ReddRadar
Agent skill
video-frame-reader
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
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/video-frame-reader
SKILL.md
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
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