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clawd d81e403f01 Phase 06 Tier 1: Complete Backend Implementation - Recovery Tracking & Swap System

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

2026-03-06 20:54:03 +01:00

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Smart Agent Auto-Spawning

Purpose

Automatically spawn the right agents at the right time without manual intervention.

Auto-Spawning Triggers

1. File Type Detection

When editing files, agents auto-spawn:

JavaScript/TypeScript: Coder agent
Markdown: Researcher agent
JSON/YAML: Analyst agent
Multiple files: Coordinator agent

2. Task Complexity

Simple task: "Fix typo"
→ Single coordinator agent

Complex task: "Implement OAuth with Google"
→ Architect + Coder + Tester + Researcher

3. Dynamic Scaling

The system monitors workload and spawns additional agents when:

Task queue grows
Complexity increases
Parallel opportunities exist

Status Monitoring:

// Check swarm health
mcp__claude-flow__swarm_status({
  "swarmId": "current"
})

// Monitor agent performance
mcp__claude-flow__agent_metrics({
  "agentId": "agent-123"
})

Configuration

MCP Tool Integration

Uses Claude Flow MCP tools for agent coordination:

// Initialize swarm with appropriate topology
mcp__claude-flow__swarm_init({
  "topology": "mesh",
  "maxAgents": 8,
  "strategy": "auto"
})

// Spawn agents based on file type
mcp__claude-flow__agent_spawn({
  "type": "coder",
  "name": "JavaScript Handler",
  "capabilities": ["javascript", "typescript"]
})

Fallback Configuration

If MCP tools are unavailable:

npx claude-flow hook pre-task --auto-spawn-agents

Benefits

🤖 Zero manual agent management
🎯 Perfect agent selection
📈 Dynamic scaling
💾 Resource efficiency

1.5 KiB Raw Blame History