---

name: video-frame-reader description: | A skill that extracts keyframes from video files and analyzes their content. Automatically removes duplicate frames and optimizes image quality to reduce token consumption.

Use when:

• User provides a video file (.mp4, .mov, .avi, etc.)
• User requests "watch this video", "analyze this video", "what's in this video"
• Checking screen recordings or screencasts
• Keyframe extraction is needed from video

---

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. Create venv (First Time Only)

cd ~/.claude/skills/video-frame-reader/scripts
python3 -m venv venv
source venv/bin/activate
pip install Pillow numpy --quiet

3. Extract Keyframes

source ~/.claude/skills/video-frame-reader/scripts/venv/bin/activate
python3 ~/.claude/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