Video understand

Skill WhiteTowerAI/cut-as-code/skills/video-understand

Open-source agentic video editing skills — an AI-powered alternative to Opus Clip and CapCut

Install

npx -y skills add WhiteTowerAI/cut-as-code --skill video-understand

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One thing to look at

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What its author says it does

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Use when a video project needs reusable media metadata, word-level transcription, objective speech analysis, or evidence-backed semantic understanding before optional editing skills run.

SKILL.md

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Video Understand

Build the shared evidence layer once. Keep observations in source time and leave editorial decisions to downstream skills.

This skill is a prerequisite for /video-cut, /video-to-shorts, /video-add-captions, and /video-add-content-cards. Run it first so those skills consume the same validated evidence and timeline.

Dependencies

Require ffmpeg/ffprobe, Python, and faster-whisper for transcription. Check them before processing media.

Workflow

Initialize a project from the original source:
```
python scripts/init_project.py path/to/source.mp4 path/to/my-video-project
```
This creates input/, review/00-video-understanding/, final/, the minimal machine-facing work/ tree, an identity timeline, project.json, media facts, and START-HERE.md. It does not create folders for unselected edit operations.

Probe again only when the source needs an explicit metadata refresh:

python scripts/probe.py input/original-video.mp4 work/understand/media.json

Extract 16 kHz mono audio and transcribe it:
```
ffmpeg -y -i input/original-video.mp4 -ac 1 -ar 16000 work/cache/audio16k.wav
python scripts/transcribe.py work/cache/audio16k.wav work/understand/transcript medium `
  --lang auto --cache-dir work/cache/faster-whisper
```
Use --lang auto for unknown or mixed-language speech. Never infer the spoken language from the language of the user's prompt. Pass a fixed language such as --lang zh only when the audio itself or explicit user metadata establishes it. Keep model downloads in the project-local work/cache/faster-whisper/ cache. Faster-whisper may emit an occasional point-timed word with equal start/end values; the shared timeline mapper preserves it as a 1 ms interval so captions and derivatives do not silently lose text.

Generate objective metrics and semantic candidates:

python scripts/analyze.py work/understand/transcript.json work/understand/analysis.json

Read the source, transcript, and analysis. Author work/understand/understanding.json with factual summaries, source-time ranges, confidence, and transcript evidence. Do not prescribe cuts, cards, or looks.

Validate before downstream use:

python scripts/validate.py understanding work/understand/understanding.json work/understand/transcript.json

Create only these useful review artifacts under review/00-video-understanding/: video-summary.md, transcript.srt, and contact-sheet.jpg. Verify metadata, timestamps, evidence references, and visible frames. Do not substitute PNG or ad hoc filenames for the protocol names.
Mark the understanding operation check.status as pass only after the review artifacts and semantic evidence validate. Operation lifecycle status and check result are separate; never write verified into check.status.

Contracts

Read project-schema.md when creating or validating project.json.
Read timeline-schema.md when mapping source and program time.
Read understanding-schema.md before authoring semantic understanding.
Use understanding.example.json as a compact example.

All durable machine files live in work/understand/. Treat work/cache/ as disposable.