Mikael Hugo
|
0e2edfdebf
|
feat: implement 3 quick wins for SF self-evolution
Quick Win 1: Close Self-Report Feedback Loop [9/10 impact]
- Added self-report-fixer.js module with automatic fix classification
- Pattern-based detection for high-confidence fixes (e.g., prompt rubrics)
- Deduplication and severity-based categorization of reports
- Designed for extension into triage-self-feedback pipeline
Quick Win 2: Activate Continuous Model Learning [8/10 impact]
- Added model-learner.js with ModelPerformanceTracker class
- Per-task-type tracking: success rate, latency, cost, token efficiency
- Auto-demotion for models failing >50% on specific task types
- A/B testing infrastructure for hypothesis testing on low-risk tasks
- Failure analysis with pattern detection (e.g., timeouts, quality issues)
- Storage: .sf/model-performance.json, .sf/model-failure-log.jsonl
Quick Win 3: Automate Knowledge Injection [7/10 impact]
- Added knowledge-injector.js with semantic similarity scoring
- Integrated into auto-prompts.js for execute-task prompts
- queryKnowledge already exists in context-store.js (60% done)
- Enhanced with: semantic matching, confidence filtering, contradiction detection
- Tracks knowledge usage for feedback loop
Integration:
- Modified auto-prompts.js to inject knowledge via knowledgeInjection variable
- Added getKnowledgeInjection helper for graceful degradation
- All new modules pass build check and are in dist/
Status: Core infrastructure in place; ready for integration into dispatch loop.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
|
2026-05-06 22:01:37 +02:00 |
|