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Dragan Spiridonov
bd938c5597
* fix(kernel): resolve relative dbPath to prevent split-brain dual-database writes UnifiedPersistenceManager and HybridMemoryBackend pass static relative path '.agentic-qe/memory.db' to UnifiedMemoryManager, which overrides the resolved absolute path in the constructor via spread. When CWD differs from project root (e.g. tests running from v3/), this creates a duplicate database at v3/.agentic-qe/memory.db, splitting dream/concept data from patterns/KV data. Fix: resolve any relative dbPath through findProjectRoot() in the constructor, ensuring all callers converge on the single project-root database. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * release(v3.6.8): fix MCP auto-init, experience persistence, DB isolation, and missing deps - Fleet auto-initializes on MCP server startup (no manual fleet_init needed) - ExperienceReplay unified to read from captured_experiences table - Data migrator writes to captured_experiences instead of removed experiences table - Added fast-json-patch and jose to root package.json (fixes #262) - Wrapped MinCut dispose saveSnapshot in try/catch for test resilience - Kernel initializes UnifiedPersistence with temp DB for in-memory backend - All 6 MCP handler test files use memoryBackend:'memory' and resetUnifiedPersistence() - domain-handlers and wrapped-domain-handlers use beforeAll to prevent OOM Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: add RVF integration plan, ADRs, and benchmark script - RVF integration implementation plan and Six Hats analyses - Phase 0 gate report for RVF integration readiness - ADR-065: RVF integration hybrid architecture - ADR-066: RVF pattern store progressive HNSW - ADR-067: Agent memory branching with RVF copy-on-write - AQE baseline benchmark script Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(tests): restore fleet after dispose in wrapped-domain-handlers beforeAll tests The "not initialized" tests call disposeFleet() but with beforeAll the fleet is only initialized once. Subsequent tests failed because the fleet was never re-initialized. Add handleFleetInit() after each dispose-based test to restore state for later tests. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Implementation Plans
This directory contains GOAP-style implementation plans for major AQE features.
Active Plans
Skill Validation: Trust But Verify
Status: Planning Priority: High Estimated Effort: 214 hours across 5 phases
Implements a 4-layer validation system for AQE skills:
Level 3: Evaluation Suite (measure behavior)
Level 2: Executable Validator (verify output)
Level 1: JSON Schema (structure output)
Level 0: SKILL.md Instructions (guide intent)
Key Deliverables:
- Trust tier frontmatter extension for SKILL.md
- JSON output schemas for all P0 skills
- Executable validators with graceful degradation
- YAML-based evaluation test suites
- CI/CD workflow for multi-model validation
- ReasoningBank integration for learning
Related Resources:
- Schemas:
/docs/schemas/ - Templates:
/docs/templates/ - Exemplars:
.claude/skills/testability-scoring/,.claude/skills/brutal-honesty-review/
Plan Structure
Each plan follows the SPARC-GOAP methodology:
- Specification: Define goal state and success criteria
- Pseudocode: Plan action sequences and state transitions
- Architecture: Structure solution components
- Refinement: TDD implementation with iteration
- Completion: Integration, validation, deployment
Milestone Template
Milestone X.Y: Name
Description: What this milestone achieves
Complexity: Low/Medium/High
Estimated Hours: N
Agent Assignment: qe-agent-name
Dependencies: [milestone IDs]
Parallel Opportunity: Yes/No
Deliverables:
- path/to/deliverable
Success Criteria:
- [ ] Measurable outcome
Memory Namespaces
Plans define memory namespaces for pattern learning:
aqe/{feature}/
├── patterns/ - Learned patterns
├── outcomes/ - Execution outcomes
├── metrics/ - Performance metrics
└── trends/ - Historical trends
Creating New Plans
- Copy the structure from an existing plan
- Define clear goal state vs current state
- Break into SPARC phases with milestones
- Assign agents and estimate complexity
- Map dependencies and parallel opportunities
- Define success criteria for each milestone
- Add to this README