Implements the 3-table normalization model for milestone, slice, and task entities:
- 9 new tables: {milestone,slice,task}_{specs,evidence} + runtime tables
- milestone_specs: immutable record of intent (vision, goals, risks, proof strategy)
- slice_specs: immutable slice-level intent
- task_specs: immutable task verification criteria
- {entity}_evidence: append-only audit trail with timestamps and phase metadata
- Indices on evidence tables for efficient chronological queries
Key improvements:
- Spec immutability: Write-once specs preserve original intent
- Audit trail: Evidence chain enables data archaeology and decision history
- Query efficiency: Each table contains only relevant columns
- Re-planning clarity: Multiple spec versions can exist for same entity ID
- Forensic capability: Timestamp + phase metadata on evidence rows
Migration:
- Schema version bumped to 32
- Migration runs on first open of existing databases
- No data loss; existing milestone/slice/task rows preserved
- Creates spec and evidence tables from existing columns (future work)
This is Phase 1 of Tier 1.3 implementation (schema definition + basic setup).
Phases 2-5 (migration, data layer updates, tool updates, tests) follow in next PRs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Document the three-phase integration of SF memory system with UOK:
Phase 1: Unit outcome recording (recordUnitOutcomeInMemory)
- Records success/failure patterns with 0.9/0.5 confidence
- Fire-and-forget async, never blocks execution
Phase 2: Dispatch ranking enhancement (enhanceUnitRankingWithMemory)
- Queries memory for similar patterns
- Boosts matching candidates by up to 15% (conservative limit)
- Deterministic embeddings ensure reproducible ranking
Phase 3: Gate context enrichment (enrichGateResultWithMemory)
- Diagnostic only; never changes gate pass/fail logic
- Helps operators understand recurring issues
All memory operations gracefully degrade if DB unavailable.
56 test cases validate integration across all phases.
Relates to ADR-0075 (UOK gates), ADR-008 (SF tools).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Add architecture decision: Memory is not exposed as MCP server.
- SF is an MCP client only (consumes external MCP tools)
- Memory is internal SF infrastructure (uses SQLite, fire-and-forget async)
- Memory exposed as SF tools only (capture, query, graph)
- No external MCP exposure needed (memory is autonomous learning, not a service)
This keeps SF's learning system private and prevents:
- External memory pollution
- Uncontrolled confidence scoring
- Inconsistent learning patterns
- Loss of autonomy (memory decisions stay internal)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Comprehensive guide for migrating from JSON to node:sqlite when Node 24 is available:
- Schema design (model_outcomes + model_stats tables)
- Phase-by-phase refactoring approach
- Data migration from JSON with backward compatibility
- Testing strategy with new SQLite-specific tests
- Future opportunities: dashboards, trend analysis, A/B testing, federated learning
This doc serves as a roadmap for ~2 days of work when Node 24 becomes standard.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Integration of 3 quick wins into existing UOK infrastructure:
1. Model Learning (Quick Win #2) → metrics.js
- Record outcomes to model-learner for per-task-type performance tracking
- Hook: recordUnitOutcome() now calls ModelLearner.recordOutcome()
- Fire-and-forget: never blocks outcome recording on learning failure
- Enables adaptive model routing decisions in downstream gates
2. Self-Report Fixing (Quick Win #1) → triage-self-feedback.js
- Auto-fix high-confidence reports (>0.85) in applyTriageReport()
- Hook: After triage and requirement promotion, apply auto-fixes
- Fire-and-forget: never blocks report application on fix failure
- Returns reportsAutoFixed count for triage metrics
3. Knowledge Injection (Quick Win #3) → already integrated in auto-prompts.js
- Already active in execute-task prompt template
- Semantic matching with graceful degradation
All integration points:
- Fire-and-forget: learning/fixing failures never block dispatch
- UOK-native: use existing outcome recording, db, gates
- Backward compatible: applyTriageReport now async, but callers handle it
- No new dependencies: all modules already in codebase
Testing: 2934 tests pass (no regressions from integration)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
