d81e403f01
COMPLETED TASKS: ✅ 06-01: Workout Swap System - Added swapped_from_id to workout_logs - Created workout_swaps table for history - POST /api/workouts/:id/swap endpoint - GET /api/workouts/available endpoint - Reversible swaps with audit trail ✅ 06-02: Muscle Group Recovery Tracking - Created muscle_group_recovery table - Implemented calculateRecoveryScore() function - GET /api/recovery/muscle-groups endpoint - GET /api/recovery/most-recovered endpoint - Auto-tracking on workout log completion ✅ 06-03: Smart Workout Recommendations - GET /api/recommendations/smart-workout endpoint - 7-day workout analysis algorithm - Recovery-based filtering (>30% threshold) - Top 3 recommendations with context - Context-aware reasoning messages DATABASE CHANGES: - Added 4 new tables: muscle_group_recovery, workout_swaps, custom_workouts, custom_workout_exercises - Extended workout_logs with: swapped_from_id, source_type, custom_workout_id, custom_workout_exercise_id - Created 7 new indexes for performance IMPLEMENTATION: - Recovery service with 4 core functions - 2 new route handlers (recovery, smartRecommendations) - Updated workouts router with swap endpoints - Integrated recovery tracking into POST /api/logs - Full error handling and logging TESTING: - Test file created: /backend/test/phase-06-tests.js - Ready for E2E and staging validation STATUS: Ready for frontend integration and production review Branch: feature/06-phase-06
1.3 KiB
1.3 KiB
Performance Bottleneck Analysis
Purpose
Identify and resolve performance bottlenecks in your development workflow.
Automated Analysis
1. Real-time Detection
The post-task hook automatically analyzes:
- Execution time vs. complexity
- Agent utilization rates
- Resource constraints
- Operation patterns
2. Common Bottlenecks
Time Bottlenecks:
- Tasks taking > 5 minutes
- Sequential operations that could parallelize
- Redundant file operations
Coordination Bottlenecks:
- Single agent for complex tasks
- Unbalanced agent workloads
- Poor topology selection
Resource Bottlenecks:
- High operation count (> 100)
- Memory constraints
- I/O limitations
3. Improvement Suggestions
Tool: mcp__claude-flow__task_results
Parameters: {"taskId": "task-123", "format": "detailed"}
Result includes:
{
"bottlenecks": [
{
"type": "coordination",
"severity": "high",
"description": "Single agent used for complex task",
"recommendation": "Spawn specialized agents for parallel work"
}
],
"improvements": [
{
"area": "execution_time",
"suggestion": "Use parallel task execution",
"expectedImprovement": "30-50% time reduction"
}
]
}
Continuous Optimization
The system learns from each task to prevent future bottlenecks!