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## Summary Implement complete MVP of 3-stage knowledge unit segmentation pipeline as outlined in the plan. Sessions are now transformed into modular, independently-documentable knowledge units. ## Architecture Stage 1 (Segmentation): - LLM analyzes session, identifies distinct knowledge units - Extracts topic, category, relevance score (0-10) - Enriches prompt with operational metadata (tools, files, git ops, errors) - Gracefully degrades to single unit if LLM unavailable Stage 2 (Documentation): - Applies 7 category-specific templates (bug, architecture, code, feature, topic, project, base) - LLM synthesizes focused markdown per unit - Generates YAML frontmatter with metadata - Returns raw content if synthesis unavailable Stage 3 (Deferred to Phase 2): - Entity extraction, freshness detection, metadata enrichment ## Files Created (13) Type System & Core Logic: - src/team/types.ts - KnowledgeUnit, SegmentationResult, DocumentGenerationResult - src/team/segment.ts - Stage 1 segmentation with metadata injection - src/team/document.ts - Stage 2 documentation generation Prompts (8 templates): - src/team/prompts/stage1-segment.md - Segmentation with metadata injection - src/team/prompts/stage2-*.md (7 category-specific templates) Tests & Docs: - test/team-segmented.test.ts - 14 unit tests (all passing) - IMPLEMENTATION.md - Technical documentation - QUICKSTART.md - User guide - IMPLEMENTATION_CHECKLIST.md - Verification checklist - DEMO_RESULTS.md - Live demo results ## Files Modified (3) - src/db.ts - Extended smriti_shares table with unit_id, relevance_score, entities - src/team/share.ts - Added shareSegmentedKnowledge() + routing logic - src/index.ts - Added --segmented and --min-relevance CLI flags ## Key Features ✅ Graceful degradation at each stage ✅ Unit-level deduplication (content_hash + unit_id) ✅ Category validation against taxonomy ✅ Sequential processing (safe, monitorable) ✅ YAML frontmatter with unit metadata ✅ Auto-generated manifest and CLAUDE.md index ✅ Full backward compatibility (legacy pipeline unchanged) ✅ Database schema migration support ## Usage # Basic usage smriti share --project myapp --segmented # With custom relevance threshold smriti share --project myapp --segmented --min-relevance 7 # Share specific category smriti share --category bug --segmented ## Testing ✅ 14 unit tests passing ✅ Code compiles without errors ✅ CLI working and documented ✅ Graceful degradation verified ✅ Backward compatibility confirmed ## Demo Results - Cleared previous knowledge - Successfully shared session e38f63e5 - Graceful degradation working (Ollama unavailable) - Output created in .smriti/knowledge/uncategorized/ - Database migration successful - CLAUDE.md auto-generated for discovery Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
- Add gitleaks pre-commit hook to detect secrets before commits - Configure .gitleaks.toml with allowlist for test tokens in knowledge base - Add GitHub Actions CI pipeline for automated secret scanning - Integrate detect-secrets as additional verification layer - All hooks pass with no false positives after baseline configuration Security improvements: - Prevents accidental credential commits - Scans full git history on each push - Configured to ignore test/demo tokens in documentation Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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#14