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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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bottleneck detect

Analyze performance bottlenecks in swarm operations and suggest optimizations.

Usage

npx claude-flow bottleneck detect [options]

Options

--swarm-id, -s <id> - Analyze specific swarm (default: current)
--time-range, -t <range> - Analysis period: 1h, 24h, 7d, all (default: 1h)
--threshold <percent> - Bottleneck threshold percentage (default: 20)
--export, -e <file> - Export analysis to file
--fix - Apply automatic optimizations

Examples

Basic bottleneck detection

npx claude-flow bottleneck detect

Analyze specific swarm

npx claude-flow bottleneck detect --swarm-id swarm-123

Last 24 hours with export

npx claude-flow bottleneck detect -t 24h -e bottlenecks.json

Auto-fix detected issues

npx claude-flow bottleneck detect --fix --threshold 15

Metrics Analyzed

Communication Bottlenecks

Message queue delays
Agent response times
Coordination overhead
Memory access patterns

Processing Bottlenecks

Task completion times
Agent utilization rates
Parallel execution efficiency
Resource contention

Memory Bottlenecks

Cache hit rates
Memory access patterns
Storage I/O performance
Neural pattern loading

Network Bottlenecks

API call latency
MCP communication delays
External service timeouts
Concurrent request limits

Output Format

🔍 Bottleneck Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 Summary
├── Time Range: Last 1 hour
├── Agents Analyzed: 6
├── Tasks Processed: 42
└── Critical Issues: 2

🚨 Critical Bottlenecks
1. Agent Communication (35% impact)
   └── coordinator → coder-1 messages delayed by 2.3s avg

2. Memory Access (28% impact)
   └── Neural pattern loading taking 1.8s per access

⚠️ Warning Bottlenecks
1. Task Queue (18% impact)
   └── 5 tasks waiting > 10s for assignment

💡 Recommendations
1. Switch to hierarchical topology (est. 40% improvement)
2. Enable memory caching (est. 25% improvement)
3. Increase agent concurrency to 8 (est. 20% improvement)

✅ Quick Fixes Available
Run with --fix to apply:
- Enable smart caching
- Optimize message routing
- Adjust agent priorities

Automatic Fixes

When using --fix, the following optimizations may be applied:

Topology Optimization
- Switch to more efficient topology
- Adjust communication patterns
- Reduce coordination overhead
Caching Enhancement
- Enable memory caching
- Optimize cache strategies
- Preload common patterns
Concurrency Tuning
- Adjust agent counts
- Optimize parallel execution
- Balance workload distribution
Priority Adjustment
- Reorder task queues
- Prioritize critical paths
- Reduce wait times

Performance Impact

Typical improvements after bottleneck resolution:

Communication: 30-50% faster message delivery
Processing: 20-40% reduced task completion time
Memory: 40-60% fewer cache misses
Overall: 25-45% performance improvement

Integration with Claude Code

// Check for bottlenecks in Claude Code
mcp__claude-flow__bottleneck_detect {
  timeRange: "1h",
  threshold: 20,
  autoFix: false
}

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