Video Frame Reader

Extract keyframes from video, present token cost, then analyze.

Requirements

• ffmpeg (for frame extraction)
• Python 3 + Pillow + numpy

Workflow

1. Capture User Intent

Clearly understand why the user wants the video analyzed:

• Example: "The screen transition behavior looks wrong"
• Example: "I want to check the response after button click"
• Example: "Help me identify performance issues"

This intent becomes important context for the analysis.

2. Install Dependencies (First Time Only)

# uv で依存関係をインストール
uv add Pillow numpy --quiet

3. Extract Keyframes

uv run python skills/video-frame-reader/scripts/extract_keyframes.py "<video_path>"

Output example (JSON):

{
  "keyframe_count": 52,
  "image_size": "266x576",
  "total_tokens": 10400,
  "cost_usd_opus": 0.156,
  "cost_usd_sonnet": 0.031,
  "cost_usd_haiku": 0.0104,
  "files": ["/.../key_0001.jpg", ...]
}

4. Present Cost

After extraction, present the following to the user:

Keyframe extraction complete:
- Frames extracted: {keyframe_count}
- Image size: {image_size}
- Estimated tokens: {total_tokens}
- Cost estimate: Haiku ${cost_usd_haiku} / Sonnet ${cost_usd_sonnet} / Opus ${cost_usd_opus}

Proceed with frame analysis?

5. Invoke Subagent After Approval

After user approval, invoke subagent using Task tool:

Task(
  subagent_type="general-purpose",
  model="haiku",
  description="Frame analysis",
  prompt="""
[User Intent]
{Intent captured in Step 1}

[Frame Image Files]
{List of paths from files array}

Analyze the above frame images and identify issues/behaviors according to the user's intent.
"""
)

Benefits of this approach:

• ✅ User intent is included in analysis context
• ✅ Subagent can focus on intent-specific efficient analysis
• ✅ Processed in independent context for better token efficiency

Options

Option	Default	Description
-t, --threshold	0.85	Similarity threshold (higher = more frames kept)
-q, --quality	30	JPEG quality (1-100)
-s, --scale	0.3	Resize scale
-o, --output	<video_name>_keyframes/	Output directory

Token Reduction Example

# More aggressive reduction (lower threshold, quality, and size)
python3 extract_keyframes.py video.mp4 -t 0.75 -q 20 -s 0.2

Overview

動画ファイルからキーフレームを自動抽出し、重複除去・最適化した上で内容を分析するスキルです。トークンコストを事前提示し、ユーザー承認後にサブエージェントで分析を実行します。

Troubleshooting

エラー	解決方法
ffmpeg not found	brew install ffmpeg（Mac）または apt install ffmpeg（Linux）でインストール
No keyframes extracted	--threshold を下げる（例: 0.75）ことでより多くのフレームを抽出

Success Criteria

• キーフレーム画像が出力ディレクトリに保存されている
• トークンコスト見積もりがJSON形式で表示されている
• ユーザーの分析意図に沿った結果が返却されている

Usage

上記「Workflow」セクションを参照。基本例:

# キーフレーム抽出
python3 skills/video-frame-reader/scripts/extract_keyframes.py "video.mp4"

# トークン削減オプション付き
python3 skills/video-frame-reader/scripts/extract_keyframes.py "video.mp4" -t 0.75 -q 20 -s 0.2