extract-docs

name	extract-docs
description	Systematically extract knowledge from scattered documentation to prevent catastrophic forgetting. Creates structured extraction reports with status tags.
short_desc	extract knowledge from scattered docs (structured)
keywords	["documentation extraction","catastrophic forgetting","status tags","scattered docs","before archiving","scattered documentation","knowledge extraction","before archival","extract knowledge from docs","pull info from documentation"]
argument-hint	[source-path-or-pattern]
tools	Read, Write, Grep, Glob, Bash, Task
model	sonnet

Extract Documentation Knowledge (Sonnet)

Purpose: Systematically extract knowledge from scattered documentation to prevent catastrophic forgetting.

When to Invoke Autonomously

1. Project has 20+ scattered documentation files - Risk of losing context
2. Before archiving completed work - Extract still-relevant content first
3. Preparing canonical living documents - Consolidate before creating
4. After major phase completion - Capture learnings before forgetting

DO NOT invoke for

• Well-organized documentation (no consolidation needed)
• Active editing sessions (extract afterward)
• Single-file documentation tasks

Search Systems

1. kg-search/kg-info (Keyword/Metadata) - Fast (~100ms):

• .claude/scripts/kg-search search "term" [--type TYPE] [--tags TAGS]
• .claude/scripts/kg-info info "Title"

2. Weaviate MCP Tools (Semantic/Graph):

• hybrid_search - Keyword + semantic across KG + docs (default search tool, ~1-2s)
• semantic_graph_search - GraphRAG with WikiLink traversal (~1-2s)

3. Code Graph (Semantic Code Search):

• search_code_graph - Find code by purpose/concept (~200-500ms)
• query_code_structure - Dependencies, callers, inheritance (~50-100ms)
• CLI: .claude/scripts/code-graph-query search "pattern"

Decision: Known terms → kg-search | Concepts → hybrid_search | Relationships → semantic_graph_search | Code entities → search_code_graph

What This Skill Does

Documentation Audit

• Scans for scattered documentation files
• Identifies priority files (session summaries → test results → implementation → evaluations)
• Detects duplicate or overlapping content
• Assesses risk of knowledge loss

Knowledge Extraction

Spawns doc-extractor agent that:

1. Reads files in priority order
2. Extracts knowledge with status tags:
   • [IMPLEMENTED] - Successfully implemented and working
   • [EXPLORED_DISCARDED] - Tried but abandoned
   • [DIDNT_WORK] - Failed approach with explanation
   • [FUTURE_IDEA] - Potential future work
3. Categorizes by target document: ARCHITECTURE.md, TESTING_GUIDE.md, PERFORMANCE_NOTES.md, etc.
4. Produces structured extraction report

Output Report Structure

# Knowledge Extraction Report

## For ARCHITECTURE.md
### [Component with status tags and details]

## For TESTING_GUIDE.md
### [Test coverage and outcomes]

## For PERFORMANCE_NOTES.md
### [Benchmarks and optimizations]

## Summary
- Files reviewed: X
- Knowledge items: Y
- Items by status: [IMPLEMENTED]: N, [EXPLORED_DISCARDED]: M...

Integration with Workflow

Knowledge Graph Integration

After extraction completes:

1. Review the report
2. Integrate extracted knowledge into canonical docs
3. Create knowledge graph nodes for reusable patterns
4. Create code graph entries for implementation patterns (if code-heavy)
5. Archive source files (with date prefixes: YYYY-MM-DD_name.md)
6. Sync canonical docs to project development collection

Usage Pattern

/extract-docs

The skill will:

1. Detect documentation structure
2. Identify priority files
3. Spawn doc-extractor agent (uses existing agent template)
4. Provide extraction report path
5. Suggest next steps (consolidate → create KG nodes → archive)

Token Efficiency

Optimized for large documentation sets:

• Agent runs in background (non-blocking)
• Processes files incrementally
• Produces concise report (not full file dumps)
• Expected usage: 2000-4000 tokens for 20-50 files

Success Criteria

After extraction:

• ✅ All knowledge captured with status tags
• ✅ Clear categorization by target document
• ✅ No information loss from archival
• ✅ Reusable patterns identified for knowledge graph