/analysis

Analysis Swarm Strategy

Purpose

Comprehensive analysis through distributed agent coordination.

Activation

Using MCP Tools

// Initialize analysis swarm
mcp__claude-flow__swarm_init({
  "topology": "mesh",
  "maxAgents": 6,
  "strategy": "adaptive"
})

// Orchestrate analysis task
mcp__claude-flow__task_orchestrate({
  "task": "analyze system performance",
  "strategy": "parallel",
  "priority": "medium"
})

Using CLI (Fallback)

`npx claude-flow swarm "analyze system performance" --strategy analysis`

Agent Roles

Agent Spawning with MCP

// Spawn analysis agents
mcp__claude-flow__agent_spawn({
  "type": "analyst",
  "name": "Data Collector",
  "capabilities": ["metrics", "logging", "monitoring"]
})

mcp__claude-flow__agent_spawn({
  "type": "analyst",
  "name": "Pattern Analyzer",
  "capabilities": ["pattern-recognition", "anomaly-detection"]
})

mcp__claude-flow__agent_spawn({
  "type": "documenter",
  "name": "Report Generator",
  "capabilities": ["reporting", "visualization"]
})

mcp__claude-flow__agent_spawn({
  "type": "coordinator",
  "name": "Insight Synthesizer",
  "capabilities": ["synthesis", "correlation"]
})

Coordination Modes

• Mesh: For exploratory analysis
• Pipeline: For sequential processing
• Hierarchical: For complex systems

Analysis Operations

// Run performance analysis
mcp__claude-flow__performance_report({
  "format": "detailed",
  "timeframe": "24h"
})

// Identify bottlenecks
mcp__claude-flow__bottleneck_analyze({
  "component": "api",
  "metrics": ["response-time", "throughput"]
})

// Pattern recognition
mcp__claude-flow__pattern_recognize({
  "data": performanceData,
  "patterns": ["anomaly", "trend", "cycle"]
})

Status Monitoring

// Monitor analysis progress
mcp__claude-flow__task_status({
  "taskId": "analysis-task-001"
})

// Get analysis results
mcp__claude-flow__task_results({
  "taskId": "analysis-task-001"
})