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WhiteTowerAIWhiteTowerAI/cut-as-code/skills/video-understand/SKILL.md

video-understand

Skill16 starsChanged 4 months agoMITView on GitHub

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.

13 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use gift-evaluator, Podcast Generate and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • dav-niu474Skill

    gift-evaluator

    The PRIMARY tool for Spring Festival gift analysis and social interaction generation. Use this skill when users upload photos of gifts (alcohol, tea, supplements, etc.) to inquire about their value, authenticity, or how to respond socially. Integrates visual perception, market valuation, and HTML card generation.

    Also in 5 projects that use this

  • dav-niu474Skill

    Podcast Generate

    Generate podcast episodes from user-provided content or by searching the web for specified topics. If user uploads a text file/article, creates a dual-host dialogue podcast (or single-host upon request). If no content is provided, searches the web for information about the user-specified topic and generates a podcast. Duration scales with content size (3-20 minutes, ~240 chars/min). Uses z-ai-web-dev-sdk for LLM script generation and TTS audio synthesis. Outputs both a podcast script (Markdown) and a complete audio file (WAV).

    Also in 5 projects that use this

  • huang-x-hSkill

    Video Generation

    Implement AI-powered video generation capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to generate videos from text prompts or images, create video content programmatically, or build applications that produce video outputs. Supports asynchronous task management with status polling and result retrieval.

    Also in 5 projects that use this

  • +9 more from dav-niu474

The file

We split this skill into the rules it gives an agent

9 rules. 0 are common, 3 are distinctive, and 6 are very specific to this project.

  • 0

    Common

    Most of the 12 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run it first so those skills consume the same validated evidence and timeline.

    line 12
  2. Very specific:

    Use --lang auto for unknown or mixed-language speech.

    line 44
  3. Very specific:

    Read the source, transcript, and analysis.

    line 57
  4. Very specific:

    Create only these useful review artifacts under review/00-video-understanding/: video-summary.md, transcript.srt, and contact-sheet.jpg.

    line 65
  5. Very specific:

    Read project-schema.md when creating or validating project.json.

    line 76
  6. Distinctive:

    Read timeline-schema.md when mapping source and program time.

    line 77
  7. Distinctive:

    Read understanding-schema.md before authoring semantic understanding.

    line 78
  8. Very specific:

    Use understanding.example.json as a compact example.

    line 79
  9. Very specific:

    All durable machine files live in work/understand/.

    line 81

“Common” is measured: kept by at least 6 in 10 of the 12 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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, /video-add-content-cards, and /video-add-motion-graphics. 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

  1. 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.

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

    python scripts/probe.py input/original-video.mp4 work/understand/media.json
  3. 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.

  4. Generate objective metrics and semantic candidates:

    python scripts/analyze.py work/understand/transcript.json work/understand/analysis.json
  5. 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.
  6. Validate before downstream use:

    python scripts/validate.py understanding work/understand/understanding.json work/understand/transcript.json
  7. 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.
  8. 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.

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