Test
927835cb0e
- Add internal/tuning package with intelligent timeout analysis - Implement TimeoutAnalyzer for tracking activity execution metrics - Calculate percentile-based timeout recommendations (P95, P99) - Generate confidence scores based on sample size and failure rate - Implement TimeoutLessonsStore for persistent lesson tracking - Store lessons in per-task JSONL files with effectiveness tracking - Generate TimeoutTuningSignal objects for planner integration - Generate human-readable lesson format for planner context - Support three-tier priority signaling (high/medium/low) - Analyze multiple activities concurrently Analysis Features: - Track duration, success/failure, timestamps for each execution - Identify undertuned activities (P99 exceeds timeout) - Detect overtuned activities (timeout > 2x P99) - Calculate confidence scores (40% sample data + 60% reliability) - Generate recommendations with reasoning Lesson Management: - Persist lessons per task in JSONL format - Support lesson effectiveness tracking - Format lessons for planner input - Enable feedback loop for timeout optimization Test Coverage: - 14 analyzer tests (metrics, analysis, persistence) - 22 lessons tests (storage, signals, formatting) - 36 total tuning tests, all passing - Edge cases: empty metrics, all failures, multiple activities Key Design: - P99 + 20% buffer for safe timeout values - Weighted confidence scoring for reliable recommendations - Separation: Analyzer (metrics), Lessons (storage), Signals (integration) - Thread-safe analyzer with RWMutex - No external dependencies added Closes T1.3