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video-understand

为视频项目构建统一证据层:初始化项目目录与时间线,探测媒体元数据,提取 16 kHz 单声道音频并用 faster-whisper 生成词级转写,再生成客观指标与语义候选;由使用者编写带源时间与转写证据的语义理解文件并校验,最后产出摘要、SRT 与联系表等评审材料,供下游剪辑类技能复用。

whitetowerai
热度 54.15
最近更新 2026-10-07 12:19:07
分类:数据依赖:代码执行 / 读写文件 / 内部调用大模型应用场景:视频编辑 / 数据处理 / 摘要总结 / 工作流 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add whitetowerai/video-understand
Metadata
name
video-understand
description
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.

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

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