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
211 changed files with 2900 additions and 3761 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": "3.2.3",
+      "version": "3.0.9",
      "author": {
        "name": "Matt Van Horn",
        "url": "https://github.com/mvanhorn"
@@ -1,6 +1,6 @@
 {
  "name": "last30days",
-  "version": "3.2.3",
+  "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 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
@@ -1,26 +0,0 @@
 name: Validate
 on:
  pull_request:
  push:
    branches:
      - main
 permissions:
  contents: read
 jobs:
  plugin-contract:
    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 plugin contract tests
        run: uv run pytest tests/test_plugin_contract.py tests/test_version_consistency.py
@@ -28,3 +28,9 @@ htmlcov/
 # Internal planning docs (ce:plan output) — keep local, don't publish
 docs/plans/
 # Marketing video build artifacts
 marketing/v3.1-launch/node_modules/
 marketing/v3.1-launch/out/
 marketing/*/node_modules/
 marketing/*/out/
@@ -1 +0,0 @@
@CLAUDE.md
@@ -5,52 +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]
 ### Changed
 - Rename "Digg AI 1000" to just "Digg" in user-facing output (footer line, source label, inline-quote suffix, why_relevant, container attribution). Internal references to the upstream Digg AI 1000 product remain in code comments and docstrings.
 - Bump `POSTS_PER_CLUSTER` from 3 to 5 and the render-side display limit from 2 to 3 to match the per-source enrichment caps used by Reddit, HN, YouTube, TikTok, and GitHub. The previous 3/2 caps routinely truncated cluster context (e.g. dropped a Jason Calacanis quote tweet on a `cli-printing-press` run).
 - Rewrite SKILL.md path resolution. STEP 0 narrows from a global canonical-path enforcement to a Claude-Code-marketplaces-only stale-clone guard. Step 1 SKILL_ROOT resolver walks a single precedence list (Claude plugin cache, then `~/.codex/skills/`, `~/.agents/skills/`, repo checkout, `./.skills/last30days` for `npx skills add`, CWD, Gemini). Adds SKILL.md frontmatter fallback to `render.py::_skill_version` so the badge no longer prints `v?` on installs that don't include `.claude-plugin/plugin.json`.
 - Switch SKILL.md's `--plan` and `--competitors-plan` invocation templates from inline single-quoted JSON to heredoc-written tmpfiles. Apostrophes in resolved context strings ("McDonald's", "people's choice", "developer's") previously closed the outer single-quote and broke shell parsing before the engine started — observed in a Codex run during PR #400 testing. The engine's `parse_plan()` / `parse_competitors_plan()` already supported file paths (via `os.path.isfile()` probe); only the template prose changed. Fixes [#403](https://github.com/mvanhorn/last30days-skill/issues/403).
 ### Removed
 - **BREAKING for Codex native-plugin users:** `.codex-plugin/plugin.json` and the matching SKILL_ROOT resolver branch in SKILL.md Step 1. Codex users should install via `npx skills add mvanhorn/last30days-skill` or copy the skill to `~/.codex/skills/last30days/`.
 - **`skills/last30days/scripts/sync.sh`.** The maintainer dev-deploy script is gone. Every job it did has a better replacement: `npx skills add . -g -y` symlinks the working tree into every detected harness's skill dir (better than sync.sh's copy model — edits propagate live), `hermes skills install mvanhorn/last30days-skill --force` handles Hermes, `clawhub install last30days-official` handles OpenClaw, and the Claude marketplace cache target was a "test against the official install path" hack we shouldn't have been recommending in the first place. The `test_sync_cache_path_uses_skill_version` test was dropped along with it. CLAUDE.md, HERMES_SETUP.md, the PR template, and a render.py docstring were updated to drop references; CHANGELOG and historical docs (release notes, plan files) keep their existing mentions as accurate history.
 ## [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 `skills/last30days/SKILL.md`, a path that does not exist. Fixed to point at root `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
@@ -107,7 +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
@@ -4,20 +4,20 @@ Claude Code skill for researching any topic across Reddit, X, YouTube, and web.
 Python scripts with multi-source search aggregation.
 ## Structure
- `skills/last30days/SKILL.md` — canonical skill definition
+- `scripts/last30days.py` — main research engine
- `skills/last30days/scripts/last30days.py` — main research engine
+- `scripts/lib/` — search, enrichment, rendering modules
- `skills/last30days/scripts/lib/` — search, enrichment, rendering modules
+- `scripts/lib/vendor/bird-search/` — vendored X search client
- `skills/last30days/scripts/lib/vendor/bird-search/` — vendored X search client
+- `SKILL.md` — skill definition (deployed to ~/.claude/skills/last30days/)
 ## Commands
 ```bash
-python3 skills/last30days/scripts/last30days.py "test query" --emit=compact
+python3 scripts/last30days.py "test query" --emit=compact  # Run research
-npx skills add . -g -y   # one-time: symlink this repo into every detected harness's skill dir
+bash scripts/sync.sh                                        # Deploy to ~/.claude, ~/.agents, ~/.codex
 ```
 ## 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.
+- After edits: run `bash scripts/sync.sh` to deploy
 - Git remote: origin = public (`mvanhorn/last30days-skill`)
 ## Beta channel
@@ -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
@@ -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
@@ -12,20 +12,23 @@
 **An AI agent-led search engine scored by upvotes, likes, and real money - not editors.**
-This README tracks the current v3 pipeline. The runtime skill spec lives in [SKILL.md](SKILL.md), which is the source of truth for the latest command and setup behavior.
+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
 ```
-**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.
@@ -65,7 +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. |
@@ -94,28 +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.
@@ -165,61 +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)
@@ -227,7 +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
@@ -235,14 +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.
@@ -1,6 +1,6 @@
 ---
 name: last30days
-version: "3.2.3"
+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
@@ -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_ROOT resolver** in the engine Bash calls walks a precedence list of known install locations and picks the highest-versioned freshest copy, never `~/.openclaw/` or other stale copies.
+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_ROOT/../../.claude-plugin/plugin.json" 2>/dev/null || awk '/^version:/{gsub(/"/,"",$2); print $2; exit}' "$SKILL_ROOT/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 v3.2.3: 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.
@@ -327,7 +317,6 @@ 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 and INCLUDE_SOURCES contains tiktok: add TikTok
@@ -592,56 +581,18 @@ When the user asks "X vs Y" (or "X vs Y vs Z"), the engine fans out N full `pipe
 **Invocation:**
 ```bash
 # Comparison mode skips Step 1, so resolve SKILL_ROOT inline here (same precedence
 # walk as Step 1 — keep the two in sync if you edit either).
 SKILL_ROOT=""
 CLAUDE_PLUGIN_ROOT="$(find "$HOME/.claude/plugins/cache/last30days-skill/last30days" -mindepth 1 -maxdepth 1 -type d 2>/dev/null | sort -V | tail -1)"
 if [ -n "$CLAUDE_PLUGIN_ROOT" ]; then
  if [ -f "$CLAUDE_PLUGIN_ROOT/skills/last30days/scripts/last30days.py" ]; then
    SKILL_ROOT="$CLAUDE_PLUGIN_ROOT/skills/last30days"
  elif [ -f "$CLAUDE_PLUGIN_ROOT/scripts/last30days.py" ]; then
    SKILL_ROOT="$CLAUDE_PLUGIN_ROOT"
  fi
 fi
 if [ -z "$SKILL_ROOT" ] || [ ! -f "$SKILL_ROOT/scripts/last30days.py" ]; then
  for dir in \
      "$HOME/.codex/skills/last30days" \
      "$HOME/.agents/skills/last30days" \
      "./skills/last30days" \
      "./.skills/last30days" \
      "." \
      "${GEMINI_EXTENSION_DIR:-}"; do
    [ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
  done
 fi
 # Write the per-entity plan to a tmpfile and pass the path to the engine.
 # The engine's parse_competitors_plan() reads file paths transparently. This
 # avoids the inline-single-quoted-JSON apostrophe trap (resolved context
 # strings like "people's choice" or "McDonald's" otherwise close the outer
 # single-quote and break shell parsing before the engine is even invoked).
 # Trailing XXXXXX (no .json suffix) so BSD/macOS mktemp works the same as
 # GNU; BSD only substitutes X's at the end of the template.
 COMPETITORS_PLAN_FILE=$(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}" "${SKILL_ROOT}/scripts/last30days.py" "{TOPIC_A} vs {TOPIC_B} vs {TOPIC_C}" \
  --emit=compact \
  --save-dir="${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.
@@ -873,7 +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`
@@ -916,65 +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
-# Resolve SKILL_ROOT by walking a precedence list of known install locations.
+# PIN SKILL_ROOT to the public plugin cache (highest-version dir wins on upgrade).
-# Claude Code plugin cache wins when present (highest version dir picked on upgrade),
+# DO NOT write your own path-discovery loop. The 2026-04-18 Peter Steinberger run 1
-# then common per-harness skill dirs, then a repo checkout.
+# regression was caused by a custom discovery loop landing on ~/.openclaw/skills/last30days/
-SKILL_ROOT=""
+# (a stale copy from a private-repo sync pattern). That path contains a pre-plan-007
 # engine and produces non-canonical output. This pinned resolution ignores every stale
 # copy (~/.openclaw/, ~/.agents/, ~/.codex/) and picks the plugin cache exclusively.
 SKILL_ROOT="$(ls -d "$HOME/.claude/plugins/cache/last30days-skill/last30days/"*/ 2>/dev/null | sort -V | tail -1)"
 SKILL_ROOT="${SKILL_ROOT%/}"
-# 1. Claude Code plugin cache (versioned, sort -V picks freshest). Two cache layouts ship in the wild:
+# Fallback for repo checkout / Gemini / Codex hosts where the plugin cache does not exist.
-#    nested ({cache}/{version}/skills/last30days/scripts/...) and flat ({cache}/{version}/scripts/...).
+# Only runs if the public plugin cache is missing entirely.
 # `find` (not `ls + glob`) because zsh errors on globs that match nothing, leaking
 # noisy "no matches found" stderr in Codex/zsh sessions even with 2>/dev/null.
 CLAUDE_PLUGIN_ROOT="$(find "$HOME/.claude/plugins/cache/last30days-skill/last30days" -mindepth 1 -maxdepth 1 -type d 2>/dev/null | sort -V | tail -1)"
 if [ -n "$CLAUDE_PLUGIN_ROOT" ]; then
  if [ -f "$CLAUDE_PLUGIN_ROOT/skills/last30days/scripts/last30days.py" ]; then
    SKILL_ROOT="$CLAUDE_PLUGIN_ROOT/skills/last30days"
  elif [ -f "$CLAUDE_PLUGIN_ROOT/scripts/last30days.py" ]; then
    SKILL_ROOT="$CLAUDE_PLUGIN_ROOT"
  fi
 fi
 # 2. Common per-harness skill dirs and repo checkout (npx skills, Codex, Agents, Gemini, etc).
 if [ -z "$SKILL_ROOT" ] || [ ! -f "$SKILL_ROOT/scripts/last30days.py" ]; then
-  for dir in \
+  for dir in "." "${CLAUDE_PLUGIN_ROOT:-}" "${GEMINI_EXTENSION_DIR:-}"; do
      "$HOME/.codex/skills/last30days" \
      "$HOME/.agents/skills/last30days" \
      "./skills/last30days" \
      "./.skills/last30days" \
      "." \
      "${GEMINI_EXTENSION_DIR:-}"; do
    [ -n "$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 any known install location" >&2
+  echo "ERROR: Could not find scripts/last30days.py in public plugin cache or repo checkout" >&2
-  echo "Searched: ~/.claude/plugins/cache/, ~/.codex/skills/, ~/.agents/skills/, ./skills/last30days, ./.skills/last30days, ." >&2
+  echo "Expected: $HOME/.claude/plugins/cache/last30days-skill/last30days/{VERSION}/scripts/last30days.py" >&2
  exit 1
 fi
 "${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 "$QUERY_PLAN_FILE"` (path to the file you just wrote)
 - `--x-handle={RESOLVED_HANDLE}` (from Step 0.5)
 - `--subreddits={RESOLVED_SUBREDDITS}` (from Step 0.55)
 - `--tiktok-hashtags={RESOLVED_HASHTAGS}` (from Step 0.55)
@@ -1577,33 +1497,6 @@ Close with `I have all the links to the {N} {source list} I pulled from. Just as
 ---
 ## SHAREABLE HTML BRIEF (when the user asked for one)
 **This section fires if EITHER trigger is true:**
 - `$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`.
@@ -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.
@@ -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/**/*"]
 }
@@ -1,10 +1,12 @@
 [project]
 name = "last30days-skill"
-version = "3.2.3"
+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 = [
@@ -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/*",
 ]
@@ -58,7 +58,7 @@ OpenClaw:
 clawhub install last30days-official
 ```
-OpenAI Codex CLI: install the repo as a local Codex marketplace/plugin. The plugin manifest lives at `.codex-plugin/plugin.json`, and the canonical skill payload is `skills/last30days/SKILL.md`.
+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.
@@ -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 [ $# -eq 0 ]; then
-  echo "Usage: bash skills/last30days/scripts/compare.sh <topic>"
+  echo "Usage: bash scripts/compare.sh <topic>"
-  echo "  Example: bash skills/last30days/scripts/compare.sh Kevin Rose"
+  echo "  Example: bash scripts/compare.sh Kevin Rose"
  exit 1
 fi
 TOPIC="$*"
@@ -22,8 +22,7 @@ from lib import env as envlib
 from lib import schema
-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"
@@ -308,10 +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:
@@ -41,7 +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()
@@ -91,46 +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,
 ) -> Path:
    from datetime import datetime
    path = Path(save_dir).expanduser().resolve()
    path.mkdir(parents=True, exist_ok=True)
    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 emit in {"json", "html"}:
+    # The saved file is the complete debug artifact.
-        content = emit_output(report, emit, synthesis_md=synthesis_md)
+    if emit == "json":
        content = emit_output(report, emit)
    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":
@@ -143,7 +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 = {
@@ -155,13 +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,
@@ -180,10 +156,9 @@ 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)
@@ -192,14 +167,6 @@ def compute_save_path_display(save_dir: str, topic: str, suffix: str, emit: str)
        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]:
    import store
@@ -226,7 +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")
@@ -234,7 +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)")
@@ -571,13 +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)
@@ -895,29 +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,
        )
        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).
@@ -926,7 +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()
@@ -7,11 +7,13 @@ See scripts/lib/vendor/bird-search/package.json for authoritative version.
 import json
 import os
 import signal
 import shutil
 import subprocess
 import sys
 from pathlib import Path
-from . import http, log, subproc
+from . import http, log
 from datetime import datetime
 from typing import Any, Dict, List, Optional, Tuple
@@ -166,51 +168,62 @@ def _run_bird_search(query: str, count: int, timeout: int) -> Dict[str, Any]:
        "--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 {"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 {"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
-    if result.returncode != 0:
+        if proc.returncode != 0:
-        error = result.stderr.strip() or "Bird search failed"
+            error = stderr.strip() if stderr else "Bird search failed"
-        return {"error": error, "items": []}
+            return {"error": error, "items": []}
-    output = result.stdout.strip()
+        output = stdout.strip() if stdout else ""
-    if not output:
+        if not output:
-        return {"items": []}
+            return {"items": []}
    try:
        parsed = json.loads(output)
        if isinstance(parsed, list):
            return {"items": parsed}
        return parsed
    except json.JSONDecodeError as e:
        return {"error": f"Invalid JSON response: {e}", "items": []}
-
+    except Exception as e:
-    if isinstance(parsed, list):
+        return {"error": str(e), "items": []}
        return {"items": parsed}
    return parsed
 def search_x(
@@ -317,29 +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
@@ -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)
@@ -372,6 +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.
@@ -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(
                {
@@ -12,6 +12,11 @@ import sys
 from datetime import datetime
 from typing import Any, Dict, List, Optional, Set
 try:
    import requests as _requests
 except ImportError:
    _requests = None
 from . import dates, http, log
 SCRAPECREATORS_BASE = "https://api.scrapecreators.com"
@@ -231,17 +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}")
@@ -275,17 +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 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}/v2/instagram/reels/search",
                params={"query": core_topic},
                headers=http.scrapecreators_headers(token),
                timeout=30,
            )
            resp.raise_for_status()
            data = resp.json()
        except Exception as e:
            _log(f"ScrapeCreators error: {e}")
            return {"items": [], "error": f"{type(e).__name__}: {e}"}
    # Items are in the 'reels' array (ScrapeCreators v2 response)
    raw_items = data.get("reels") or data.get("items") or data.get("data") or []
@@ -335,7 +367,7 @@ def fetch_captions(
    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
    max_captions = config["max_captions"]
-    if not video_items or not token:
+    if not video_items or not token or not _requests:
        return {}
    top_items = video_items[:max_captions]
@@ -360,24 +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=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 [
@@ -348,7 +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(
@@ -400,53 +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],
@@ -494,7 +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,
@@ -80,7 +78,6 @@ MOCK_AVAILABLE_SOURCES = [
    "github",
    "perplexity",
    "xquik",
    "digg",
 ]
@@ -108,8 +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):
@@ -505,12 +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
@@ -535,12 +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
@@ -976,13 +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), {}
@@ -1075,45 +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, []), {
@@ -67,7 +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"},
@@ -93,6 +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"
@@ -71,29 +71,8 @@ def _normalize_phrase(text: str) -> str:
    return ' '.join(re.sub(r'[^\w\s]', ' ', text.lower()).split())
 class PreparedQuery:
    """Precomputed query shape reused across items in a stream.
    Built once per ranking_query; reused by token_overlap_relevance so the
    per-item normalize/score loops don't re-tokenize the same query N times.
    """
    __slots__ = ("raw", "q_tokens", "informative_q_tokens", "normalized_phrase")
    def __init__(self, query: str) -> None:
        self.raw = query
        self.q_tokens = tokenize(query)
        informative = {t for t in self.q_tokens if t not in LOW_SIGNAL_QUERY_TOKENS}
        self.informative_q_tokens = informative or self.q_tokens
        self.normalized_phrase = _normalize_phrase(query)
 def _as_prepared(query: "str | PreparedQuery") -> PreparedQuery:
    return query if isinstance(query, PreparedQuery) else PreparedQuery(query)
 def token_overlap_relevance(
-    query: "str | PreparedQuery",
+    query: str,
    text: str,
    hashtags: Optional[List[str]] = None,
 ) -> float:
@@ -116,8 +95,7 @@ def token_overlap_relevance(
    Returns:
        Float between 0.0 and 1.0 (0.5 for empty queries)
    """
-    prepared = _as_prepared(query)
+    q_tokens = tokenize(query)
    q_tokens = prepared.q_tokens
    # Combine text and hashtags for matching
    combined = text
@@ -141,7 +119,9 @@ def token_overlap_relevance(
    if overlap == 0:
        return 0.0
-    informative_q_tokens = prepared.informative_q_tokens
+    informative_q_tokens = {t for t in q_tokens if t not in LOW_SIGNAL_QUERY_TOKENS}
    if not informative_q_tokens:
        informative_q_tokens = q_tokens
    coverage = overlap / len(q_tokens)
    informative_overlap = len(informative_q_tokens & t_tokens) / len(informative_q_tokens)
@@ -149,7 +129,7 @@ def token_overlap_relevance(
    precision = overlap / precision_denominator
    phrase_bonus = 0.0
-    normalized_query = prepared.normalized_phrase
+    normalized_query = _normalize_phrase(query)
    normalized_text = _normalize_phrase(combined)
    if normalized_query and normalized_query in normalized_text:
        phrase_bonus = 0.12 if len(normalized_query.split()) > 1 else 0.16
@@ -4,7 +4,6 @@ from __future__ import annotations
 import json
 import pathlib
 import re
 from collections import Counter
 from datetime import date
 from urllib.parse import urlparse
@@ -12,47 +11,21 @@ from urllib.parse import urlparse
 from . import dates, schema
 _VERSION_RE = re.compile(
    r'''^version:\s*(?:"([^"]+)"|'([^']+)'|(\S+))\s*$''',
    re.MULTILINE,
 )
 def _skill_version() -> str:
-    """Read plugin version from .claude-plugin/plugin.json, falling back to SKILL.md frontmatter.
+    """Read plugin version from .claude-plugin/plugin.json if available.
-    Per-harness skill install dirs (`~/.claude/skills`, `~/.codex/skills`, `~/.agents/skills`,
+    Tries nearest plugin.json by walking up from render.py's own location.
-    Hermes, etc.) do not always carry `.claude-plugin/plugin.json` — that file ships with
+    Falls back to "?" if not found. This keeps the badge emission from
-    plugin-cache installs but not with per-harness skill installs. SKILL.md frontmatter is
+    crashing on non-plugin-cache installs (repo checkout, Gemini, Codex).
    the fallback that keeps the badge from emitting v? on those installs. Returns "?" only
    if both sources are missing.
    A corrupt manifest at one ancestor does not shadow a valid manifest at a deeper one
    (continue, not break). YAML frontmatter accepts double-quoted, single-quoted, or
    unquoted version scalars.
    """
    here = pathlib.Path(__file__).resolve()
-    for parent in here.parents:
+    for parent in [here.parent, *here.parents]:
-        manifest = parent / ".claude-plugin" / "plugin.json"
+        candidate = parent / ".claude-plugin" / "plugin.json"
-        if manifest.is_file():
+        if candidate.is_file():
            try:
-                version = json.loads(manifest.read_text()).get("version")
+                return json.loads(candidate.read_text()).get("version", "?")
            except (json.JSONDecodeError, OSError):
-                continue
+                return "?"
            if version:
                return version
    # No usable manifest found at any ancestor — fall back to SKILL.md frontmatter.
    for parent in here.parents:
        skill_md = parent / "SKILL.md"
        if skill_md.is_file():
            try:
                match = _VERSION_RE.search(skill_md.read_text())
            except (OSError, UnicodeDecodeError):
                break
            if match:
                return next(g for g in match.groups() if g is not None)
            break
    return "?"
@@ -79,7 +52,6 @@ SOURCE_LABELS = {
    "xiaohongshu": "Xiaohongshu",
    "x": "X",
    "github": "GitHub",
    "digg": "Digg",
    "perplexity": "Perplexity",
 }
@@ -198,168 +170,6 @@ def render_compact(report: schema.Report, cluster_limit: int = 8, fun_level: str
    return "\n".join(lines).strip() + "\n"
 def render_for_html(
    report: schema.Report,
    synthesis_md: str | None = None,
    *,
    save_path: str | None = None,
 ) -> str:
    """Render markdown intended for shareable HTML conversion.
    This output keeps the public badge, compact source/date metadata, an
    optional one-line data quality note, optional synthesized brief markdown,
    and the engine footer. It deliberately omits the debug file header,
    model-facing safety note, and evidence scratchpad emitted by
    render_compact().
    When synthesis_md is None, the body is intentionally sparse: badge,
    metadata, optional data quality note, and engine footer only.
    """
    lines = [
        *_render_badge(),
        *_render_html_metadata(report),
    ]
    if synthesis_md:
        lines.extend(["", synthesis_md.strip()])
    # Data quality warnings are NOT rendered into the HTML artifact. The HTML
    # is meant to be shared (Slack, email, Notion); recipients haven't asked
    # for technical commentary about how the run was produced. Generators see
    # the same warnings via collect_html_warnings() routed to stderr by the
    # CLI, so they can fix quality issues before sharing.
    _append_html_footer(lines, report, save_path)
    return "\n".join(lines).strip() + "\n"
 def render_for_html_comparison(
    entity_reports: list[tuple[str, schema.Report]],
    synthesis_md: str | None = None,
    *,
    save_path: str | None = None,
 ) -> str:
    """Render comparison markdown intended for shareable HTML conversion.
    Same semantics as render_for_html(), but metadata and data quality notes
    are aggregated across the compared entities.
    """
    if not entity_reports:
        raise ValueError("render_for_html_comparison requires at least one report")
    entities = [label for label, _ in entity_reports]
    main_report = entity_reports[0][1]
    meta = (
        f"<!-- META: {main_report.range_from} to {main_report.range_to} "
        f"· comparing {len(entities)}: {', '.join(entities)} -->"
    )
    lines = [
        *_render_badge(),
        meta,
    ]
    if synthesis_md:
        lines.extend(["", synthesis_md.strip()])
    # Comparison data quality notes also go to stderr, not into the artifact.
    _append_html_footer(lines, main_report, save_path)
    return "\n".join(lines).strip() + "\n"
 def collect_html_warnings(report: schema.Report) -> list[str]:
    """Collect data quality warnings for stderr output (NOT for the HTML artifact).
    Returns a list of human-readable warning strings. Empty list if the run
    was clean. Used by the CLI to emit diagnostics to stderr after writing
    the HTML to stdout/file.
    """
    notes: list[str] = []
    if _render_degraded_run_warning(report):
        notes.append("Run was missing pre-flight resolution. Re-run with `--plan` for richer results.")
    elif _render_pre_research_warning(report):
        notes.append("Pre-research was skipped, so results may be thinner than a resolved run.")
    freshness_warning = _assess_data_freshness(report)
    if freshness_warning:
        notes.append(freshness_warning)
    notes.extend(report.warnings)
    return _dedupe_notes(notes)
 def collect_html_warnings_comparison(
    entity_reports: list[tuple[str, schema.Report]],
 ) -> list[str]:
    """Collect comparison-mode warnings, prefixed by entity label."""
    notes: list[str] = []
    for label, report in entity_reports:
        for w in collect_html_warnings(report):
            notes.append(f"{label}: {w}")
    return notes
 def _render_html_metadata(report: schema.Report) -> list[str]:
    """Inline metadata as an HTML comment marker.
    html_render.py post-processes ``<!-- META: ... -->`` markers into a
    ``<div class="meta">`` after markdown conversion, so the metadata escapes
    the markdown converter's HTML-escaping pass cleanly. Same pattern as the
    PASS_THROUGH_FOOTER marker used for the engine tree.
    """
    non_empty = [s for s, items in sorted(report.items_by_source.items()) if items]
    if non_empty:
        sources = ", ".join(_source_label(s) for s in non_empty)
    else:
        sources = "no active sources"
    return [
        f"<!-- META: {report.range_from} to {report.range_to} · {sources} -->",
    ]
 def _render_html_data_quality_note(report: schema.Report) -> str | None:
    notes: list[str] = []
    degraded_warning = _render_degraded_run_warning(report)
    if degraded_warning:
        notes.append("This run was missing pre-flight resolution. Re-run with `--plan` for richer results.")
    pre_research_warning = _render_pre_research_warning(report)
    if pre_research_warning and not degraded_warning:
        notes.append("Pre-research was skipped, so results may be thinner than a resolved run.")
    freshness_warning = _assess_data_freshness(report)
    if freshness_warning:
        notes.append(freshness_warning)
    notes.extend(report.warnings)
    if not notes:
        return None
    return f"> **Data quality note:** {' '.join(_dedupe_notes(notes))}"
 def _render_html_comparison_data_quality_note(
    entity_reports: list[tuple[str, schema.Report]],
 ) -> str | None:
    notes: list[str] = []
    for label, report in entity_reports:
        note = _render_html_data_quality_note(report)
        if note:
            clean = note.removeprefix("> **Data quality note:** ").strip()
            notes.append(f"{label}: {clean}")
    if not notes:
        return None
    return f"> **Data quality note:** {' '.join(_dedupe_notes(notes))}"
 def _dedupe_notes(notes: list[str]) -> list[str]:
    out: list[str] = []
    seen: set[str] = set()
    for note in notes:
        normalized = " ".join(str(note).split())
        if not normalized or normalized in seen:
            continue
        seen.add(normalized)
        out.append(normalized)
    return out
 def _append_html_footer(lines: list[str], report: schema.Report, save_path: str | None) -> None:
    footer = _render_emoji_footer(report, save_path)
    lines.append("")
    lines.append("<!-- PASS-THROUGH FOOTER: emit verbatim in the model response per LAW 5. -->")
    lines.extend(footer)
    lines.append("<!-- END PASS-THROUGH FOOTER -->")
 def _render_canonical_boundary() -> list[str]:
    """Emit the explicit END-OF-CANONICAL-OUTPUT boundary.
@@ -853,7 +663,7 @@ def render_full(report: schema.Report) -> str:
    lines.append("## All Items by Source")
    lines.append("")
    source_order = ["reddit", "x", "youtube", "tiktok", "instagram", "threads", "pinterest",
-                    "hackernews", "bluesky", "truthsocial", "polymarket", "grounding", "xiaohongshu", "github", "digg", "perplexity"]
+                    "hackernews", "bluesky", "truthsocial", "polymarket", "grounding", "xiaohongshu", "github", "perplexity"]
    for source in source_order:
        items = report.items_by_source.get(source, [])
        if not items:
@@ -879,9 +689,6 @@ def render_full(report: schema.Report) -> str:
                    tc_score = tc.get("score", "")
                    attribution = _comment_attribution(item.source, tc.get("author"))
                    lines.append(f"  Top comment {attribution} ({tc_score} {vote_label}): {excerpt}")
            # Digg: inline X-post quotes attached to the cluster.
            for post in _digg_posts_for(item, limit=3):
                lines.append(f"  > {_format_digg_quote(post)}")
            # Comment insights for Reddit
            insights = item.metadata.get("comment_insights", [])
            if insights:
@@ -1003,8 +810,6 @@ def _render_candidate(candidate: schema.Candidate, prefix: str) -> list[str]:
        source = primary.source if primary else None
        attribution = _comment_attribution(source, tc.get("author"))
        lines.append(f"   - {attribution} ({score} {vote_label}): {_truncate(excerpt.strip(), 240)}")
    for post in _digg_posts_for(primary):
        lines.append(f"   - {_format_digg_quote(post)}")
    insight = _comment_insight(primary)
    if insight:
        lines.append(f"   - Insight: {_truncate(insight, 220)}")
@@ -1255,7 +1060,6 @@ _FOOTER_SOURCES: list[tuple[str, str, str, str, list[tuple[str, str]]]] = [
    ("bluesky",     "🦋", "Bluesky",      "post",     [("likes", "likes"), ("reposts", "reposts")]),
    ("truthsocial", "🇺🇸", "Truth Social", "post",     [("likes", "likes"), ("reposts", "reposts")]),
    ("github",      "🐙", "GitHub",       "item",     [("reactions", "reactions"), ("comments", "comments")]),
    ("digg",        "⛏️", "Digg",         "cluster",  [("postCount", "posts"), ("uniqueAuthors", "authors")]),
 ]
@@ -1513,7 +1317,6 @@ ENGAGEMENT_DISPLAY: dict[str, list[tuple[str, str]]] = {
    "polymarket":   [],
    "github":       [("reactions", "react"), ("comments", "cmt")],
    "perplexity":   [("citations", "cite")],
    "digg":         [("postCount", "posts"), ("uniqueAuthors", "auth")],
 }
@@ -1701,6 +1504,16 @@ def _top_comments_list(item: schema.SourceItem | None, limit: int = 3, min_score
    return [c for c in comments if (c.get("score") or 0) >= min_score][:limit]
 def _top_comment_excerpt(item: schema.SourceItem | None) -> str | None:
    if not item:
        return None
    comments = item.metadata.get("top_comments") or []
    if not comments or not isinstance(comments[0], dict):
        return None
    top = comments[0]
    return str(top.get("excerpt") or top.get("text") or "").strip() or None
 def _comment_insight(item: schema.SourceItem | None) -> str | None:
    if not item:
        return None
@@ -1710,39 +1523,6 @@ def _comment_insight(item: schema.SourceItem | None) -> str | None:
    return str(insights[0]).strip() or None
 def _digg_posts_for(item: schema.SourceItem | None, limit: int = 3) -> list[dict]:
    """Return up to `limit` parsed Digg posts attached as enrichment to a cluster.
    Returns an empty list for non-digg sources or clusters without enrichment.
    """
    if not item or item.source != "digg":
        return []
    posts = item.metadata.get("posts") or []
    if not isinstance(posts, list):
        return []
    out: list[dict] = []
    for entry in posts:
        if isinstance(entry, dict) and entry.get("body") and entry.get("username"):
            out.append(entry)
        if len(out) >= limit:
            break
    return out
 def _format_digg_quote(post: dict, body_limit: int = 200) -> str:
    """Format a Digg-attached X post as an inline 'via Digg' quote line."""
    handle = post.get("username") or ""
    x_url = post.get("x_url") or ""
    body = (post.get("body") or "").replace("\n", " ").strip()
    if len(body) > body_limit:
        body = body[: body_limit - 1].rstrip() + "…"
    if x_url and handle:
        return f"[@{handle}]({x_url}) via Digg: {body}"
    if handle:
        return f"@{handle} via Digg: {body}"
    return f"via Digg: {body}"
 def _transcript_highlights(item: schema.SourceItem | None) -> list[str]:
    if not item or item.source != "youtube":
        return []
@@ -12,7 +12,6 @@ SOURCE_QUALITY = {
    "xiaohongshu": 0.7,
    "hackernews": 0.8,
    "youtube": 0.85,
    "digg": 0.85,
    "reddit": 0.6,
    "x": 0.68,
    "bluesky": 0.66,
@@ -27,10 +26,7 @@ def source_quality(source: str) -> float:
    return SOURCE_QUALITY.get(source, 0.6)
-def local_relevance(
+def local_relevance(item: schema.SourceItem, ranking_query: str) -> float:
    item: schema.SourceItem,
    ranking_query: "str | relevance.PreparedQuery",
 ) -> float:
    text = "\n".join(
        part
        for part in [item.title, item.body, item.snippet]
@@ -96,7 +92,6 @@ ENGAGEMENT_WEIGHTS: dict[str, list[tuple[str, float]]] = {
    "bluesky":      [("likes", 0.40), ("reposts", 0.30), ("replies", 0.20), ("quotes", 0.10)],
    "truthsocial":  [("likes", 0.45), ("reposts", 0.30), ("replies", 0.25)],
    "polymarket":   [("volume", 0.60), ("liquidity", 0.40)],
    "digg":         [("postCount", 0.40), ("uniqueAuthors", 0.30), ("rank_score", 0.30)],
 }
@@ -180,14 +175,13 @@ def normalize(values: list[float | None]) -> list[int | None]:
 def annotate_stream(
    items: list[schema.SourceItem],
-    ranking_query: "str | relevance.PreparedQuery",
+    ranking_query: str,
    freshness_mode: str,
 ) -> list[schema.SourceItem]:
    """Attach local scoring metadata and return items sorted by local_rank_score."""
    prepared_query = ranking_query if isinstance(ranking_query, relevance.PreparedQuery) else relevance.PreparedQuery(ranking_query)
    engagement_scores = normalize([engagement_raw(item) for item in items])
    for item, eng_score in zip(items, engagement_scores, strict=True):
-        item.local_relevance = local_relevance(item, prepared_query)
+        item.local_relevance = local_relevance(item, ranking_query)
        item.freshness = freshness(item, freshness_mode)
        item.engagement_score = eng_score
        item.source_quality = source_quality(item.source)
@@ -26,7 +26,7 @@ def _windows(words: list[str], size: int, overlap: int) -> list[str]:
 def extract_best_snippet(
    item: schema.SourceItem,
-    ranking_query: "str | relevance.PreparedQuery",
+    ranking_query: str,
    max_words: int = 120,
 ) -> str:
    """Prefer existing snippets, else extract the best matching evidence window."""
@@ -43,9 +43,8 @@ def extract_best_snippet(
    if not candidates:
        return _truncate_words(body, max_words)
    prepared_query = ranking_query if isinstance(ranking_query, relevance.PreparedQuery) else relevance.PreparedQuery(ranking_query)
    best = max(
        candidates,
-        key=lambda candidate: relevance.token_overlap_relevance(prepared_query, candidate),
+        key=lambda candidate: relevance.token_overlap_relevance(ranking_query, candidate),
    )
    return _truncate_words(best, max_words)
@@ -152,16 +152,35 @@ def search_threads(
    _log(f"Searching for '{core_topic}' (depth={depth}, limit={config['results']})")
    try:
-        data = http.get(
+        import requests as _requests
-            f"{SCRAPECREATORS_BASE}/search",
+    except ImportError:
-            params={"keyword": core_topic},
+        _requests = None
-            headers=http.scrapecreators_headers(token),
+
-            timeout=30,
+    if not _requests:
-            retries=2,
+        _log("requests library not installed, falling back to urllib")
-        )
+        try:
-    except Exception as e:
+            from urllib.parse import urlencode
-        _log(f"ScrapeCreators error: {e}")
+            params = urlencode({"keyword": core_topic})
-        return {"items": [], "error": f"{type(e).__name__}: {e}"}
+            url = f"{SCRAPECREATORS_BASE}/search?{params}"
            headers = http.scrapecreators_headers(token)
            headers["User-Agent"] = http.USER_AGENT
            data = http.get(url, headers=headers, timeout=30, retries=2)
        except Exception as e:
            _log(f"ScrapeCreators error (urllib): {e}")
            return {"items": [], "error": f"{type(e).__name__}: {e}"}
    else:
        try:
            resp = _requests.get(
                f"{SCRAPECREATORS_BASE}/search",
                params={"keyword": core_topic},
                headers=http.scrapecreators_headers(token),
                timeout=30,
            )
            resp.raise_for_status()
            data = resp.json()
        except Exception as e:
            _log(f"ScrapeCreators error: {e}")
            return {"items": [], "error": f"{type(e).__name__}: {e}"}
    # Extract items from response (try common SC response shapes)
    raw_items = (
@@ -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/tiktok"
@@ -209,17 +214,30 @@ def _hashtag_search(
        List of raw TikTok item dicts (aweme_info format).
    """
    _log(f"Hashtag search: #{hashtag}")
-    try:
+    if not _requests:
-        data = http.get(
+        try:
-            f"{SCRAPECREATORS_BASE}/search/hashtag",
+            from urllib.parse import urlencode
-            params={"hashtag": hashtag},
+            params = urlencode({"hashtag": hashtag})
-            headers=http.scrapecreators_headers(token),
+            url = f"{SCRAPECREATORS_BASE}/search/hashtag?{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"Hashtag search error for #{hashtag}: {e}")
+            _log(f"Hashtag search error (urllib) for #{hashtag}: {e}")
-        return []
+            return []
    else:
        try:
            resp = _requests.get(
                f"{SCRAPECREATORS_BASE}/search/hashtag",
                params={"hashtag": hashtag},
                headers=http.scrapecreators_headers(token),
                timeout=30,
            )
            resp.raise_for_status()
            data = resp.json()
        except Exception as e:
            _log(f"Hashtag search error for #{hashtag}: {e}")
            return []
    raw_items = data.get("aweme_list") or data.get("data") or []
    _log(f"  -> {len(raw_items)} results for #{hashtag}")
@@ -243,17 +261,30 @@ def _profile_videos(
    """
    _log(f"Profile videos: @{handle}")
    profile_url = "https://api.scrapecreators.com/v3/tiktok/profile/videos"
-    try:
+    if not _requests:
-        data = http.get(
+        try:
-            profile_url,
+            from urllib.parse import urlencode
-            params={"handle": handle, "sort_by": "latest"},
+            params = urlencode({"handle": handle, "sort_by": "latest"})
-            headers=http.scrapecreators_headers(token),
+            url = f"{profile_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"Profile videos error for @{handle}: {e}")
+            _log(f"Profile videos error (urllib) for @{handle}: {e}")
-        return []
+            return []
    else:
        try:
            resp = _requests.get(
                profile_url,
                params={"handle": handle, "sort_by": "latest"},
                headers=http.scrapecreators_headers(token),
                timeout=30,
            )
            resp.raise_for_status()
            data = resp.json()
        except Exception as e:
            _log(f"Profile videos error for @{handle}: {e}")
            return []
    raw_items = data.get("aweme_list") or data.get("data") or []
    _log(f"  -> {len(raw_items)} videos from @{handle}")
@@ -287,17 +318,31 @@ def search_tiktok(
    _log(f"Searching TikTok 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/keyword",
+        try:
-            params={"query": core_topic, "sort_by": "relevance"},
+            from urllib.parse import urlencode
-            headers=http.scrapecreators_headers(token),
+            params = urlencode({"query": core_topic, "sort_by": "relevance"})
-            timeout=30,
+            url = f"{SCRAPECREATORS_BASE}/search/keyword?{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/keyword",
                params={"query": core_topic, "sort_by": "relevance"},
                headers=http.scrapecreators_headers(token),
                timeout=30,
            )
            resp.raise_for_status()
            data = resp.json()
        except Exception as e:
            _log(f"ScrapeCreators error: {e}")
            return {"items": [], "error": f"{type(e).__name__}: {e}"}
    # Items are nested under aweme_info
    raw_entries = data.get("search_item_list") or data.get("data") or []
@@ -352,7 +397,7 @@ def fetch_captions(
    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
    max_captions = config["max_captions"]
-    if not video_items or not token:
+    if not video_items or not token or not _requests:
        return {}
    top_items = video_items[:max_captions]
@@ -377,23 +422,24 @@ def fetch_captions(
        if not url:
            continue
        try:
-            data = http.get(
+            resp = _requests.get(
                f"{SCRAPECREATORS_BASE}/video/transcript",
                params={"url": url},
                headers=http.scrapecreators_headers(token),
                timeout=15,
                retries=1,
            )
-            transcript = data.get("transcript")
+            if resp.status_code == 200:
-            if transcript:
+                data = resp.json()
-                if isinstance(transcript, list):
+                transcript = data.get("transcript")
                    transcript = " ".join(str(s) for s in transcript)
                transcript = _clean_webvtt(transcript)
                if transcript:
-                    words = transcript.split()
+                    if isinstance(transcript, list):
-                    if len(words) > CAPTION_MAX_WORDS:
+                        transcript = " ".join(str(s) for s in transcript)
-                        transcript = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
+                    transcript = _clean_webvtt(transcript)
-                    captions[vid] = transcript
+                    if transcript:
                        words = transcript.split()
                        if len(words) > CAPTION_MAX_WORDS:
                            transcript = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
                        captions[vid] = transcript
        except Exception as e:
            _log(f"Transcript fetch failed for {vid}: {e}")
@@ -574,17 +620,30 @@ def _fetch_post_comments(
        List of comment dicts with author, text, digg_count (likes), date.
        Empty list on any error — comment failures never crash the pipeline.
    """
-    try:
+    if not _requests:
-        data = http.get(
+        try:
-            f"{SCRAPECREATORS_BASE}/video/comments",
+            from urllib.parse import urlencode
-            params={"url": post_url, "trim": "true"},
+            params = urlencode({"url": post_url, "trim": "true"})
-            headers=http.scrapecreators_headers(token),
+            url = f"{SCRAPECREATORS_BASE}/video/comments?{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 exc:
+        except Exception as exc:
-        _log(f"Comment fetch error for {post_url}: {exc}")
+            _log(f"Comment fetch error (urllib) for {post_url}: {exc}")
-        return []
+            return []
    else:
        try:
            resp = _requests.get(
                f"{SCRAPECREATORS_BASE}/video/comments",
                params={"url": post_url, "trim": "true"},
                headers=http.scrapecreators_headers(token),
                timeout=30,
            )
            resp.raise_for_status()
            data = resp.json()
        except Exception as exc:
            _log(f"Comment fetch error for {post_url}: {exc}")
            return []
    raw_comments = data.get("comments") or data.get("data") or []
    # Sort by digg_count desc so normalize sees the highest-signal first.
@@ -124,7 +124,6 @@ SOURCE_COMPLETION_ORDER = [
    "polymarket",
    "grounding",
    "xiaohongshu",
    "digg",
 ]
 SOURCE_COMPLETION_META = {
@@ -139,7 +138,6 @@ SOURCE_COMPLETION_META = {
    "polymarket": ("Polymarket", "market", "markets", Colors.GREEN),
    "grounding": ("Web", "result", "results", Colors.GREEN),
    "xiaohongshu": ("Xiaohongshu", "post", "posts", Colors.RED),
    "digg": ("Digg", "cluster", "clusters", Colors.YELLOW),
 }
--- a/Show More
+++ b/Show More