transcript-learn

name	transcript-learn
description	Extract knowledge from video transcripts (YouTube, courses, seminars, podcasts) and convert to DQ knowledge chunks, skills, or agent definitions. Accepts YouTube URLs or .txt transcript files. ONLY invoked explicitly by user — never auto-invoked.
command	/transcript-learn
allowed-tools	["Bash","Read","Write","Grep","Glob"]
user-invocable	true
disable-model-invocation	true
experimental	true

/transcript-learn — Video Knowledge Ingestion

Extract structured knowledge from video transcripts and integrate into DQIII8.

Usage

/transcript-learn <youtube-url>
/transcript-learn <path-to-transcript.txt>
/transcript-learn --batch <file-with-urls.txt>

Pipeline

Step 1: Get transcript

• If YouTube URL: use youtube-transcript MCP tool get_transcript (without timestamps for long videos)
• If .txt file: read directly
• If transcript > 25000 tokens: process in a forked subagent to save context

Step 2: Analyze content

Extract from the transcript:

1. Key concepts — main ideas, definitions, frameworks
2. Techniques/methods — actionable processes, step-by-step workflows
3. Tools/technologies — software, libraries, APIs mentioned
4. Data/metrics — numbers, benchmarks, comparisons
5. Quotes — notable statements with timestamp if available
6. Action items — things we could implement in DQ or projects

Step 3: Classify domain

Map content to DQ domain:

• formal_sciences: math, logic, algorithms, statistics
• natural_sciences: biology, physics, chemistry, nutrition
• social_sciences: finance, marketing, law, economics
• humanities_arts: writing, philosophy, history, design
• applied_sciences: programming, devops, web dev, AI/ML

Step 4: Generate output

Based on content type, generate ONE OR MORE of:

A) Knowledge chunk (factual knowledge): Save to: knowledge/{domain}/{topic_slug}.md Format: use template templates/knowledge_chunk.md

B) Skill proposal (workflow/methodology): Save to: skills-registry/custom/proposed/{skill_name}.md Format: standard SKILL.md with frontmatter Status: PENDING_REVIEW

C) Agent proposal (deep expertise): Save to: .claude/agents/proposed/{agent_name}.md Format: standard agent .md with frontmatter Status: PENDING_REVIEW — user must approve before activation

Step 5: Index

After saving knowledge chunk:

• Add to the domain's index.json if it exists
• Print summary of what was generated

Step 6: Report

Print:

=== TRANSCRIPT LEARNING COMPLETE ===
Source: {url or file}
Duration: {if known}
Domain: {classified domain}
Generated:
  Knowledge chunks: N (paths listed)
  Skill proposals: N (paths listed)
  Agent proposals: N (paths listed)

Review pending items with: ls skills-registry/custom/proposed/

Templates

• Knowledge chunks: .claude/skills/transcript-learn/templates/knowledge_chunk.md
• Skill proposals: .claude/skills/transcript-learn/templates/skill_proposal.md

Notes

• This skill NEVER auto-invokes. User must explicitly type /transcript-learn
• For videos > 60 min, use subagent (context: fork) to avoid filling main context
• Knowledge chunks go to knowledge/ (existing DQ structure)
• Proposed skills/agents go to proposed/ subdirectories (not active until approved)
• All generated content includes source attribution