AGENT SKILL

media-ingest

Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.

License 许可
MIT
最近更新
2026-04-26
Stars
★ 0
Forks
0

01 前言 Front Matter

name
media-ingest
tools
[ "search", "query", "get_page", "put_page", "add_link", "add_timeline_entry", "file_upload" ]
version
1.0.0
mutating
true
triggers
[ "watch this video", "process this YouTube link", "ingest this PDF", "save this podcast", "process this book", "what's in this screenshot", "check out this repo" ]
writes_to
[ "concepts/", "people/", "companies/", "sources/" ]
description
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
writes_pages
true

03 SKILL.md 正文

Media Ingest Skill

Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain.

Filing rule: Read skills/_brain-filing-rules.md before creating any new page.

Contract

This skill guarantees:

  • Every ingested media item has a brain page with analysis (not just a transcript dump)
  • Transcripts (video/audio) saved in raw and human-readable formats
  • Entity extraction: every person and company mentioned gets back-linked
  • Raw source files preserved via gbrain files upload-raw <!-- QMD-adapted: in QMD local mode, use local file copy instead of cloud upload -->
  • Filing by primary subject, not by media format

Convention: See skills/conventions/quality.md for Iron Law back-linking.

Every mention of a person or company with a brain page MUST create a back-link.

Phases

Phase 1: Identify format and fetch

Format Action
YouTube/video URL Fetch transcript (Whisper, transcription service, or captions)
Audio file Transcribe with available STT service
PDF Extract text (OCR if needed)
Book PDF Extract text, identify chapters/sections
Screenshot/image OCR via vision model, extract text and entities
GitHub repo Clone, read README + key files, summarize architecture

Phase 2: Upload raw source

Save the original file for provenance: gbrain files upload-raw <file> --page <slug> <!-- QMD-adapted: in QMD local mode, copy to local data dir instead of cloud upload -->

Phase 3: Create brain page

File by primary subject (not format). Use this template:

# {Title}

**Source:** {URL or file path}
**Format:** {video/audio/PDF/book/screenshot/repo}
**Created:** {date}

## Summary
{Key points, not a transcript dump}

## Key Segments / Highlights
{For video/audio: timestamped highlights. For books: chapter summaries.}

## People Mentioned
{List with links to brain pages}

## Companies Mentioned
{List with links to brain pages}

Phase 4: Entity extraction and propagation

For every person and company mentioned:

  1. Check brain for existing page
  2. Create/enrich if needed (delegate to enrich skill)
  3. Add back-link from entity page to this media page
  4. Add timeline entry on entity page

A media item is NOT fully ingested until entity propagation is complete.

Phase 5: Sync

gbrain sync to update the index.

Output Format

Brain page created with summary, highlights, and entity cross-links. Report to user: "Ingested {title}: {N} entities detected, {N} pages updated."

Anti-Patterns

  • Dumping raw transcripts without analysis
  • Skipping entity extraction ("I'll do that separately")
  • Filing by format (all videos in media/videos/) instead of by subject
  • Not preserving raw source files
  • Creating stub pages without meaningful content

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