2.3 KiB
2.3 KiB
Optimization Swarm Strategy
Purpose
Performance optimization through specialized analysis.
Activation
Using MCP Tools
// Initialize optimization swarm
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 6,
"strategy": "adaptive"
})
// Orchestrate optimization task
mcp__claude-flow__task_orchestrate({
"task": "optimize performance",
"strategy": "parallel",
"priority": "high"
})
Using CLI (Fallback)
npx claude-flow swarm "optimize performance" --strategy optimization
Agent Roles
Agent Spawning with MCP
// Spawn optimization agents
mcp__claude-flow__agent_spawn({
"type": "optimizer",
"name": "Performance Profiler",
"capabilities": ["profiling", "bottleneck-detection"]
})
mcp__claude-flow__agent_spawn({
"type": "analyst",
"name": "Memory Analyzer",
"capabilities": ["memory-analysis", "leak-detection"]
})
mcp__claude-flow__agent_spawn({
"type": "optimizer",
"name": "Code Optimizer",
"capabilities": ["code-optimization", "refactoring"]
})
mcp__claude-flow__agent_spawn({
"type": "tester",
"name": "Benchmark Runner",
"capabilities": ["benchmarking", "performance-testing"]
})
Optimization Areas
Performance Analysis
// Analyze bottlenecks
mcp__claude-flow__bottleneck_analyze({
"component": "all",
"metrics": ["cpu", "memory", "io", "network"]
})
// Run benchmarks
mcp__claude-flow__benchmark_run({
"suite": "performance"
})
// WASM optimization
mcp__claude-flow__wasm_optimize({
"operation": "simd-acceleration"
})
Optimization Operations
// Optimize topology
mcp__claude-flow__topology_optimize({
"swarmId": "optimization-swarm"
})
// DAA optimization
mcp__claude-flow__daa_optimization({
"target": "performance",
"metrics": ["speed", "memory", "efficiency"]
})
// Load balancing
mcp__claude-flow__load_balance({
"swarmId": "optimization-swarm",
"tasks": optimizationTasks
})
Monitoring and Reporting
// Performance report
mcp__claude-flow__performance_report({
"format": "detailed",
"timeframe": "7d"
})
// Trend analysis
mcp__claude-flow__trend_analysis({
"metric": "performance",
"period": "30d"
})
// Cost analysis
mcp__claude-flow__cost_analysis({
"timeframe": "30d"
})