Video decompose

Skill mnttnm/claude-skills/video-decompose

Custom Claude Code skills spanning UI/UX, productivity, and developer tooling

Install

npx -y skills add mnttnm/claude-skills --skill video-decompose

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What its author says it does

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Convert screen recording videos (Loom, mp4) into structured keyframes + transcript for LLM consumption. Use when the user provides a video URL or local video file and wants to extract product requirements, document a feature walkthrough, or convert video content into a format Claude can analyze. Triggers on "decompose this video", "extract frames from video", user shares a Loom or video URL and wants requirements extracted, "convert this video to requirements", or user mentions video-based product requirements or feature walkthroughs they need analyzed.

SKILL.md

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Video Decompose

Convert screen recordings into structured frames + aligned transcript for LLM analysis.

Prerequisites

Verify before proceeding:

python3 -c "import cv2, skimage; print('OK')"
# If missing: pip install opencv-python scikit-image numpy
# For URL downloads: which yt-dlp (install with: brew install yt-dlp)

Workflow

1. Determine Input

Ask the user for:

URL — a Loom or other video URL (requires yt-dlp)
Local files — mp4 video + optional SRT/VTT transcript

2. Run Decomposition

Execute the script from this skill's scripts/ directory:

# From URL
python3 scripts/video_decompose.py --url <URL> --output ./output/<name> --json

# From local files
python3 scripts/video_decompose.py --video <path.mp4> --transcript <path.vtt> --output ./output/<name> --json

Tuning (adjust if frame count is too high/low):

--threshold 0.80 — Lower (0.70) = fewer frames, higher (0.90) = more frames
--min-gap 3.0 — Increase to 5+ if too many rapid-transition frames
--sample-rate 1 — Raise to 2 for fast-paced demos

3. Review Output

Read output/<name>/summary.md and spot-check 2-3 frame images. If frame count is off, re-run with adjusted flags.

Expected output:

output/<name>/
├── frames/          # PNG keyframes
├── summary.md       # Markdown mapping frames to transcript
└── product-requirements.md  # (if step 4 is performed)

4. Extract Requirements (Optional)

When the user wants structured product requirements, read references/requirements-extraction-prompt.md for the extraction template. Read summary.md, view all frame images, and produce a requirements document following that template. Save as product-requirements.md in the output directory.

JSON Output

With --json, stdout contains structured data (progress on stderr):

{
  "status": "success",
  "video": { "filename": "...", "duration_seconds": 529.0 },
  "extraction": { "total_frames": 14 },
  "frames": [
    {
      "index": 1,
      "filename": "frame_001_00m00s.png",
      "file_path": "./output/frames/frame_001_00m00s.png",
      "timestamp_start": 0.0,
      "timestamp_end": 171.0,
      "transcript": "..."
    }
  ]
}

Add --base64 to embed frame images as base64 in JSON (self-contained, no file references).

Error Handling

Exit code	Meaning	Action
0	Success	Proceed
1	Input error (missing file, bad URL)	Check paths/URL, retry
2	Processing error (no frames)	Lower threshold or check video format