Video Analyzer Skill

鏅鸿兘鍒嗘瀽 Bilibili銆乊ouTube 鎴栨湰鍦拌棰戯紝鐢熸垚杞啓銆佽瘎浼板拰鎬荤粨銆傛敮鎸佸叧閿抚鎴浘鑷姩宓屽叆銆?

When to Use This Skill

褰撶敤鎴锋彁鍒颁互涓嬪唴瀹规椂婵€娲绘鎶€鑳斤細

• "鍒嗘瀽瑙嗛"
• "杞啓瑙嗛"
• "鎬荤粨瑙嗛鍐呭"
• "璇勪及杩欎釜瑙嗛"
• "瑙嗛鍐呭鍒嗘瀽"
• "鎻愬彇瑙嗛鏂囧瓧"

How It Works

姝ゆ妧鑳戒細锛?

1. 涓嬭浇瑙嗛锛氭敮鎸?Bilibili銆乊ouTube 鎴栨湰鍦版枃浠?
2. 璇煶杞啓锛氫娇鐢?Whisper AI 妯″瀷杩涜楂樼簿搴﹁浆鍐?
3. **鍏抽敭甯ф彁鍙?*锛氭櫤鑳介€夋嫨鍏抽敭鑺傜偣骞舵彁鍙栨埅鍥撅紙榛樿鍚敤锛?
4. 鍐呭鍒嗘瀽锛氫娇鐢?LLM 杩涜鍐呭璇勪及銆佹€荤粨銆佹牸寮忓寲
5. 缁撴灉淇濆瓨锛氱敓鎴?Markdown 鏍煎紡鐨勫垎鏋愭姤鍛?

Usage

鍩虹鐢ㄦ硶

python .claude/skills/video-analyzer/run.py --url "<VIDEO_URL>"

甯哥敤鍙傛暟

• --url: 瑙嗛閾炬帴鎴栨湰鍦版枃浠惰矾寰勶紙蹇呭～锛?
• --whisper-model: Whisper 妯″瀷锛堥粯璁? large-v2锛?
  • 鍙€? tiny, base, small, medium, large-v2, large-v3, turbo
• --analysis-types: 鍒嗘瀽绫诲瀷锛堥粯璁? evaluation,summary锛?
  • 鍙€? evaluation, summary, format
• --output-dir: 杈撳嚭鐩綍锛堥粯璁? ./video-analysis锛?
• --summary-style: 鎬荤粨椋庢牸锛堝彲閫? concise, deep, social, study锛?
• --no-screenshots: 绂佺敤鍏抽敭甯ф埅鍥?

绀轰緥

# 鍩虹鍒嗘瀽
python run.py --url "https://www.bilibili.com/video/BV1xx411c7mD"

# 蹇€熻浆鍐欙紙灏忔ā鍨嬶級
python run.py --url "https://youtu.be/xxx" --whisper-model small

# 鍙仛鎬荤粨
python run.py --url "./video.mp4" --analysis-types summary

Configuration

棣栨浣跨敤闇€瑕侀厤缃?API key锛?

1. 澶嶅埗 config.example.json 涓?config.json
2. 濉叆浣犵殑 API key锛?

{
  "llm": {
    "provider": "openai",
    "api_key": "your-api-key",
    "base_url": "https://api.openai.com/v1",
    "model": "gpt-4o-mini"
  }
}

鏀寔鐨勬彁渚涘晢锛?

• OpenAI: gpt-4o-mini, gpt-4o
• Anthropic: claude-3-5-sonnet-20241022
• **鍏煎 OpenAI API 鐨勬湇鍔?*: 濡?Gemini銆丏eepSeek 绛?

Dependencies

绯荤粺渚濊禆

• FFmpeg锛堝繀闇€锛? 瑙嗛澶勭悊
  • Windows: winget install ffmpeg
  • macOS: brew install ffmpeg
  • Linux: sudo apt install ffmpeg

Python 渚濊禆

棣栨杩愯浼氳嚜鍔ㄦ鏌ュ苟瀹夎锛?

• yt-dlp - 瑙嗛涓嬭浇
• faster-whisper - 璇煶杞啓
• openai / anthropic - LLM API
• 鍏朵粬渚濊禆瑙?requirements.txt

Features

• 馃幀 澶氬钩鍙版敮鎸侊紙B绔欍€乊ouTube銆佹湰鍦版枃浠讹級
• 馃帳 楂樼簿搴﹁浆鍐欙紙Whisper AI锛?
• 馃 鏅鸿兘鍒嗘瀽锛堝唴瀹硅瘎浼般€佹€荤粨锛?
• 馃摳 鍏抽敭甯ф埅鍥撅紙鑷姩鎻愬彇骞跺祵鍏ワ級
• 馃寪 澶氳瑷€鏀寔锛堜腑鑻辨枃绛夛級

Troubleshooting

Q: 鎻愮ず缂哄皯 FFmpeg A: 瀹夎 FFmpeg锛堣涓婃柟绯荤粺渚濊禆锛?

Q: API 璋冪敤澶辫触 A: 妫€鏌?config.json 涓殑 API key 鏄惁姝ｇ‘

Q: 瑙嗛涓嬭浇澶辫触 A: 妫€鏌ョ綉缁滆繛鎺ワ紝鎴栦娇鐢ㄦ湰鍦拌棰戞枃浠?

*Q: 鎴浘鍔熻兘涓嶅伐浣? A: 纭繚 FFmpeg 宸插畨瑁咃紝涓斾娇鐢ㄧ殑鏄棰?URL锛堜笉鏄函闊抽锛?

Notes

• 棣栨杩愯浼氫笅杞?Whisper 妯″瀷锛堢害 3GB锛屼娇鐢ㄥ浗鍐呴暅鍍忥級
• 瑙嗛涓存椂涓嬭浇鍒扮郴缁熶复鏃剁洰褰曪紝鍒嗘瀽瀹屾垚鍚庤嚜鍔ㄦ竻鐞?
• 澶фā鍨嬶紙large-v2锛夌簿搴﹂珮浣嗛€熷害鎱紝灏忔ā鍨嬶紙small锛夐€熷害蹇絾绮惧害杈冧綆
• 鍚敤鎴浘闇€瑕佷笅杞藉畬鏁磋棰戯紙鑰岄潪浠呴煶棰戯級

Code-level Feishu Publishing (Required)

Do not store Feishu app credentials in this skill config. Publishing is handled by built-in Python flow (feishu_publisher.py) after analysis.

After analysis succeeds:

1. Use video_title as Feishu doc title exactly.
2. Merge all generated content into one markdown body (summary + evaluation + transcript).
3. Create a wiki docx node under configured space_id + parent_node_token.
4. Write the full markdown body into the created doc token.
5. Return publish result (doc_token/doc_url) in output field feishu_publish.

Credential/target resolution priority:

• feishu_space_id / feishu_parent_node_token parameters
• FEISHU_SPACE_ID / FEISHU_PARENT_NODE_TOKEN env
• config.json -> feishu.space_id / feishu.parent_node_token

App credentials are loaded from:

• FEISHU_APP_ID / FEISHU_APP_SECRET env, or
• OpenClaw openclaw.json -> channels.feishu

If publish fails, keep analysis result and return feishu_publish.success=false with error details.