{
  "started_at": "2026-05-15T12:33:50.080217+00:00",
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  "consolidated": [
    {
      "name": "ai-video-broadcast",
      "into": "youtube-channel-research",
      "source": "absorbed_into (model-declared at delete)",
      "reason": "Both skills covered the AI video production pipeline (script → TTS → Pillow → ffmpeg); ai-video-broadcast's technical depth (rendering, avatar animation, encoding, pitfalls) and support files (news-video-sourcing.md, server-environment-notes.md, youtube-news-market.md, gen_broadcast_video.py) were absorbed as Part 3 and additional references under the youtube-channel-research umbrella."
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  "llm_final": "---\n\n## Human Summary\n\n### Clusters Identified\n\nWith only 3 agent-created skills, there was one obvious cluster and one standalone:\n\n**Cluster: Video Production & YouTube Content (2 skills)**\n- `ai-video-broadcast` — Technical pipeline for AI-generated broadcast videos (script → TTS → Pillow frames → ffmpeg)\n- `youtube-channel-research` — YouTube market research + content strategy + (duplicated) video production notes\n\nThese two skills had massive overlap in the video production domain. `youtube-channel-research` already contained a `video-generation-notes.md` reference file that covered the same pipeline basics as `ai-video-broadcast`'s SKILL.md, plus the `ai-video-broadcast` SKILL.md had the deep technical content (avatar animation, visual layout, encoding settings, 18 pitfalls) that `youtube-channel-research` lacked.\n\n**Standalone: Hermes Deployment (1 skill)**\n- `hermes-web-dashboard` — Web dashboard, Nginx reverse proxy, API server, QQ bot, systemd services. Completely distinct domain (infrastructure/deployment) with zero overlap.\n\n### Consolidation Performed\n\n**Merged `ai-video-broadcast` → `youtube-channel-research`** (Method A: merge into existing umbrella)\n\n1. **Copied 3 unique reference files** from `ai-video-broadcast` into `youtube-channel-research/references/`:\n   - `news-video-sourcing.md` — Working CDN sources (AP News, India Today), non-working sources, transcoding pipeline\n   - `server-environment-notes.md` — Server specs, what works/doesn't, hybrid workflow\n   - `youtube-news-market.md` — ytInitialData extraction method, niche opportunities, CPM benchmarks, 2026 AI policy\n\n2. **Copied 1 script file** into `youtube-channel-research/scripts/`:\n   - `gen_broadcast_video.py` — Production-ready 1080p frame renderer with animated avatar\n\n3. **Patched `youtube-channel-research` SKILL.md** with:\n   - Updated description to reflect broader scope (video production added)\n   - New \"Part 3: AI Broadcast Video Production\" section with full pipeline details, Pillow rendering architecture, avatar drawing, encoding commands, production upgrade path, compositing patterns, and 10 additional pitfalls\n   - Updated References section listing all 15 support files (removed 2 broken references to files that never existed)\n\n4. **Archived `ai-video-broadcast`** to `~/.hermes/skills/.archive/ai-video-broadcast/` (recoverable)\n\n### Decision: Keep `hermes-web-dashboard`\n\nThis skill is a class-level umbrella for Hermes deployment infrastructure. It has no overlap with video production or market research. It stays.\n\n---\n\n## Structured summary (required)\n\n```yaml\nconsolidations:\n  - from: ai-video-broadcast\n    into: youtube-channel-research\n    reason: Both skills covered the AI video production pipeline (script → TTS → Pillow → ffmpeg); ai-video-broadcast's technical depth (rendering, avatar animation, encoding, pitfalls) and support files (news-video-sourcing.md, server-environment-notes.md, youtube-news-market.md, gen_broadcast_video.py) were absorbed as Part 3 and additional references under the youtube-channel-research umbrella.\nprunings: []\n```",
  "llm_summary": "---\n\n## Human Summary\n\n### Clusters Identified\n\nWith only 3 agent-created skills, there was one obvious cluster and one standalone:\n\n**Cluster: Video Production & YouTube Content (2 skills)**\n- `ai-video-broadcast` — Technical pipeline for …",
  "llm_error": null,
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      "name": "skill_view",
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      "arguments": "{\"mode\": \"replace\", \"path\": \"/root/.hermes/skills/devops/youtube-channel-research/SKILL.md\", \"old_string\": \"**张哥亲身经历的有效观看时长案例（重要参考）：**\\n- 张哥的音乐频道：总播放4000小时 → 有效观看时长仅2600小时\\n- 原因：音乐视频观众听个开头就走，有效比率约65%\\n- **启示**：叙事类/故事类内容有效比率远高于音乐类，观众会看完\\n- 灵异/怪谈故事属于高留存内容，有效比率预计80%+\", \"new_string\": \"**张哥亲身经历的有效观看时长案例（重要参考）：**\\n- 张哥的音乐频道：总播放4000小时 → 有效观看时长仅2600小时\\n- 原因：音乐视频观众听个开头就走，有效比率约65%\\n- **启示**：叙事类/故事类内容有效比率远高于…"
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      "arguments": "{\"mode\": \"replace\", \"new_string\": \"---\\nname: youtube-channel-research\\ndescription: YouTube channel market research, competitor analysis, and AI-generated video content production — from niche discovery to broadcast-ready video output. Covers YouTube/B站 niche analysis, content strategy, AI video generation pipeline (Pillow + ffmpeg), news footage sourcing, and platform policy.\\n---\\n\\n# YouTube C…"
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      "arguments": "{\"mode\": \"replace\", \"new_string\": \"## References\\n\\nSee `references/youtube-niche-analysis-2026-05-11.md` for the latest YouTube niche research data.\\nSee `references/youtube-niche-analysis.md` for the consolidated niche analysis cheat sheet.\\nSee `references/youtube-niche-cpm-analysis-2026-05-13.md` for detailed CPM comparison across YouTube niches (tech, finance, paranormal, etc.) and feasibilit…"
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    {
      "name": "terminal",
      "arguments": "{\"command\": \"mkdir -p ~/.hermes/skills/.archive && mv ~/.hermes/skills/creative/ai-video-broadcast ~/.hermes/skills/.archive/ai-video-broadcast && echo \\\"Archived ai-video-broadcast\\\" && ls ~/.hermes/skills/.archive/\"}"
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      "name": "terminal",
      "arguments": "{\"command\": \"echo \\\"=== Agent-created skills (active) ===\\\" && ls -d ~/.hermes/skills/devops/*/ ~/.hermes/skills/creative/*/ 2>/dev/null && echo \\\"=== Archived skills ===\\\" && ls -d ~/.hermes/skills/.archive/*/ 2>/dev/null\"}"
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}