Case 03577
A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.
npx -y skills add knownasnaffy/prompthound --skill case_03577Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Analyze video content to extract keyframes, identify themes, and generate representative screenshots with analysis reports. Use when: (1) User sends a video file and asks for analysis, (2) User wants to understand video content without watching, (3) User needs representative screenshots from a video, (4) User asks "what's in this video" or "analyze this video". Supports MP4, MOV, AVI and other common video formats.
SKILL.md
2.8 KB, 602 tokens by cl100k_base, as published. Nobody here has run it
Video Analyzer
Overview
Extract keyframes from videos, analyze content with vision models, and generate comprehensive reports with 3 representative screenshots. Optimized for token efficiency using I-frame detection.
Workflow
Video Input → Extract Keyframes → Vision Analysis → Select Top 3 → Generate Report → Send Output
Step-by-Step Process
1. Download Video (if from Feishu)
When user sends video via Feishu, the file is auto-saved to:
~/.openclaw/media/inbound/<filename>.mp4
2. Extract Video Metadata
ffmpeg -i <video_path> 2>&1 | grep -E "(Duration|Video)"
Returns: duration, resolution, bitrate, codec info.
3. Extract Keyframes
Use the provided script for optimal keyframe extraction:
bash ~/.openclaw/workspace/skills/video-analyzer/scripts/extract_keyframes.sh <video_path> [output_dir]
Parameters:
video_path: Path to video file (required)output_dir: Output directory (optional, defaults to~/.openclaw/media/keyframes/)
Output: JPEG images at 640px width, named keyframe_XX.jpg
Token efficiency: Uses I-frame detection to extract only meaningful frames, reducing token consumption by ~7% vs uniform sampling.
4. Analyze with Vision Model
Use the image tool with all extracted keyframes:
prompt: "Analyze these keyframes from a video. Please:
1. Describe the video's theme and content
2. Select 3 most representative frames (explain why)"
5. Generate Report
Structure the analysis report:
## 📌 Video Theme
[Description]
## 🖼️ Representative Screenshots
| Frame | Reason |
|-------|--------|
| frame_XX | [Why representative] |
6. Send Output
Send via Feishu:
- Analysis report (text message)
- 3 representative screenshots (image messages)
Token Consumption Reference
| Video Length | Keyframes | Estimated Tokens |
|---|---|---|
| 5 seconds | 5-8 | ~8,000-14,000 |
| 15 seconds | 12-16 | ~20,000-28,000 |
| 30 seconds | 20-30 | ~35,000-50,000 |
Optimization tips:
- Images account for 95%+ of tokens
- Shorter videos = fewer tokens
- Low-motion videos produce fewer keyframes
Resources
scripts/
extract_keyframes.sh- Extract keyframes using ffmpeg I-frame detection
references/
ffmpeg_reference.md- Advanced ffmpeg commands for video processing
What ships with it: 2 files
3.9 KB alongside SKILL.md, 1 of them executable
references/
- ffmpeg_reference.md2.7 KB
scripts/
- extract_keyframes.shruns1.2 KB