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video-analyzer

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TikTok/YouTube動画を分析してテンプレート化するスキル。 動画ダウンロード→フレーム抽出→STT→構成分析→テンプレートJSON生成。 競合分析、人気動画の構成学習に使用。 「TikTok分析」「YouTube分析」「動画テンプレート化」「競合動画分析」等で発動。

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Video Analyzer

TikTok/YouTube/Instagram動画をダウンロード→分析→テンプレート化する。

対応プラットフォーム

  • TikTok — https://www.tiktok.com/@user/video/... / https://vt.tiktok.com/...
  • YouTube — https://www.youtube.com/watch?v=... / https://youtu.be/...
  • YouTube Shorts — https://youtube.com/shorts/...
  • Instagram Reels — https://www.instagram.com/reel/...
  • その他 yt-dlp が対応するプラットフォーム

クイックスタート

# TikTok動画を分析
python skills/video-analyzer/scripts/analyze_video.py \
  --url "https://www.tiktok.com/@user/video/123456" \
  --output output/templates/

# YouTube動画を分析
python skills/video-analyzer/scripts/analyze_video.py \
  --url "https://www.youtube.com/watch?v=XXXXX" \
  --output output/templates/

# YouTube Shortsを分析
python skills/video-analyzer/scripts/analyze_video.py \
  --url "https://youtube.com/shorts/XXXXX" \
  --output output/templates/

パイプライン

URL → yt-dlp → 動画ファイル（TikTok/YouTube/Instagram等対応）
  → ffmpeg → フレーム抽出 (1fps)
  → Whisper API → STT (テキスト + タイムスタンプ)
  → Vision AI → フレーム分析 (テロップ位置、デザイン、構図)
  → テンプレート JSON (scenes.json互換)

出力: template.json

{
  "source_url": "https://...",
  "duration": 32.5,
  "resolution": "1080x1920",
  "scenes": [
    {
      "frame_number": 1,
      "timestamp": "0:00-0:03",
      "duration": 3.0,
      "narration": "STTから抽出したテキスト",
      "text_overlay": {
        "text": "画面上のテロップ",
        "position": "center",
        "style": "bold",
        "color": "#FFFFFF",
        "has_stroke": true
      },
      "visual": {
        "shot_type": "close_up | medium | wide | overhead",
        "subject": "人物が商品を持っている",
        "transition_to_next": "cut | fade | swipe"
      },
      "motion_type": "i2v",
      "energy": "high | medium | low"
    }
  ],
  "summary": {
    "total_scenes": 8,
    "avg_scene_duration": 4.1,
    "full_transcript": "...",
    "category": "tutorial",
    "caption_style": "太字白テキスト、黒ストローク、画面中央下",
    "structure": "hook → problem → solution → demo → CTA",
    "pacing": "fast | medium | slow",
    "key_techniques": ["technique1", "technique2"]
  }
}

使い方

1. 単体分析

python analyze_video.py --url "URL"

2. バッチ分析（複数URL）

python analyze_video.py --urls-file urls.txt --output output/templates/

3. Playbook蓄積（別スキル）

分析結果のtemplate.jsonを video-playbook スキルに渡して、タイプ別知見を蓄積：

python skills/video-playbook/scripts/manage_playbook.py --add -t output/templates/template.json

4. テンプレートを使って新動画生成

分析結果のtemplate.jsonをstoryboard-generatorに渡して、自社コンテンツでリメイク：

python generate_storyboard.py --template output/templates/template.json --topic "自社プロダクト名"

依存

  • yt-dlp (.bin/yt-dlp)
  • ffmpeg (.bin/ffmpeg)
  • OpenAI Whisper API (STT)
  • Gemini Vision API (フレーム分析)

環境変数

  • OPENAI_API_KEY — Whisper STT用
  • GEMINI_API_KEY — Vision分析用