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
- OpenMontageInc/OpenMontagetheir own version, first seen Nov 8, 2026
- calesthio/OpenMontagetheir own version, first seen Nov 8, 2026
- Gopyr/gopyr-skills-hubtheir own version, first seen Oct 11, 2026
- jjyaoao/HelloAgentstheir own version, first seen Oct 6, 2026
- andrewii23/ii23-skillstheir own version, first seen Sep 27, 2026
- andfrancot-alt/claude-skillstheir own version, first seen Aug 17, 2026
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-niu474Skillgift-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-niu474SkillPodcast 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-hSkillVideo 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
Other versions13 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 13
- calesthio/OpenMontage★ 78ksame name
- OpenMontageInc/OpenMontage★ 78ksame name
- jjyaoao/HelloAgents★ 3.2ksame name
- xiaotianfotos/skills★ 582same name
- jrusso1020/video-understand-skills★ 19same name
- choupiyang/ZAI_SKILLS★ 13same name
- andrewii23/ii23-skills★ 11same name
- LinHao-city/StoryMind★ 10same name
- dav-niu474/MyClaude-Code★ 1same name
- SuperInstance/flux-fleet-scanner★ 1same name
3 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- cut-as-codeSkill
- video-add-b-rollSkill
- video-add-captionsSkill
- video-add-content-cardsSkill
- video-add-motion-graphicsSkill
- video-color-gradeSkill
- video-cutSkill
- video-edit-compareSkill
- video-to-shortsSkill
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
- Distinctive: line 12
Run it first so those skills consume the same validated evidence and timeline.
- Very specific: line 44
Use --lang auto for unknown or mixed-language speech.
- Very specific: line 57
Read the source, transcript, and analysis.
- Very specific: line 65
Create only these useful review artifacts under review/00-video-understanding/: video-summary.md, transcript.srt, and contact-sheet.jpg.
- Very specific: line 76
Read project-schema.md when creating or validating project.json.
- Distinctive: line 77
Read timeline-schema.md when mapping source and program time.
- Distinctive: line 78
Read understanding-schema.md before authoring semantic understanding.
- Very specific: line 79
Use understanding.example.json as a compact example.
- Very specific: line 81
All durable machine files live in work/understand/.
“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.
Files
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
Initialize a project from the original source:
python scripts/init_project.py path/to/source.mp4 path/to/my-video-projectThis creates
input/,review/00-video-understanding/,final/, the minimal machine-facingwork/tree, an identity timeline,project.json, media facts, andSTART-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.jsonExtract 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-whisperUse
--lang autofor 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 zhonly when the audio itself or explicit user metadata establishes it. Keep model downloads in the project-localwork/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.jsonwith 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, andcontact-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.statusaspassonly after the review artifacts and semantic evidence validate. Operation lifecyclestatusand check result are separate; never writeverifiedintocheck.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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

