| name | video-frame-reader |
| description | 動画ファイルからキーフレームを抽出し、重複除去・最適化した上で内容を分析するスキル。 「動画の中身を見て」「キーフレームを抽出」「この動画を分析して」等で発動。 |
| triggers | ["動画の中身を見て","キーフレームを抽出","この動画を分析して","動画を確認して","スクリーン録画を見て","video-frame-reader","keyframe extraction"] |
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 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
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
Usage
上記「Workflow」セクションを参照。基本例:
python3 skills/video-frame-reader/scripts/extract_keyframes.py "video.mp4"
python3 skills/video-frame-reader/scripts/extract_keyframes.py "video.mp4" -t 0.75 -q 20 -s 0.2
