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
1.5 KiB
1.5 KiB
Automatic Topology Selection
Purpose
Automatically select the optimal swarm topology based on task complexity analysis.
How It Works
1. Task Analysis
The system analyzes your task description to determine:
- Complexity level (simple/medium/complex)
- Required agent types
- Estimated duration
- Resource requirements
2. Topology Selection
Based on analysis, it selects:
- Star: For simple, centralized tasks
- Mesh: For medium complexity with flexibility needs
- Hierarchical: For complex tasks requiring structure
- Ring: For sequential processing workflows
3. Example Usage
Simple Task:
Tool: mcp__claude-flow__task_orchestrate
Parameters: {"task": "Fix typo in README.md"}
Result: Automatically uses star topology with single agent
Complex Task:
Tool: mcp__claude-flow__task_orchestrate
Parameters: {"task": "Refactor authentication system with JWT, add tests, update documentation"}
Result: Automatically uses hierarchical topology with architect, coder, and tester agents
Benefits
- 🎯 Optimal performance for each task type
- 🤖 Automatic agent assignment
- ⚡ Reduced setup time
- 📊 Better resource utilization
Hook Configuration
The pre-task hook automatically handles topology selection:
{
"command": "npx claude-flow hook pre-task --optimize-topology"
}
Direct Optimization
Tool: mcp__claude-flow__topology_optimize
Parameters: {"swarmId": "current"}
CLI Usage
# Auto-optimize topology via CLI
npx claude-flow optimize topology