wifi-densepose/.claude/commands/analysis/bottleneck-detect.md

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:

1. Topology Optimization

   • Switch to more efficient topology
   • Adjust communication patterns
   • Reduce coordination overhead

2. Caching Enhancement

   • Enable memory caching
   • Optimize cache strategies
   • Preload common patterns

3. Concurrency Tuning

   • Adjust agent counts
   • Optimize parallel execution
   • Balance workload distribution

4. 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
}

See Also

• performance report - Detailed performance analysis
• token usage - Token optimization analysis
• swarm monitor - Real-time monitoring
• cache manage - Cache optimization