']} updated findings\n"
@@ -931,32 +824,7 @@ def main() -> int:
    # Show quality nudge if applicable
    try:
        from lib import quality_nudge
-        # Populate transcript-fetch ratio so quality_nudge can detect the
+        quality = quality_nudge.compute_quality_score(config, {})
        # degraded-YouTube failure mode (videos returned but transcripts
        # silently failed - typically a stale yt-dlp binary).
        youtube_items = report.items_by_source.get("youtube") or []
        instagram_items = report.items_by_source.get("instagram") or []
        research_results = {
            "youtube_videos_count": len(youtube_items),
            "youtube_transcripts_count": sum(
                1 for it in youtube_items
                if (it.metadata.get("transcript_highlights") or it.metadata.get("transcript_snippet"))
            ),
            "youtube_error": report.errors_by_source.get("youtube"),
            "x_error": report.errors_by_source.get("x"),
            # Captions-disabled videos can never produce a transcript regardless
            # of yt-dlp version; subtract them from the degraded-ratio
            # denominator so a single uploader-disabled video does not trip the
            # "stale yt-dlp" nudge.
            "youtube_captions_disabled_count": sum(
                1 for it in youtube_items if it.metadata.get("captions_disabled")
            ),
            # Track Instagram returned-zero-items so quality_nudge can detect
            # the silent-failure case (SC configured but the v2 reels endpoint
            # 500'd through both the original query and the hashtag retry).
            "instagram_items_count": len(instagram_items),
        }
        quality = quality_nudge.compute_quality_score(config, research_results)
        if quality.get("nudge_text"):
            sys.stderr.write(f"\n{quality['nudge_text']}\n")
            sys.stderr.flush()
@@ -964,15 +832,10 @@ def main() -> int:
        pass
    fun_level = config.get("FUN_LEVEL", "medium").lower()
    # Comparison HTML is the one case where the saved file's title and content
    # have to be overridden away from the leading entity's report. Compute the
    # gate once so the footer-display and save-output paths can't disagree.
    is_comparison_html = bool(entity_reports) and args.emit == "html"
    footer_save_path = None
    if args.save_dir:
        save_topic_for_display = comparison_topic(entity_reports) if is_comparison_html else report.topic
        footer_save_path = compute_save_path_display(
-            args.save_dir, save_topic_for_display, args.save_suffix or "", args.emit
+            args.save_dir, report.topic, args.save_suffix or "", args.emit
        )
    # Signal to render_compact whether pre-research flags were supplied.
@@ -991,31 +854,15 @@ def main() -> int:
    if entity_reports:
        rendered = emit_comparison_output(
-            entity_reports,
+            entity_reports, args.emit, fun_level=fun_level, save_path=footer_save_path,
            args.emit,
            fun_level=fun_level,
            save_path=footer_save_path,
            synthesis_md=synthesis_md,
        )
    else:
        rendered = emit_output(
-            report,
+            report, args.emit, fun_level=fun_level, save_path=footer_save_path,
            args.emit,
            fun_level=fun_level,
            save_path=footer_save_path,
            synthesis_md=synthesis_md,
        )
    if args.save_dir:
        # Save the main topic's raw file (single-entity or comparison main).
-        save_path = save_output(
+        save_path = save_output(report, args.emit, args.save_dir, suffix=args.save_suffix or "")
            report,
            args.emit,
            args.save_dir,
            suffix=args.save_suffix or "",
            synthesis_md=synthesis_md,
            topic_override=comparison_topic(entity_reports) if is_comparison_html else None,
            rendered_content=rendered if is_comparison_html else None,
        )
        sys.stderr.write(f"[last30days] Saved output to {save_path}\n")
        # Competitor / vs-mode: also save a per-entity raw file for each peer.
        # Matches historical vs-mode behavior (N passes → N save files).
@@ -1024,7 +871,6 @@ def main() -> int:
                peer_path = save_output(
                    entity_report, args.emit, args.save_dir,
                    suffix=args.save_suffix or "",
                    synthesis_md=synthesis_md,
                )
                sys.stderr.write(f"[last30days] Saved output to {peer_path}\n")
        sys.stderr.flush()
@@ -7,22 +7,18 @@ See scripts/lib/vendor/bird-search/package.json for authoritative version.
 import json
 import os
 import signal
 import shutil
 import subprocess
 import sys
 import time
 from pathlib import Path
-from . import http, log, subproc
+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
 # How many times to retry the bird-search subprocess when stdout is non-JSON
 # (typically an HTML anti-bot interstitial from Twitter's edge).
 MAX_JSON_DECODE_RETRIES = 2
 JSON_DECODE_RETRY_DELAY = 5.0  # seconds between retry attempts
 def _first_of(*values):
    """Return first value that is not None."""
@@ -154,14 +150,16 @@ def get_bird_status() -> Dict[str, Any]:
    }
-def _invoke_bird_subprocess(query: str, count: int, timeout: int):
+def _run_bird_search(query: str, count: int, timeout: int) -> Dict[str, Any]:
-    """Invoke the vendored bird-search.mjs subprocess once.
+    """Run a search using the vendored bird-search.mjs module.
-    Returns (result, error_dict). If error_dict is non-None, treat it as the
+    Args:
-    final result and do not retry — those errors are terminal (timeout,
+        query: Full search query string (including since: filter)
-    spawn failure). If error_dict is None, the subprocess ran to completion
+        count: Number of results to request
-    and `result` is the SubprocResult; the caller decides whether to retry
+        timeout: Timeout in seconds
-    based on the result.stdout content.
+
    Returns:
        Raw Bird JSON response or error dict.
    """
    cmd = [
        "node", str(_BIRD_SEARCH_MJS),
@@ -170,109 +168,62 @@ def _invoke_bird_subprocess(query: str, count: int, timeout: int):
        "--json",
    ]
-    pid_holder: list[int] = []
+    # Use process groups for clean cleanup on timeout/kill
    preexec = os.setsid if hasattr(os, 'setsid') else None
-    def _register(pid: int) -> None:
+    try:
-        pid_holder.append(pid)
+        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
+            from last30days import register_child_pid, unregister_child_pid
-            register_child_pid(pid)
+            register_child_pid(proc.pid)
        except ImportError:
            pass
-    try:
+        try:
-        result = subproc.run_with_timeout(
+            stdout, stderr = proc.communicate(timeout=timeout)
-            cmd,
+        except subprocess.TimeoutExpired:
-            timeout=timeout,
+            # Kill the entire process group
-            env=_subprocess_env(),
+            try:
-            on_pid=_register,
+                os.killpg(os.getpgid(proc.pid), signal.SIGTERM)
-        )
+            except (ProcessLookupError, PermissionError, OSError):
-    except subproc.SubprocTimeout:
+                proc.kill()
-        return None, {"error": f"Search timed out after {timeout}s", "items": []}
+            proc.wait(timeout=5)
-    except Exception as e:
+            return {"error": f"Search timed out after {timeout}s", "items": []}
-        return None, {"error": str(e), "items": []}
+        finally:
    finally:
        if pid_holder:
            try:
                from last30days import unregister_child_pid
-                unregister_child_pid(pid_holder[0])
+                unregister_child_pid(proc.pid)
            except Exception:
                pass
-    return result, None
+        if proc.returncode != 0:
-
+            error = stderr.strip() if stderr else "Bird search failed"
 def _run_bird_search(query: str, count: int, timeout: int) -> Dict[str, Any]:
    """Run a search using the vendored bird-search.mjs module.
    Retries the subprocess on JSON-decode failure (typically a Twitter
    anti-bot HTML interstitial in stdout) up to MAX_JSON_DECODE_RETRIES
    times with JSON_DECODE_RETRY_DELAY seconds between attempts. Terminal
    errors (subprocess timeout, non-zero return code) are returned
    immediately without retry.
    Args:
        query: Full search query string (including since: filter)
        count: Number of results to request
        timeout: Timeout in seconds (per attempt)
    Returns:
        Raw Bird JSON response or error dict.
    """
    last_decode_error: Optional[str] = None
    for attempt in range(MAX_JSON_DECODE_RETRIES):
        result, terminal_error = _invoke_bird_subprocess(query, count, timeout)
        if terminal_error is not None:
            return terminal_error
        if result.returncode != 0:
            error = result.stderr.strip() or "Bird search failed"
            return {"error": error, "items": []}
-        output = result.stdout.strip()
+        output = stdout.strip() if stdout else ""
        if not output:
            return {"items": []}
-        try:
+        parsed = json.loads(output)
            parsed = json.loads(output)
        except json.JSONDecodeError as e:
            # Twitter's edge sometimes serves an HTML anti-bot interstitial
            # in place of JSON. Tag the failure shape so it's distinguishable
            # from "no results" in logs, then retry the subprocess.
            looks_html = output.lstrip().lower().startswith(("<!doctype", "<html", "<"))
            attempt_num = attempt + 1
            log_msg = (
                f"Bird search returned non-JSON stdout "
                f"(looks_html={looks_html}, attempt {attempt_num}/{MAX_JSON_DECODE_RETRIES}, "
                f"first 80 chars: {output[:80]!r})"
            )
            last_decode_error = str(e)
            if attempt_num < MAX_JSON_DECODE_RETRIES:
                log.source_log(
                    "X/bird",
                    f"{log_msg}; retrying in {JSON_DECODE_RETRY_DELAY:.0f}s",
                )
                time.sleep(JSON_DECODE_RETRY_DELAY)
                continue
            log.source_log("X/bird", log_msg)
            return {
                "error": (
                    f"Invalid JSON response after {MAX_JSON_DECODE_RETRIES} attempts "
                    f"(likely Twitter anti-bot interstitial): {e}"
                ),
                "items": [],
            }
        if isinstance(parsed, list):
            return {"items": parsed}
        return parsed
-    # Defensive fallthrough — loop should always return above.
+    except json.JSONDecodeError as e:
-    return {
+        return {"error": f"Invalid JSON response: {e}", "items": []}
-        "error": f"Bird search exhausted retries: {last_decode_error}",
+    except Exception as e:
-        "items": [],
+        return {"error": str(e), "items": []}
    }
 def search_x(
@@ -379,29 +330,47 @@ def search_handles(
            "--json",
        ]
-        try:
+        preexec = os.setsid if hasattr(os, 'setsid') else None
            result = subproc.run_with_timeout(cmd, timeout=15, env=_subprocess_env())
        except subproc.SubprocTimeout:
            _log(f"Handle search timed out for @{handle}")
            return []
        except OSError as e:
            _log(f"Handle search error for @{handle}: {e}")
            return []
        if result.returncode != 0:
            _log(f"Handle search failed for @{handle}: {result.stderr.strip()}")
            return []
        output = result.stdout.strip()
        if not output:
            return []
        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}")
-            return []
+        except (OSError, subprocess.SubprocessError) as e:
-        return parse_bird_response(response, query=core_topic)
+            _log(f"Handle search error for @{handle}: {e}")
        return []
    from concurrent.futures import ThreadPoolExecutor, as_completed
@@ -1,19 +1,10 @@
 """Bluesky search via AT Protocol (requires app password).
-Uses bsky.social for auth and api.bsky.app for post search (the canonical
+Uses bsky.social for auth and public.api.bsky.app for post search.
-authenticated AppView). The previous default `public.api.bsky.app` is the
+Requires BSKY_HANDLE and BSKY_APP_PASSWORD env vars.
 unauthenticated public mirror, which BunnyCDN now blocks for searchPosts
 regardless of auth header (verified 2026-05-04). Override the search host
 via BSKY_SEARCH_HOST env var if Bluesky migrates infrastructure again.
 Requires BSKY_HANDLE and BSKY_APP_PASSWORD env vars. App passwords are
 19-char xxxx-xxxx-xxxx-xxxx; generate at bsky.app/settings/app-passwords.
 The createSession endpoint accepts main-account passwords too, but they're
 bad hygiene (no scope, can't revoke individually).
 """
 import math
 import os
 import re
 import sys
 import time
@@ -23,64 +14,7 @@ from typing import Any, Dict, List, Optional
 from . import http, log
 BSKY_SESSION_URL = "https://bsky.social/xrpc/com.atproto.server.createSession"
-_DEFAULT_BSKY_SEARCH_HOST = "api.bsky.app"
+BSKY_SEARCH_URL = "https://public.api.bsky.app/xrpc/app.bsky.feed.searchPosts"
 def _resolve_search_url(config: Optional[Dict[str, Any]] = None) -> str:
    """Resolve the Bluesky search URL with BSKY_SEARCH_HOST override.
    Default is api.bsky.app. Override via BSKY_SEARCH_HOST in shell env or
    .env file. The project's env.py loads .env into config but not into
    os.environ, so check both — same hybrid pattern as last30days.py for
    LAST30DAYS_STORE.
    Hardens user-supplied host values against three common mis-configurations:
    whitespace (e.g. " api.bsky.app "), embedded path components (e.g.
    "api.bsky.app/xrpc/proxy") that would double the /xrpc/ segment, and
    embedded scheme prefixes (e.g. "https://api.bsky.app"). On any of these
    we log a warning and fall back to the default rather than building an
    invalid URL with an opaque downstream error.
    """
    config = config or {}
    raw = (
        os.environ.get("BSKY_SEARCH_HOST")
        or config.get("BSKY_SEARCH_HOST")
        or _DEFAULT_BSKY_SEARCH_HOST
    )
    host = raw.strip().rstrip("/")
    # Strip embedded scheme so users who paste full URLs do not break the f-string.
    for prefix in ("https://", "http://"):
        if host.lower().startswith(prefix):
            host = host[len(prefix):]
            break
    if not host or "/" in host or " " in host:
        # Embedded path or whitespace remains — don't trust it. Default + log.
        if raw != _DEFAULT_BSKY_SEARCH_HOST:
            _log(
                f"BSKY_SEARCH_HOST={raw!r} is not a bare hostname; "
                f"falling back to default {_DEFAULT_BSKY_SEARCH_HOST!r}"
            )
        host = _DEFAULT_BSKY_SEARCH_HOST
    return f"https://{host}/xrpc/app.bsky.feed.searchPosts"
 # App-password format: xxxx-xxxx-xxxx-xxxx (19 chars, lowercase alphanumeric
 # with three hyphens at fixed positions).
 _APP_PASSWORD_RE = re.compile(r"^[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}$")
 def _validate_app_password_format(value) -> bool:
    """Return True if value matches Bluesky's 19-char app-password format.
    False for non-strings (None, int, list) so callers passing config dict
    values directly don't crash. Detect-but-not-gate: the createSession
    endpoint also accepts main-account passwords, so failing this check is
    a hygiene smell, not a hard error.
    """
    if not isinstance(value, str):
        return False
    return bool(_APP_PASSWORD_RE.fullmatch(value))
 DEPTH_CONFIG = {
    "quick": 15,
@@ -210,20 +144,6 @@ def search_bluesky(
    if not handle or not app_password:
        return {"posts": [], "error": "Bluesky credentials not configured"}
    # One-shot hygiene warning if BSKY_APP_PASSWORD is not in app-password
    # form. createSession accepts main-account passwords too — but main
    # passwords have no scope (full account access), can't be revoked
    # individually, and rotating them breaks every service that holds them.
    # We warn but do not gate, matching the project's detect-don't-block
    # philosophy elsewhere.
    if not _validate_app_password_format(app_password):
        _log(
            "BSKY_APP_PASSWORD does not look like an app password "
            "(expected xxxx-xxxx-xxxx-xxxx, 19 chars). It may be a main "
            "account password — those work but are bad hygiene. Generate "
            "an app password at https://bsky.app/settings/app-passwords"
        )
    count = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
    core_topic = _extract_core_subject(topic)
@@ -235,7 +155,7 @@ def search_bluesky(
        "limit": str(min(count, 100)),
        "sort": "top",
    }
-    url = f"{_resolve_search_url(config)}?{urlencode(params)}"
+    url = f"{BSKY_SEARCH_URL}?{urlencode(params)}"
    def _auth_and_search() -> tuple[Optional[Dict[str, Any]], Optional[str]]:
        token = _create_session(handle, app_password)
@@ -1,12 +1,9 @@
-"""Chrome and Brave cookie extraction for macOS.
+"""Chrome cookie extraction for macOS.
-Extracts cookies from Chromium-based browser SQLite databases using only
+Extracts cookies from Chrome's encrypted SQLite database using only stdlib
-stdlib modules and the system openssl CLI (ships with macOS). Zero pip
+modules and the system openssl CLI (ships with macOS). Zero pip dependencies.
 dependencies.
-Chromium on macOS uses v10 encryption (AES-128-CBC with Keychain-stored key).
+Chrome on macOS uses v10 encryption (AES-128-CBC with Keychain-stored key).
 Chrome and Brave share the same algorithm; only the DB path and Keychain
 service name differ.
 This is NOT affected by Windows App-Bound Encryption (v20).
 """
@@ -21,11 +18,10 @@ from typing import Optional
 logger = logging.getLogger(__name__)
-# Cookie DB locations on macOS
+# Chrome cookie DB location on macOS
 CHROME_COOKIES_DB = Path.home() / "Library" / "Application Support" / "Google" / "Chrome" / "Default" / "Cookies"
 BRAVE_BASE_DIR = Path.home() / "Library" / "Application Support" / "BraveSoftware" / "Brave-Browser"
-# Chromium v10 encryption constants (shared by Chrome and Brave)
+# Chrome v10 encryption constants
 CHROME_SALT = b"saltysalt"
 CHROME_PBKDF2_ITERATIONS = 1003
 CHROME_KEY_LENGTH = 16
@@ -33,8 +29,8 @@ CHROME_KEY_LENGTH = 16
 CHROME_IV_HEX = "20" * 16
-def _get_chromium_encryption_key(service_name: str) -> Optional[bytes]:
+def _get_chrome_encryption_key() -> Optional[bytes]:
-    """Retrieve the encryption passphrase for a Chromium-based browser from macOS Keychain.
+    """Retrieve Chrome's encryption passphrase from macOS Keychain.
    Calls `security find-generic-password` which may trigger a system dialog
    on first access.
@@ -43,34 +39,30 @@ def _get_chromium_encryption_key(service_name: str) -> Optional[bytes]:
    """
    try:
        result = subprocess.run(
-            ["security", "find-generic-password", "-w", "-s", service_name],
+            ["security", "find-generic-password", "-w", "-s", "Chrome Safe Storage"],
            capture_output=True,
            text=True,
            timeout=10,
        )
        if result.returncode != 0:
-            logger.info("%s Keychain access denied or browser not installed: %s", service_name, result.stderr.strip())
+            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("%s Keychain returned empty passphrase", service_name)
+            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("%s Keychain access timed out", service_name)
+        logger.info("Chrome Keychain access timed out")
        return None
    except Exception as e:
-        logger.info("Failed to get %s encryption key: %s", service_name, e)
+        logger.info("Failed to get Chrome encryption key: %s", e)
        return None
 def _get_chrome_encryption_key() -> Optional[bytes]:
    return _get_chromium_encryption_key("Chrome Safe Storage")
 def _derive_aes_key(passphrase: bytes) -> bytes:
    """Derive 16-byte AES key from Chrome's Keychain passphrase via PBKDF2."""
    return hashlib.pbkdf2_hmac(
@@ -173,42 +165,36 @@ def _get_db_version(cursor: sqlite3.Cursor) -> int:
    return 0
-def _extract_chromium_cookies_macos(
+def extract_chrome_cookies_macos(domain: str, cookie_names: list[str]) -> Optional[dict[str, str]]:
-    db_path: Path,
+    """Extract cookies from Chrome on macOS.
    keychain_service: str,
    domain: str,
    cookie_names: list[str],
 ) -> Optional[dict[str, str]]:
    """Extract cookies from any Chromium-based browser 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:
-        db_path: Path to the browser's Cookies SQLite file.
+        domain: Cookie domain to match (e.g., ".twitter.com", ".x.com")
-        keychain_service: macOS Keychain service name (e.g. "Chrome Safe Storage").
+        cookie_names: List of cookie names to extract
        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 db_path.exists():
+    if not CHROME_COOKIES_DB.exists():
-        logger.info("%s cookies database not found at %s", keychain_service, db_path)
+        logger.info("Chrome cookies database not found at %s", CHROME_COOKIES_DB)
        return None
-    passphrase = _get_chromium_encryption_key(keychain_service)
+    # 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 (browser locks the original while running)
+    # 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(db_path), tmp_path)
+        shutil.copy2(str(CHROME_COOKIES_DB), tmp_path)
    except Exception as e:
-        logger.info("Failed to copy %s cookies database: %s", keychain_service, e)
+        logger.info("Failed to copy Chrome cookies database: %s", e)
        if tmp_path:
            try:
                Path(tmp_path).unlink(missing_ok=True)
@@ -225,22 +211,26 @@ def _extract_chromium_cookies_macos(
        cursor = conn.cursor()
        db_version = _get_db_version(cursor)
-        logger.debug("%s cookie DB version: %d", keychain_service, db_version)
+        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)
@@ -251,72 +241,25 @@ def _extract_chromium_cookies_macos(
                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 %s for domain %s", keychain_service, domain)
+            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 %s cookies database: %s", keychain_service, e)
+        logger.info("Failed to read Chrome cookies database: %s", e)
        return None
    except Exception as e:
-        logger.info("Unexpected error reading %s cookies: %s", keychain_service, e)
+        logger.info("Unexpected error reading Chrome cookies: %s", e)
        return None
    finally:
        try:
            Path(tmp_path).unlink(missing_ok=True)
        except Exception:
            pass
 def extract_chrome_cookies_macos(domain: str, cookie_names: list[str]) -> Optional[dict[str, str]]:
    """Extract cookies from Chrome on macOS."""
    return _extract_chromium_cookies_macos(
        CHROME_COOKIES_DB, "Chrome Safe Storage", domain, cookie_names
    )
 def _find_brave_cookies_db() -> Optional[Path]:
    """Find Brave's Cookies database on macOS.
    Tries the Default profile first, then scans numbered Profile directories
    by most-recently-modified. Brave creates extra profiles as "Profile 1",
    "Profile 2", etc. alongside Default; the most recently used one is the
    likeliest to hold current cookies. Lexicographic sort would visit
    "Profile 10" before "Profile 2", which can return the wrong profile.
    """
    default = BRAVE_BASE_DIR / "Default" / "Cookies"
    if default.exists():
        return default
    try:
        candidates = [
            child for child in BRAVE_BASE_DIR.iterdir()
            if child.is_dir() and child.name.startswith("Profile ")
        ]
        for child in sorted(candidates, key=lambda p: p.stat().st_mtime, reverse=True):
            candidate = child / "Cookies"
            if candidate.exists():
                return candidate
    except OSError:
        pass
    return None
 def extract_brave_cookies_macos(domain: str, cookie_names: list[str]) -> Optional[dict[str, str]]:
    """Extract cookies from Brave on macOS.
    Brave uses the same v10 AES-128-CBC encryption as Chrome; only the DB
    path and Keychain service name differ.
    """
    db_path = _find_brave_cookies_db()
    if db_path is None:
        logger.info("Brave cookies database not found under %s", BRAVE_BASE_DIR)
        return None
    return _extract_chromium_cookies_macos(db_path, "Brave Safe Storage", domain, cookie_names)
@@ -1,6 +1,6 @@
 """Browser cookie extraction for last30days.
-Extracts cookies from local browser databases (Firefox, Chrome, Brave, Safari)
+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.
@@ -255,29 +255,6 @@ def extract_chrome_cookies(
        return None
 def extract_brave_cookies(
    domain: str, cookie_names: List[str]
 ) -> Optional[Dict[str, str]]:
    """Extract cookies from Brave for the given domain and cookie names.
    macOS only — Brave uses the same v10 AES-128-CBC encryption as Chrome,
    with a different DB path and Keychain service name ("Brave Safe Storage").
    Tries the Default profile first, then scans numbered Profile directories.
    Returns:
        Dict of {cookie_name: cookie_value} or None if extraction fails.
    """
    if platform.system() != "Darwin":
        logger.debug("Brave cookie extraction only supported on macOS")
        return None
    try:
        from .chrome_cookies import extract_brave_cookies_macos
        return extract_brave_cookies_macos(domain, cookie_names)
    except Exception as exc:
        logger.debug("Brave cookie extraction failed: %s", exc)
        return None
 def extract_safari_cookies(
    domain: str, cookie_names: List[str]
 ) -> Optional[Dict[str, str]]:
@@ -305,9 +282,9 @@ def extract_cookies(
    """Extract cookies from the specified browser.
    Args:
-        browser: One of 'firefox', 'chrome', 'brave', 'safari', or 'auto'.
+        browser: One of 'firefox', 'chrome', 'safari', or 'auto'.
            'auto' tries browsers in platform-appropriate order:
-            - macOS: Chrome -> Brave -> Firefox -> Safari
+            - 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.
@@ -356,7 +333,7 @@ def extract_cookies_with_source(
    so callers can track the source.
    Args:
-        browser: One of 'firefox', 'chrome', 'brave', 'safari', or 'auto'.
+        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.
@@ -367,7 +344,6 @@ def extract_cookies_with_source(
    extractors = {
        "firefox": extract_firefox_cookies,
        "chrome": extract_chrome_cookies,
        "brave": extract_brave_cookies,
        "safari": extract_safari_cookies,
    }
@@ -384,7 +360,7 @@ def extract_cookies_with_source(
    # Auto mode: try browsers in platform-appropriate order
    system = platform.system()
    if system == "Darwin":
-        order = ["chrome", "brave", "firefox", "safari"]
+        order = ["chrome", "firefox", "safari"]
    elif system == "Linux":
        order = ["firefox"]
    else:
@@ -39,14 +39,11 @@ def normalize_text(text: str) -> str:
    return re.sub(r"\s+", " ", text).strip()
 def _ngrams_of_normalized(norm: str, n: int = 3) -> set[str]:
    if len(norm) < n:
        return {norm} if norm else set()
    return {norm[index:index + n] for index in range(len(norm) - n + 1)}
 def get_ngrams(text: str, n: int = 3) -> set[str]:
-    return _ngrams_of_normalized(normalize_text(text), n)
+    text = normalize_text(text)
    if len(text) < n:
        return {text} if text else set()
    return {text[index:index + n] for index in range(len(text) - n + 1)}
 def jaccard_similarity(left: set[str], right: set[str]) -> float:
@@ -93,7 +90,7 @@ class _PreparedText:
    def __init__(self, raw: str) -> None:
        norm = normalize_text(raw)
-        self.ngrams = _ngrams_of_normalized(norm)
+        self.ngrams = get_ngrams(norm) if norm else set()
        self.tokens = _tokenize(norm)
@@ -106,7 +106,7 @@ def _extract_subreddits(reddit_items: List[Dict[str, Any]]) -> List[str]:
    for item in reddit_items:
        # Primary subreddit
-        sub = item.get("subreddit", "").strip().removeprefix("r/")
+        sub = item.get("subreddit", "").strip().lstrip("r/")
        if sub:
            sub_counts[sub] += 1
@@ -29,23 +29,6 @@ else:
 CODEX_AUTH_FILE = Path(os.environ.get("CODEX_AUTH_FILE", str(Path.home() / ".codex" / "auth.json")))
 # macOS Keychain integration: items stored with this service prefix are picked
 # up automatically on Darwin as the lowest-priority credential source.
 # Example: `security add-generic-password -a "$USER" -s last30days-XAI_API_KEY -w "xai-..."`.
 KEYCHAIN_SERVICE_PREFIX = "last30days-"
 # Single source of truth for which credentials the Keychain loader looks up.
 # The setup-keychain.sh helper mirrors this list and is held in sync via
 # tests/test_env_keychain.py::test_keychain_keys_match_setup_script.
 KEYCHAIN_KEYS = (
    "OPENAI_API_KEY", "XAI_API_KEY", "GOOGLE_API_KEY", "GEMINI_API_KEY",
    "GOOGLE_GENAI_API_KEY", "SCRAPECREATORS_API_KEY", "APIFY_API_TOKEN",
    "AUTH_TOKEN", "CT0", "BSKY_HANDLE", "BSKY_APP_PASSWORD",
    "TRUTHSOCIAL_TOKEN", "BRAVE_API_KEY", "EXA_API_KEY", "SERPER_API_KEY",
    "OPENROUTER_API_KEY", "PARALLEL_API_KEY", "XQUIK_API_KEY",
    "XIAOHONGSHU_API_BASE",
 )
 AuthSource = Literal["api_key", "codex", "none"]
 AuthStatus = Literal["ok", "missing", "expired", "missing_account_id"]
@@ -70,10 +53,6 @@ class OpenAIAuth:
 def _check_file_permissions(path: Path) -> None:
    """Warn to stderr if a secrets file has overly permissive permissions."""
    if os.name == "nt":
        # Windows reports synthesized POSIX mode bits that do not reflect NTFS ACLs.
        return
    try:
        mode = path.stat().st_mode
        # Check if group or other can read (bits 0o044)
@@ -112,46 +91,6 @@ def load_env_file(path: Path) -> dict[str, str]:
    return env
 def _load_keychain(keys: list[str]) -> dict[str, str]:
    """Load credentials from macOS Keychain (no-op on other platforms).
    Each key is looked up as a generic password with service name
    ``f"{KEYCHAIN_SERVICE_PREFIX}{key}"`` for the current user. Missing items
    and lookup failures are silent — Keychain is the lowest-priority source
    and is meant to be additive over `.env` files and process environment.
    """
    import platform
    if platform.system() != "Darwin":
        return {}
    import shutil
    security = shutil.which("security")
    if not security:
        return {}
    import subprocess
    import pwd
    # USER can be unset under sudo, in Docker without --env USER, or in some CI
    # runners; fall back to the OS user record so lookups still match items
    # stored by setup-keychain.sh (which uses $USER).
    user = os.environ.get("USER") or pwd.getpwuid(os.getuid()).pw_name
    env: dict[str, str] = {}
    for key in keys:
        try:
            result = subprocess.run(
                [security, "find-generic-password",
                 "-a", user,
                 "-s", f"{KEYCHAIN_SERVICE_PREFIX}{key}",
                 "-w"],
                capture_output=True, text=True, timeout=5,
            )
        except (subprocess.TimeoutExpired, OSError):
            continue
        if result.returncode == 0 and result.stdout.strip():
            env[key] = result.stdout.strip()
    return env
 def _decode_jwt_payload(token: str) -> dict[str, Any] | None:
    """Decode JWT payload without verification."""
    try:
@@ -275,7 +214,6 @@ def get_config() -> dict[str, Any]:
      1. Environment variables (os.environ)
      2. .claude/last30days.env (per-project config)
      3. ~/.config/last30days/.env (global config)
      4. macOS Keychain items prefixed ``last30days-`` (Darwin only)
    """
    # Load from global config file
    file_env = load_env_file(CONFIG_FILE) if CONFIG_FILE else {}
@@ -284,14 +222,9 @@ def get_config() -> dict[str, Any]:
    project_env_path = _find_project_env()
    project_env = load_env_file(project_env_path) if project_env_path else {}
-    # Merge file sources: project > global
+    # Merge: project overrides global
    merged_env = {**file_env, **project_env}
    # Keychain is the lowest-priority source (Darwin only; no-op elsewhere).
    # Loaded before openai_auth so OPENAI_API_KEY can come from Keychain too.
    keychain_env = _load_keychain(list(KEYCHAIN_KEYS))
    merged_env = {**keychain_env, **merged_env}
    openai_auth = get_openai_auth(merged_env)
    # Build config: Codex/OpenAI auth + process.env > project .env > global .env
@@ -314,7 +247,6 @@ def get_config() -> dict[str, Any]:
        ('LAST30DAYS_RERANK_MODEL', None),
        ('LAST30DAYS_X_MODEL', None),
        ('LAST30DAYS_X_BACKEND', None),
        ('LAST30DAYS_STORE', None),
        ('OPENAI_MODEL_PIN', None),
        ('XAI_MODEL_PIN', None),
        ('SCRAPECREATORS_API_KEY', None),
@@ -323,7 +255,6 @@ def get_config() -> dict[str, Any]:
        ('CT0', None),
        ('BSKY_HANDLE', None),
        ('BSKY_APP_PASSWORD', None),
        ('BSKY_SEARCH_HOST', None),
        ('TRUTHSOCIAL_TOKEN', None),
        ('BRAVE_API_KEY', None),
        ('EXA_API_KEY', None),
@@ -334,41 +265,16 @@ def get_config() -> dict[str, Any]:
        ('FROM_BROWSER', None),
        ('SETUP_COMPLETE', None),
        ('INCLUDE_SOURCES', ''),
        ('EXCLUDE_SOURCES', ''),
        ('LAST30DAYS_YOUTUBE_SSH_HOST', None),
        ('LAST30DAYS_TRANSCRIPT_TIMEOUT', None),
    ]
    for key, default in keys:
        config[key] = os.environ.get(key) or merged_env.get(key, default)
-    # Backward-compat: ScrapeCreators' own examples and tutorials use the
+    # Track which config source was used
    # SCRAPE_CREATORS_API_KEY spelling (with underscore between SCRAPE and
    # CREATORS). Accept that form too so users who follow the vendor's docs
    # don't silently end up with has_scrapecreators=False. Canonical name
    # wins when both are set.
    if not config.get('SCRAPECREATORS_API_KEY'):
        legacy = os.environ.get('SCRAPE_CREATORS_API_KEY') or merged_env.get('SCRAPE_CREATORS_API_KEY')
        if legacy:
            config['SCRAPECREATORS_API_KEY'] = legacy
    # Multi-key rotation: comma-separated SCRAPECREATORS_API_KEY round-robins
    # via random.choice per run. Originally added in #268, accidentally dropped
    # in v3.0.6, restored here.
    sc_key_raw = config.get('SCRAPECREATORS_API_KEY') or ''
    if ',' in sc_key_raw:
        import random
        sc_keys = [k.strip() for k in sc_key_raw.split(',') if k.strip()]
        config['SCRAPECREATORS_API_KEY'] = random.choice(sc_keys) if sc_keys else ''
    # Track which config source was used (highest-priority file source wins
    # the label; keychain is only reported when nothing else is configured).
    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}'
    elif keychain_env:
        config['_CONFIG_SOURCE'] = 'keychain'
    else:
        config['_CONFIG_SOURCE'] = 'env_only'
@@ -466,6 +372,14 @@ def config_exists() -> bool:
    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.
@@ -611,12 +525,12 @@ def _parse_include_sources(config: dict[str, Any]) -> set[str]:
 def is_threads_available(config: dict[str, Any]) -> bool:
    """Check if Threads source is available.
-    Returns True when SCRAPECREATORS_API_KEY is set. Threads runs alongside
+    Requires SCRAPECREATORS_API_KEY AND 'threads' in INCLUDE_SOURCES.
-    TikTok and Instagram as part of the SC family — same key, same per-call
+    Threads is an opt-in source - it is not activated by default.
    cost shape, so the same default-on rule applies. Suppress via
    EXCLUDE_SOURCES=threads.
    """
-    return bool(config.get('SCRAPECREATORS_API_KEY'))
+    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:
@@ -116,8 +116,6 @@ def weighted_rrf(
    """Fuse ranked lists into a single candidate pool."""
    subqueries = {subquery.label: subquery for subquery in plan.subqueries}
    candidates: dict[str, schema.Candidate] = {}
    # Track (source, item_id) pairs already attached to each candidate for O(1) dedup.
    seen_source_items: dict[str, set[tuple[str, str]]] = {}
    for (label, source), items in streams.items():
        subquery = subqueries[label]
@@ -156,7 +154,6 @@ def weighted_rrf(
                        ]
                    },
                )
                seen_source_items[key] = {(item.source, item.item_id)}
                continue
            candidate = candidates[key]
@@ -182,9 +179,7 @@ def weighted_rrf(
                candidate.subquery_labels.append(label)
            if item.source not in candidate.sources:
                candidate.sources.append(item.source)
-            source_item_key = (item.source, item.item_id)
+            if not any(existing.source == item.source and existing.item_id == item.item_id for existing in candidate.source_items):
            if source_item_key not in seen_source_items[key]:
                seen_source_items[key].add(source_item_key)
                candidate.source_items.append(item)
            candidate.metadata.setdefault("provenance", []).append(
                {
@@ -2,7 +2,6 @@
 from __future__ import annotations
 import sys
 import urllib.parse
 from datetime import datetime
 from urllib.parse import urlparse
@@ -140,10 +139,7 @@ def parallel_search(
    data = http.request(
        "POST", "https://api.parallel.ai/v1/search",
        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-        json_data={
+        json_data={"query": query, "max_results": count},
            "search_queries": [query],
            "advanced_settings": {"max_results": count},
        },
        timeout=15,
    )
    items = []
@@ -153,7 +149,7 @@ def parallel_search(
        url = r.get("url", "")
        if not url:
            continue
-        raw_date = r.get("publish_date") or ""
+        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
@@ -162,7 +158,7 @@ def parallel_search(
            "title": r.get("title", ""),
            "url": url,
            "source_domain": _domain(url),
-            "snippet": ((r.get("excerpts") or [""])[0] or "")[:500],
+            "snippet": r.get("snippet", ""),
            "date": pub_date,
            "relevance": 0.8,
            "why_relevant": "Parallel AI web search",
@@ -209,90 +205,29 @@ def web_search(
            backend = "parallel"
        else:
            return [], {}
    items: list[dict] = []
    artifact: dict = {}
    if backend == "brave":
        key = config.get("BRAVE_API_KEY")
        if not key:
            raise RuntimeError("BRAVE_API_KEY is required when web_backend='brave'")
-        items, artifact = brave_search(query, date_range, key)
+        return brave_search(query, date_range, key)
-    elif backend == "exa":
+    if backend == "exa":
        key = config.get("EXA_API_KEY")
        if not key:
            raise RuntimeError("EXA_API_KEY is required when web_backend='exa'")
-        items, artifact = exa_search(query, date_range, key)
+        return exa_search(query, date_range, key)
-    elif backend == "serper":
+    if backend == "serper":
        key = config.get("SERPER_API_KEY")
        if not key:
            raise RuntimeError("SERPER_API_KEY is required when web_backend='serper'")
-        items, artifact = serper_search(query, date_range, key)
+        return serper_search(query, date_range, key)
-    elif backend == "parallel":
+    if backend == "parallel":
        key = config.get("PARALLEL_API_KEY")
        if not key:
            raise RuntimeError("PARALLEL_API_KEY is required when web_backend='parallel'")
-        items, artifact = parallel_search(query, date_range, key)
+        return parallel_search(query, date_range, key)
-    elif backend != "none":
+    if backend != "none":
        raise ValueError(f"Unsupported web backend: {backend!r}")
-    else:
+    return [], {}
        return [], {}
    if items and not _reddit_excluded(config):
        items = _enrich_reddit_items(items)
    return items, artifact
 def _reddit_excluded(config: dict) -> bool:
    """Return True when EXCLUDE_SOURCES contains 'reddit'.
    Respects the same suppression knob the pipeline uses for source gating,
    so a user who set EXCLUDE_SOURCES=reddit doesn't get Reddit content
    smuggled back in via web-search URLs.
    """
    raw = (config.get("EXCLUDE_SOURCES") or "").split(",")
    return any(s.strip().lower() == "reddit" for s in raw)
 def _enrich_reddit_items(items: list[dict]) -> list[dict]:
    """Enrich web search results that are Reddit URLs with thread body and comments.
    Claude Code's WebFetch blocks reddit.com, so the model can't retrieve
    Reddit content from web search results. This fetches it via the public
    JSON API (reddit.com/.../.json) which bypasses that restriction.
    Callers should gate this with EXCLUDE_SOURCES=reddit handling (see
    `_reddit_excluded`) so a user who explicitly excluded Reddit doesn't
    get Reddit content via web-search URLs.
    """
    from . import reddit_enrich
    from .reddit_enrich import RedditRateLimitError
    for item in items:
        url = item.get("url", "")
        if "reddit.com" not in url or "/comments/" not in url:
            continue
        try:
            thread_data = reddit_enrich.fetch_thread_data(url, timeout=8)
            if not thread_data:
                continue
            parsed = reddit_enrich.parse_thread_data(thread_data)
            # selftext lives under parsed["submission"], not at the top level
            selftext = (parsed.get("submission") or {}).get("selftext", "")
            if selftext:
                item["snippet"] = selftext[:2000]
            comments = parsed.get("comments", [])
            top = reddit_enrich.get_top_comments(comments)
            if top:
                item["top_comments"] = [
                    {"score": c.get("score", 0), "excerpt": (c.get("body") or "")[:200]}
                    for c in top[:5]
                ]
            item["enriched_via"] = "reddit_json_api"
        except RedditRateLimitError as exc:
            # Stop iterating to avoid flooding more 429s
            sys.stderr.write(f"[Web] Reddit rate-limited, halting enrichment: {exc}\n")
            break
        except Exception as exc:
            sys.stderr.write(f"[Web] Reddit enrichment failed for {url}: {exc}\n")
    return items
 # ---------------------------------------------------------------------------
@@ -88,26 +88,17 @@ def search_hackernews(
    # Use extracted core subject instead of raw topic for cleaner Algolia matching
    core = extract_core_subject(topic)
-    # Hyphens and commas tokenize awkwardly in Algolia; flatten them so themed
+    _log(f"Searching for '{core}' (raw: '{topic}', since {from_date}, count={count})")
    # queries like "ts-bun-node" or "claude, personal agents" become plain words.
    core_flat = _flatten_query_for_algolia(core)
    _log(f"Searching for '{core_flat}' (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_flat,
+        "query": core,
        "tags": "story",
        "numericFilters": f"created_at_i>{from_ts},created_at_i<{to_ts},points>2",
        "hitsPerPage": str(count),
    }
    # Algolia defaults to AND across query tokens, so a 4-5 word theme query
    # matches no stories. Mark all-but-the-first token as optional so Algolia
    # ranks by how many tokens match instead of requiring every one.
    tokens = core_flat.split()
    if len(tokens) > 1:
        params["optionalWords"] = " ".join(tokens[1:])
    from urllib.parse import urlencode
    url = f"{ALGOLIA_SEARCH_URL}?{urlencode(params)}"
@@ -126,56 +117,28 @@ def search_hackernews(
    return response
 _WORD_BOUNDARY_RE_CACHE: Dict[str, "re.Pattern[str]"] = {}
 def _flatten_query_for_algolia(text: str) -> str:
    """Normalise query for Algolia + post-filter comparison.
    Multi-keyword theme queries frequently contain commas (delimiters) or
    hyphens (compound terms like ``ts-bun-node``); both tokenize awkwardly.
    Flatten them to spaces and collapse runs of whitespace so the search
    parameter and the post-filter operate on the same shape.
    """
    return " ".join(text.replace(",", " ").replace("-", " ").split())
 def _title_matches_query(title: str, query: str, author: str = "") -> bool:
-    """Check if any query token appears as a whole word in the title.
+    """Check if the query term appears in the title content, not just an HN prefix or author.
-    Returns True when the query is empty (no filter), or when at least one
+    Returns True if the query (or any multi-word token) appears in the title
-    query token matches as a whole word in the title after stripping
+    after stripping "Tell HN:", "Show HN:", "Ask HN:", "Launch HN:" prefixes
-    "Tell HN:", "Show HN:", "Ask HN:", "Launch HN:" prefixes.
+    and ignoring the author name.  Returns True when query is empty (no filter).
    We previously required *every* token to appear (all-words), which killed
    every Algolia hit on multi-keyword themes like "claude, personal agents,
    agentic infra" because real HN titles never contain all five tokens
    verbatim. Relaxing to any-word matches Algolia's `optionalWords` behaviour
    in `search_hackernews`. Token-overlap relevance scoring at parse time
    demotes hits where only one weak token matched, so the loosened gate
    won't surface noise to the top of the ranking.
    Word-boundary matching (rather than naive substring) prevents short
    tokens like ``ai`` or ``ts`` from matching unrelated words like
    ``email`` or ``artists``.
    """
    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()
-    # Normalise the query the same way search_hackernews does so post-filter
+    query_lower = query.lower()
-    # tokens line up with what Algolia actually saw.
+    # Check each word of the query independently; all must appear somewhere
-    query_words = [w for w in _flatten_query_for_algolia(query.lower()).split() if w]
+    # in the stripped title (not just the prefix).
-    if not query_words:
+    query_words = query_lower.split()
        return True
    for word in query_words:
-        pattern = _WORD_BOUNDARY_RE_CACHE.get(word)
+        if word in check_text:
-        if pattern is None:
+            continue
-            pattern = re.compile(rf"\b{re.escape(word)}\b")
+        # Word not found in stripped title — reject
-            _WORD_BOUNDARY_RE_CACHE[word] = pattern
+        return False
-        if pattern.search(check_text):
+    return True
            return True
    return False
 def parse_hackernews_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
@@ -2,7 +2,6 @@
 import json
 import re
 import socket
 import sys
 import time
 import urllib.error
@@ -23,19 +22,9 @@ def log(msg: str):
 MAX_RETRIES = 5
 MAX_429_RETRIES = 2
 RETRY_DELAY = 2.0
 # DNS resolution failures (gaierror) are transient — typically resolved by a
 # brief backoff and retry. Use a dedicated minimum attempt count + exponential
 # delays (1s, 2s, 4s) so callers that pass a small `retries` value still get a
 # meaningful chance to recover from a transient resolution failure.
 MIN_DNS_RETRIES = 3
 USER_AGENT = "last30days-skill/3.0 (Assistant Skill)"
 def _is_dns_failure(err: urllib.error.URLError) -> bool:
    """Return True if a URLError was caused by DNS resolution (gaierror)."""
    return isinstance(getattr(err, "reason", None), socket.gaierror)
 class HTTPError(Exception):
    """HTTP request error with status code."""
    def __init__(self, message: str, status_code: Optional[int] = None, body: Optional[str] = None):
@@ -96,13 +85,7 @@ def request(
    last_error = None
    rate_limit_count = 0
-    # DNS failures get a dedicated minimum attempt count + exponential backoff.
+    for attempt in range(retries):
    # `effective_retries` is the actual loop bound; we expand it on the first
    # gaierror if the caller passed a smaller `retries` value than MIN_DNS_RETRIES.
    effective_retries = retries
    dns_attempts = 0
    attempt = 0
    while attempt < effective_retries:
        try:
            with urllib.request.urlopen(req, timeout=timeout) as response:
                body = response.read().decode('utf-8')
@@ -132,8 +115,6 @@ def request(
                if rate_limit_count >= max_429_retries:
                    raise last_error
            # HTTP errors respect the caller's original `retries`; only DNS
            # failures get the widened `effective_retries` budget.
            if attempt < retries - 1:
                if e.code == 429:
                    # Respect Retry-After header, fall back to exponential backoff
@@ -149,43 +130,11 @@ def request(
                else:
                    delay = RETRY_DELAY * (2 ** attempt)
                time.sleep(delay)
            else:
                # Caller's original retry budget exhausted; an earlier DNS
                # failure may have widened `effective_retries`, but that
                # widening is DNS-only — don't grant extra HTTP attempts.
                break
        except urllib.error.URLError as e:
            log(f"URL Error: {e.reason}")
            last_error = HTTPError(f"URL Error: {e.reason}")
-            if _is_dns_failure(e):
+            if attempt < retries - 1:
                # DNS resolution failures are transient; expand the retry budget
                # to MIN_DNS_RETRIES if the caller passed fewer, and use
                # exponential backoff (1s, 2s, 4s, ...) instead of the linear
                # default. Counts DNS attempts separately so other URLError
                # causes don't bypass the regular retry budget.
                dns_attempts += 1
                if effective_retries < MIN_DNS_RETRIES:
                    log(
                        f"DNS resolution failed; expanding retry budget from "
                        f"{effective_retries} to {MIN_DNS_RETRIES}"
                    )
                    effective_retries = MIN_DNS_RETRIES
                if attempt < effective_retries - 1:
                    delay = 2 ** (dns_attempts - 1)  # 1s, 2s, 4s, 8s, ...
                    log(
                        f"DNS resolution failure (attempt {dns_attempts}); "
                        f"retrying in {delay:.1f}s"
                    )
                    time.sleep(delay)
            elif attempt < retries - 1:
                # Non-DNS URLError (e.g. ConnectionRefused) respects the
                # caller's original retry budget, not the DNS-widened bound.
                time.sleep(RETRY_DELAY * (attempt + 1))
            else:
                # Caller's original retry budget exhausted; an earlier DNS
                # failure widening `effective_retries` does not carry over
                # to non-DNS error paths.
                break
        except json.JSONDecodeError as e:
            log(f"JSON decode error: {e}")
            last_error = HTTPError(f"Invalid JSON response: {e}")
@@ -195,13 +144,7 @@ def request(
            log(f"Connection error: {type(e).__name__}: {e}")
            last_error = HTTPError(f"Connection error: {type(e).__name__}: {e}")
            if attempt < retries - 1:
                # Socket errors respect the caller's original retry budget.
                time.sleep(RETRY_DELAY * (attempt + 1))
            else:
                # Original budget exhausted; DNS widening doesn't apply here.
                break
        attempt += 1
    if last_error:
        raise last_error
@@ -7,14 +7,17 @@ Requires SCRAPECREATORS_API_KEY in config. 100 free API calls, then PAYG.
 API docs: https://scrapecreators.com/docs
 """
 import os
 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
 from .relevance import token_overlap_relevance as _compute_relevance
 SCRAPECREATORS_BASE = "https://api.scrapecreators.com"
@@ -28,42 +31,7 @@ DEPTH_CONFIG = {
 # Max words to keep from each caption
 CAPTION_MAX_WORDS = 500
-# Default transcript fetch timeout (seconds). SC's
+from .relevance import token_overlap_relevance as _compute_relevance
 # /v2/instagram/media/transcript regularly takes >15s on real workloads,
 # so the default is generous; override via LAST30DAYS_TRANSCRIPT_TIMEOUT.
 DEFAULT_TRANSCRIPT_TIMEOUT = 30
 def _resolve_transcript_timeout(
    timeout: Optional[float] = None,
    config: Optional[Dict[str, Any]] = None,
 ) -> float:
    """Resolve the IG transcript-fetch timeout.
    Priority (highest wins):
      1. Explicit ``timeout`` kwarg
      2. ``LAST30DAYS_TRANSCRIPT_TIMEOUT`` in os.environ
      3. ``LAST30DAYS_TRANSCRIPT_TIMEOUT`` in caller-supplied config dict
      4. ``DEFAULT_TRANSCRIPT_TIMEOUT`` (30s)
    Mirrors the ``os.environ.get(X) or config.get(X)`` pattern used for
    LAST30DAYS_STORE in last30days.py so the env var works whether it's
    shell-exported or set in ~/.config/last30days/.env.
    """
    if timeout is not None:
        try:
            return float(timeout)
        except (TypeError, ValueError):
            pass
    raw = os.environ.get("LAST30DAYS_TRANSCRIPT_TIMEOUT")
    if not raw and config:
        raw = config.get("LAST30DAYS_TRANSCRIPT_TIMEOUT")
    if raw:
        try:
            return float(raw)
        except (TypeError, ValueError):
            pass
    return float(DEFAULT_TRANSCRIPT_TIMEOUT)
 def _extract_core_subject(topic: str) -> str:
@@ -81,17 +49,6 @@ def _extract_core_subject(topic: str) -> str:
    return extract_core_subject(topic, noise=_INSTAGRAM_NOISE)
 def _to_hashtag_form(query: str) -> str:
    """Collapse a multi-word query to hashtag form (no spaces, lowercase).
    SC's /v2/instagram/reels/search wraps Google Search and is documented
    to be flaky on multi-token queries. Single-token queries map to a
    hashtag page lookup which is the stable path. Used as a 500-retry
    fallback before the request bubbles up as a silent failure.
    """
    return ''.join(query.split()).lower()
 def _infer_query_intent(topic: str) -> str:
    """Tiny local intent classifier for Instagram query expansion."""
    text = topic.lower().strip()
@@ -279,17 +236,30 @@ def _user_reels(
    """
    _log(f"User reels: @{handle}")
    reels_url = f"{SCRAPECREATORS_BASE}/v1/instagram/user/reels"
-    try:
+    if not _requests:
-        data = http.get(
+        try:
-            reels_url,
+            from urllib.parse import urlencode
-            params={"handle": handle},
+            params = urlencode({"handle": handle})
-            headers=http.scrapecreators_headers(token),
+            url = f"{reels_url}?{params}"
-            timeout=30,
+            headers = http.scrapecreators_headers(token)
-            retries=2,
+            headers["User-Agent"] = http.USER_AGENT
-        )
+            data = http.get(url, headers=headers, timeout=30, retries=2)
-    except Exception as e:
+        except Exception as e:
-        _log(f"User reels error for @{handle}: {e}")
+            _log(f"User reels error (urllib) for @{handle}: {e}")
-        return []
+            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}")
@@ -323,37 +293,31 @@ def search_instagram(
    _log(f"Searching Instagram for '{core_topic}' (depth={depth}, count={config['results_per_page']})")
-    try:
+    if not _requests:
-        data = http.get(
+        _log("requests library not installed, falling back to urllib")
-            f"{SCRAPECREATORS_BASE}/v2/instagram/reels/search",
+        try:
-            params={"query": core_topic},
+            from urllib.parse import urlencode
-            headers=http.scrapecreators_headers(token),
+            params = urlencode({"query": core_topic})
-            timeout=30,
+            url = f"{SCRAPECREATORS_BASE}/v2/instagram/reels/search?{params}"
-            retries=2,
+            headers = http.scrapecreators_headers(token)
-        )
+            headers["User-Agent"] = http.USER_AGENT
-    except http.HTTPError as e:
+            data = http.get(url, headers=headers, timeout=30, retries=2)
-        # SC's v2 reels search wraps Google Search and 500s frequently on
+        except Exception as e:
-        # multi-token queries. Single tokens hit the stable hashtag-page
+            _log(f"ScrapeCreators error (urllib): {e}")
-        # path. Retry once with hashtag form before bubbling up.
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
-        if getattr(e, "status_code", None) == 500 and ' ' in core_topic:
+    else:
-            _log(f"IG search 500 on '{core_topic}', retrying with hashtag form")
+        try:
-            try:
+            resp = _requests.get(
-                data = http.get(
+                f"{SCRAPECREATORS_BASE}/v2/instagram/reels/search",
-                    f"{SCRAPECREATORS_BASE}/v2/instagram/reels/search",
+                params={"query": core_topic},
-                    params={"query": _to_hashtag_form(core_topic)},
+                headers=http.scrapecreators_headers(token),
-                    headers=http.scrapecreators_headers(token),
+                timeout=30,
-                    timeout=30,
+            )
-                    retries=2,
+            resp.raise_for_status()
-                )
+            data = resp.json()
-            except Exception as retry_e:
+        except Exception as e:
                _log(f"IG search retry failed: {retry_e}")
                return {"items": [], "error": f"{type(retry_e).__name__}: {retry_e}"}
        else:
            _log(f"ScrapeCreators error: {e}")
            return {"items": [], "error": f"{type(e).__name__}: {e}"}
    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 []
@@ -385,8 +349,6 @@ def fetch_captions(
    video_items: List[Dict[str, Any]],
    token: str,
    depth: str = "default",
    timeout: Optional[float] = None,
    config: Optional[Dict[str, Any]] = None,
 ) -> Dict[str, str]:
    """Fetch transcripts for top N Instagram reels via ScrapeCreators.
@@ -398,21 +360,14 @@ def fetch_captions(
        video_items: Items from search_instagram()
        token: ScrapeCreators API key
        depth: Depth level for caption limit
        timeout: Optional per-request transcript timeout in seconds. When
            None, resolves from LAST30DAYS_TRANSCRIPT_TIMEOUT (env or
            config), defaulting to DEFAULT_TRANSCRIPT_TIMEOUT (30s).
        config: Optional config dict (from env.get_config()) used as a
            fallback source for LAST30DAYS_TRANSCRIPT_TIMEOUT when the
            value is not exported in os.environ.
    Returns:
        Dict mapping video_id -> caption text (truncated to 500 words)
    """
-    depth_cfg = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
-    max_captions = depth_cfg["max_captions"]
+    max_captions = config["max_captions"]
    transcript_timeout = _resolve_transcript_timeout(timeout, config)
-    if not video_items or not token:
+    if not video_items or not token or not _requests:
        return {}
    top_items = video_items[:max_captions]
@@ -437,24 +392,26 @@ def fetch_captions(
        if not url:
            continue
        try:
-            data = http.get(
+            resp = _requests.get(
                f"{SCRAPECREATORS_BASE}/v2/instagram/media/transcript",
                params={"url": url},
                headers=http.scrapecreators_headers(token),
-                timeout=transcript_timeout,
+                timeout=15,
                retries=1,
            )
-            transcripts = data.get("transcripts") or []
+            if resp.status_code == 200:
-            if transcripts and isinstance(transcripts, list):
+                data = resp.json()
-                transcript_text = " ".join(
+                transcripts = data.get("transcripts") or []
-                    t.get("text", "") for t in transcripts
+                if transcripts and isinstance(transcripts, list):
-                    if isinstance(t, dict) and t.get("text")
+                    # Combine all transcript segments
-                )
+                    transcript_text = " ".join(
-                if transcript_text:
+                        t.get("text", "") for t in transcripts
-                    words = transcript_text.split()
+                        if isinstance(t, dict) and t.get("text")
-                    if len(words) > CAPTION_MAX_WORDS:
+                    )
-                        transcript_text = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
+                    if transcript_text:
-                    captions[vid] = 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}")
@@ -49,7 +49,6 @@ def normalize_source_items(
        "xquik": _normalize_x,
        "pinterest": _normalize_pinterest,
        "polymarket": _normalize_polymarket,
        "digg": _normalize_digg,
        "grounding": _normalize_grounding,
        "xiaohongshu": _normalize_grounding,
        "github": _normalize_github,
@@ -111,19 +110,6 @@ def _first_present(d: dict[str, Any], keys: tuple[str, ...], default: Any) -> An
    return default
 def _join_comment_excerpts(
    top_comments: list[Any],
    key: str,
    limit: int = 3,
 ) -> str:
    """Space-join the `key` field from the first `limit` dict-shaped comments."""
    return " ".join(
        str(comment.get(key) or "").strip()
        for comment in top_comments[:limit]
        if isinstance(comment, dict)
    )
 def _domain_from_url(url: str) -> str | None:
    if not url:
        return None
@@ -183,7 +169,11 @@ def _normalize_reddit(
    to_date: str,
 ) -> schema.SourceItem:
    top_comments = item.get("top_comments") or []
-    comment_text = _join_comment_excerpts(top_comments, "excerpt")
+    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 [
@@ -251,11 +241,6 @@ def _normalize_youtube(
    metadata: dict[str, Any] = {}
    if highlights:
        metadata["transcript_highlights"] = highlights
    if item.get("captions_disabled"):
        # Surfaced for quality_nudge: uploader disabled captions, so this
        # video should be subtracted from the degraded-transcript-ratio
        # denominator (it was never going to produce a transcript).
        metadata["captions_disabled"] = True
    metadata["top_comments"] = _remap_comments(
        item.get("top_comments") or [],
        score_keys=("score", "likes"),
@@ -353,7 +338,11 @@ def _normalize_hackernews(
    to_date: str,
 ) -> schema.SourceItem:
    top_comments = item.get("top_comments") or []
-    comment_text = _join_comment_excerpts(top_comments, "text")
+    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(
@@ -405,53 +394,6 @@ def _normalize_microblog(
    )
 def _normalize_digg(
    source: str,
    item: dict[str, Any],
    index: int,
    from_date: str,
    to_date: str,
 ) -> schema.SourceItem:
    """Normalizer for Digg AI 1000 clusters.
    Each cluster is one item. The TLDR carries the most useful body for
    rerank and synthesis. Top-ranked X posts attached at search time are
    passed through under metadata['posts'] so render can emit them as
    inline 'via Digg' quotes.
    """
    title = str(item.get("title") or "").strip()
    tldr = str(item.get("tldr") or "").strip()
    body = "\n\n".join(part for part in [title, tldr] if part)
    posts = item.get("posts") or []
    if not isinstance(posts, list):
        posts = []
    cluster_url_id = str(item.get("id") or f"DG{index + 1}")
    return _source_item(
        item_id=cluster_url_id,
        source=source,
        title=title or f"Digg cluster {index + 1}",
        body=body,
        url=str(item.get("url") or f"https://di.gg/ai/{cluster_url_id}"),
        author="",
        container="Digg",
        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=tldr[:400],
        metadata={
            "clusterUrlId": cluster_url_id,
            "tldr": tldr,
            "rank": (item.get("engagement") or {}).get("rank"),
            "uniqueAuthors": (item.get("engagement") or {}).get("uniqueAuthors"),
            "postCount": (item.get("engagement") or {}).get("postCount"),
            "firstPostAge": item.get("first_post_age"),
            "posts": posts,
        },
    )
 def _normalize_polymarket(
    source: str,
    item: dict[str, Any],
@@ -499,7 +441,11 @@ def _normalize_github(
    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_comment_excerpts(top_comments, "excerpt")
+    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(
@@ -11,6 +11,11 @@ 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"
@@ -135,17 +140,31 @@ def search_pinterest(
    _log(f"Searching Pinterest for '{core_topic}' (depth={depth}, count={config['results_per_page']})")
-    try:
+    if not _requests:
-        data = http.get(
+        _log("requests library not installed, falling back to urllib")
-            f"{SCRAPECREATORS_BASE}/search",
+        try:
-            params={"keyword": core_topic},
+            from urllib.parse import urlencode
-            headers=http.scrapecreators_headers(token),
+            params = urlencode({"keyword": core_topic})
-            timeout=30,
+            url = f"{SCRAPECREATORS_BASE}/search?{params}"
-            retries=2,
+            headers = http.scrapecreators_headers(token)
-        )
+            headers["User-Agent"] = http.USER_AGENT
-    except Exception as e:
+            data = http.get(url, headers=headers, timeout=30, retries=2)
-        _log(f"ScrapeCreators error: {e}")
+        except Exception as e:
-        return {"items": [], "error": f"{type(e).__name__}: {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 []
@@ -15,7 +15,6 @@ from . import (
    bluesky,
    dates,
    dedupe,
    digg,
    entity_extract,
    env,
    github,
@@ -31,7 +30,6 @@ from . import (
    query,
    reddit,
    reddit_public,
    relevance,
    rerank,
    schema,
    signals,
@@ -79,10 +77,7 @@ MOCK_AVAILABLE_SOURCES = [
    "xiaohongshu",
    "github",
    "perplexity",
    "threads",
    "pinterest",
    "xquik",
    "digg",
 ]
@@ -110,8 +105,6 @@ def available_sources(config: dict[str, Any], requested_sources: list[str] | Non
    available.extend(["hackernews", "polymarket"])
    if config.get("GITHUB_TOKEN") or which("gh"):
        available.append("github")
    if which("digg-pp-cli"):
        available.append("digg")
    if env.is_bluesky_available(config):
        available.append("bluesky")
    if env.is_truthsocial_available(config):
@@ -120,9 +113,7 @@ def available_sources(config: dict[str, Any], requested_sources: list[str] | Non
        available.append("grounding")
    # Perplexity Sonar: opt-in additive source via INCLUDE_SOURCES=perplexity
    include_sources = (config.get("INCLUDE_SOURCES") or "").lower().split(",")
-    if config.get("OPENROUTER_API_KEY") and (
+    if config.get("OPENROUTER_API_KEY") and "perplexity" in include_sources:
        "perplexity" in include_sources or (requested_sources and "perplexity" in requested_sources)
    ):
        available.append("perplexity")
    if requested_sources and "xiaohongshu" in requested_sources and env.is_xiaohongshu_available(config):
        available.append("xiaohongshu")
@@ -132,9 +123,6 @@ def available_sources(config: dict[str, Any], requested_sources: list[str] | Non
        available.append("pinterest")
    if env.is_xquik_available(config):
        available.append("xquik")
    exclude = {s.strip().lower() for s in (config.get("EXCLUDE_SOURCES") or "").split(",") if s.strip()}
    if exclude:
        available = [s for s in available if s not in exclude]
    return available
@@ -207,7 +195,7 @@ def run(
            available = [source for source in available if source in requested_sources]
    if web_backend == "none":
        available = [s for s in available if s != "grounding"]
-    elif web_backend in ("brave", "exa", "serper", "parallel") and "grounding" not in available:
+    elif web_backend in ("brave", "exa", "serper") and "grounding" not in available:
        available.append("grounding")
    if not available:
        raise RuntimeError("No sources are available for this run.")
@@ -512,12 +500,11 @@ def _normalize_score_dedupe(
        source, raw_items, from_date, to_date,
        freshness_mode=freshness_mode,
    )
-    prepared_query = relevance.PreparedQuery(ranking_query)
+    normalized = signals.annotate_stream(normalized, ranking_query, freshness_mode)
    normalized = signals.annotate_stream(normalized, prepared_query, freshness_mode)
    normalized = signals.prune_low_relevance(normalized)
    normalized = dedupe.dedupe_items(normalized)
    for item in normalized:
-        item.snippet = snippet.extract_best_snippet(item, prepared_query)
+        item.snippet = snippet.extract_best_snippet(item, ranking_query)
    return normalized
@@ -542,12 +529,6 @@ def _finalize_items_by_source(
            keywords = config.get("_polymarket_keywords") if isinstance(config, dict) else None
            if keywords:
                items = polymarket.filter_items_against_keywords(items, keywords)
        if source == "digg" and items:
            # Pull top-ranked X posts only for the survivors that will appear
            # in the brief. Spending the enrichment budget here (rather than
            # at retrieval time) keeps the inline 'via Digg' quotes
            # paired with the clusters dedupe actually kept.
            digg.enrich_source_items(items, top_k=3)
        finalized[source] = items
    return finalized
@@ -983,13 +964,6 @@ def _retrieve_stream(
    if source == "hackernews":
        result = hackernews.search_hackernews(subquery.search_query, from_date, to_date, depth=depth)
        return hackernews.parse_hackernews_response(result, query=subquery.search_query), {}
    if source == "digg":
        result = digg.search_digg(subquery.search_query, from_date, to_date, depth=depth)
        items = digg.parse_digg_response(result, query=subquery.search_query)
        # Enrichment with attached X posts is deferred to
        # _finalize_items_by_source so it runs on the items that actually
        # survive dedupe rather than on top-K of the raw fanout.
        return items, {}
    if source == "bluesky":
        result = bluesky.search_bluesky(subquery.search_query, from_date, to_date, depth=depth, config=config)
        return bluesky.parse_bluesky_response(result), {}
@@ -1082,45 +1056,6 @@ def _mock_stream_results(source: str, subquery: schema.SubQuery) -> tuple[list[d
                "why_relevant": "Brave web search",
            }
        ],
        "digg": [
            {
                "id": "mock1abc",
                "title": f"Digg cluster about {subquery.search_query}",
                "url": "https://di.gg/ai/mock1abc",
                "tldr": f"Curated cluster summarizing recent {subquery.search_query} discussion across the AI 1000.",
                "author": "",
                "date": dates.get_date_range(3)[0],
                "engagement": {"postCount": 8, "uniqueAuthors": 5, "rank": 2, "rank_score": 49.0},
                "first_post_age": "3d",
                "posts": [
                    {
                        "username": "exampledev",
                        "display_name": "Example Dev",
                        "category": "Engineer",
                        "rank": 142,
                        "body": f"Quote from the AI 1000 about {subquery.search_query}.",
                        "post_type": "tweet",
                        "x_url": "https://x.com/exampledev/status/1",
                        "posted_at": dates.get_date_range(3)[0],
                    },
                ],
                "relevance": 0.84,
                "why_relevant": "Mock Digg cluster",
            },
            {
                "id": "mock2def",
                "title": f"Second Digg cluster on {subquery.search_query}",
                "url": "https://di.gg/ai/mock2def",
                "tldr": f"Another angle on {subquery.search_query}.",
                "author": "",
                "date": dates.get_date_range(8)[0],
                "engagement": {"postCount": 3, "uniqueAuthors": 2, "rank": 18, "rank_score": 33.0},
                "first_post_age": "8d",
                "posts": [],
                "relevance": 0.71,
                "why_relevant": "Mock Digg cluster",
            },
        ],
    }
    if source == "grounding":
        return payloads.get(source, []), {
@@ -19,14 +19,14 @@ ALLOWED_INTENTS = {
 }
 ALLOWED_CLUSTER_MODES = {"none", "story", "workflow", "market", "debate"}
 QUICK_SOURCE_PRIORITY = {
-    "factual": ["hackernews", "reddit", "x", "xquik", "youtube"],
+    "factual": ["hackernews", "reddit", "x", "youtube"],
-    "product": ["youtube", "reddit", "x", "xquik", "tiktok"],
+    "product": ["youtube", "reddit", "x", "tiktok"],
-    "concept": ["hackernews", "reddit", "x", "xquik", "youtube"],
+    "concept": ["hackernews", "reddit", "x", "youtube"],
-    "opinion": ["reddit", "x", "xquik", "youtube", "hackernews"],
+    "opinion": ["reddit", "x", "youtube", "hackernews"],
-    "how_to": ["youtube", "reddit", "x", "xquik", "hackernews"],
+    "how_to": ["youtube", "reddit", "x", "hackernews"],
-    "comparison": ["reddit", "x", "xquik", "hackernews", "youtube"],
+    "comparison": ["reddit", "x", "hackernews", "youtube"],
-    "breaking_news": ["x", "xquik", "reddit", "hackernews", "youtube", "polymarket"],
+    "breaking_news": ["x", "reddit", "hackernews", "youtube", "polymarket"],
-    "prediction": ["polymarket", "x", "xquik", "hackernews", "reddit", "youtube"],
+    "prediction": ["polymarket", "x", "hackernews", "reddit", "youtube"],
 }
 SOURCE_PRIORITY = {
    "factual": ["hackernews", "reddit", "x", "youtube"],
@@ -60,7 +60,6 @@ INTENT_SOURCE_EXCLUSIONS: dict[str, set[str]] = {
 SOURCE_CAPABILITIES = {
    "reddit": {"discussion", "social"},
    "x": {"discussion", "social"},
    "xquik": {"discussion", "social"},
    "youtube": {"video", "video_longform", "discussion"},
    "tiktok": {"video", "video_shortform", "social"},
    "instagram": {"video", "video_shortform", "social"},
@@ -68,7 +67,6 @@ SOURCE_CAPABILITIES = {
    "bluesky": {"discussion", "social"},
    "truthsocial": {"discussion", "social"},
    "polymarket": {"market"},
    "digg": {"discussion", "social", "link"},
    "xiaohongshu": {"video", "video_shortform", "social"},
    "github": {"discussion", "link"},
    "grounding": {"web", "reference", "link"},
@@ -9,7 +9,7 @@ from typing import Any
 from . import env, http, schema
-GEMINI_FLASH_LITE = "gemini-3.1-flash-lite"
+GEMINI_FLASH_LITE = "gemini-3.1-flash-lite-preview"
 GEMINI_PRO = "gemini-3.1-pro-preview"
 OPENAI_DEFAULT = "gpt-5.4-nano"
 XAI_DEFAULT = "grok-4-1-fast"
@@ -19,11 +19,7 @@ OPENAI_RESPONSES_URL = "https://api.openai.com/v1/responses"
 CODEX_RESPONSES_URL = "https://chatgpt.com/backend-api/codex/responses"
 XAI_RESPONSES_URL = "https://api.x.ai/v1/responses"
 OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
-# OpenRouter routes the Gemini Flash Lite tier as the -preview slug; that is the
+OPENROUTER_DEFAULT = "google/gemini-flash-2.0"
 # stable form on that routing layer even though native Gemini's GEMINI_FLASH_LITE
 # constant is suffix-free. If GEMINI_FLASH_LITE moves to a non-preview stable ID,
 # double-check that OpenRouter's slug still maps to the same upstream model.
 OPENROUTER_DEFAULT = "google/gemini-3.1-flash-lite-preview"
 class ReasoningClient:
@@ -97,6 +93,13 @@ class GeminiClient(ReasoningClient):
        )
        return extract_gemini_text(payload)
    def ground_search(self, model: str, prompt: str) -> dict[str, Any]:
        return self._generate_content(model, prompt, tools=[{"google_search": {}}])
    def url_context_json(self, model: str, prompt: str) -> dict[str, Any]:
        return self.generate_json(model, prompt, tools=[{"url_context": {}}])
 class OpenAIClient(ReasoningClient):
    name = "openai"
@@ -236,8 +239,8 @@ def _resolve_model_pins(config: dict[str, Any], depth: str, provider_name: str)
    rerank_model = config.get("LAST30DAYS_RERANK_MODEL") or default_rerank
    if provider_name == "gemini":
-        _require_gemini_31(planner_model, role="planner")
+        _require_gemini_31_preview(planner_model, role="planner")
-        _require_gemini_31(rerank_model, role="rerank")
+        _require_gemini_31_preview(rerank_model, role="rerank")
    return planner_model, rerank_model
@@ -348,11 +351,11 @@ def _resolve_x_backend(config: dict[str, Any]) -> str | None:
    return env.get_x_source(config)
-def _require_gemini_31(model: str, *, role: str) -> None:
+def _require_gemini_31_preview(model: str, *, role: str) -> None:
-    if model.startswith("gemini-3.1-"):
+    if model.startswith("gemini-3.1-") and model.endswith("-preview"):
        return
    raise RuntimeError(
-        f"{role} must use a Gemini 3.1 model. Got: {model}"
+        f"{role} must use a Gemini 3.1 preview model. Got: {model}"
    )
@@ -0,0 +1,190 @@
 """Post-research quality score and upgrade nudge.
 Computes a quality score based on 5 core sources and builds
 a nudge message describing what the user missed and how to fix it.
 """
 from typing import List
 # The 5 core sources
 CORE_SOURCES = ["hn", "polymarket", "x", "youtube", "reddit"]
 # Labels for display
 SOURCE_LABELS = {
    "hn": "Hacker News",
    "polymarket": "Polymarket",
    "x": "X/Twitter",
    "youtube": "YouTube",
    "reddit": "Reddit",
 }
 def _is_x_active(config: dict, research_results: dict) -> bool:
    """Check if X source is active (has credentials AND didn't error)."""
    has_creds = bool(config.get("AUTH_TOKEN") or config.get("XAI_API_KEY"))
    if not has_creds:
        return False
    # If X errored this run, it's configured but broken
    if research_results.get("x_error"):
        return False
    return True
 def _is_youtube_active(config: dict, research_results: dict) -> bool:
    """Check if YouTube source is active (yt-dlp installed)."""
    try:
        from . import youtube_yt
        has_ytdlp = youtube_yt.is_ytdlp_installed()
    except Exception:
        has_ytdlp = False
    if not has_ytdlp:
        return False
    if research_results.get("youtube_error"):
        return False
    return True
 def compute_quality_score(config: dict, research_results: dict) -> dict:
    """Compute research quality score based on 5 core sources.
    Args:
        config: Configuration dict from env.get_config()
        research_results: Dict with keys like x_error, youtube_error,
            reddit_error reflecting what happened this run.
    Returns:
        {
            "score_pct": 40-100,
            "core_active": ["hn", "polymarket", ...],
            "core_missing": ["x", "youtube"],
            "core_errored": [],  # configured but errored
            "nudge_text": "..." or None if 100%
        }
    """
    core_active: List[str] = []
    core_missing: List[str] = []
    core_errored: List[str] = []
    # HN, Polymarket, and Reddit are always active
    core_active.append("hn")
    core_active.append("polymarket")
    core_active.append("reddit")
    # X
    has_x_creds = bool(config.get("AUTH_TOKEN") or config.get("XAI_API_KEY"))
    if _is_x_active(config, research_results):
        core_active.append("x")
    else:
        core_missing.append("x")
        if has_x_creds and research_results.get("x_error"):
            core_errored.append("x")
    # YouTube
    yt_active = _is_youtube_active(config, research_results)
    if yt_active:
        core_active.append("youtube")
    else:
        core_missing.append("youtube")
        # Check if configured but errored (yt-dlp installed but failed this run)
        try:
            from . import youtube_yt
            has_ytdlp = youtube_yt.is_ytdlp_installed()
        except Exception:
            has_ytdlp = False
        if has_ytdlp and research_results.get("youtube_error"):
            core_errored.append("youtube")
    score_pct = int(len(core_active) / 5 * 100)
    has_sc = bool(config.get("SCRAPECREATORS_API_KEY"))
    active_sources = research_results.get("active_sources") or []
    nudge_text = _build_nudge_text(core_missing, core_errored, has_sc=has_sc, active_sources=active_sources) if core_missing else None
    return {
        "score_pct": score_pct,
        "core_active": core_active,
        "core_missing": core_missing,
        "core_errored": core_errored,
        "nudge_text": nudge_text,
    }
 def _build_nudge_text(core_missing: List[str], core_errored: List[str], has_sc: bool = False, active_sources: list = None) -> str:
    """Build human-readable nudge text describing what was missed.
    Prioritizes free suggestions. Optionally mentions bonus sources
    (TikTok, Instagram, Threads, Pinterest) if ScrapeCreators key is configured.
    """
    lines: List[str] = []
    # Describe what was missed
    missed_parts: List[str] = []
    for src in core_missing:
        label = SOURCE_LABELS[src]
        if src in core_errored:
            missed_parts.append(f"{label} (errored this run)")
        else:
            missed_parts.append(label)
    active_count = 5 - len(core_missing)
    lines.append(f"Research quality: {active_count}/5 core sources.")
    lines.append(f"Missing: {', '.join(missed_parts)}.")
    lines.append("")
    # Free suggestions
    free_suggestions: List[str] = []
    if "x" in core_missing:
        if "x" in core_errored:
            free_suggestions.append(
                "X/Twitter errored - log into x.com in your browser, then re-run."
            )
        else:
            free_suggestions.append(
                "X/Twitter: real-time posts with likes and reposts - the fastest "
                "signal for breaking topics. Two options: log into x.com in your "
                "browser and re-run (cookies detected automatically), or add "
                "XAI_API_KEY to your .env (no browser access, get key at api.x.ai)."
            )
    if "youtube" in core_missing:
        if "youtube" in core_errored:
            free_suggestions.append(
                "YouTube errored - update yt-dlp: brew upgrade yt-dlp"
            )
        else:
            free_suggestions.append(
                "YouTube: video transcripts with key moments - often the deepest "
                "explanations on any topic. Install yt-dlp: brew install yt-dlp (free)"
            )
    # Mention bonus opt-in sources when SC key is present
    if has_sc:
        bonus_hints = []
        if "threads" not in (active_sources or []):
            bonus_hints.append("Threads")
        if "pinterest" not in (active_sources or []):
            bonus_hints.append("Pinterest")
        if bonus_hints:
            free_suggestions.append(
                f"Your SC key also powers {', '.join(bonus_hints)} and YouTube comments. "
                "Add them to INCLUDE_SOURCES in your .env to enable."
            )
    if free_suggestions:
        lines.append("Free fixes:")
        for s in free_suggestions:
            lines.append(f"  - {s}")
        lines.append("")
    # Bonus sources mention (non-blocking)
    if not has_sc:
        lines.append(
            "Bonus: TikTok and Instagram are available with a free "
            "ScrapeCreators key at scrapecreators.com (no affiliation)."
        )
    else:
        lines.append("last30days has no affiliation with any API provider.")
    return "\n".join(lines)
@@ -334,7 +334,7 @@ def _global_search(
        )
        return data.get("posts", data.get("data", []))
    except http.HTTPError as e:
-        if e.status_code in (401, 402, 403):
+        if e.status_code in (401, 403):
            raise
        _log(f"Global search error: {e}")
        return []
@@ -376,11 +376,6 @@ def _subreddit_search(
            retries=2,
        )
        return data.get("posts", data.get("data", []))
    except http.HTTPError as e:
        if e.status_code in (401, 402, 403):
            raise
        _log(f"Subreddit search error for r/{subreddit}: {e}")
        return []
    except Exception as e:
        _log(f"Subreddit search error for r/{subreddit}: {e}")
        return []
@@ -408,11 +403,6 @@ def fetch_post_comments(
            retries=2,
        )
        return data.get("comments", data.get("data", []))
    except http.HTTPError as e:
        if e.status_code in (401, 402, 403):
            raise
        _log(f"Comment fetch error: {e}")
        return []
    except Exception as e:
        _log(f"Comment fetch error: {e}")
        return []
--- a/Show More
+++ b/Show More