analysis.md•1.98 kB
# Analysis Swarm Strategy
## Purpose
Comprehensive analysis through distributed agent coordination.
## Activation
### Using MCP Tools
```javascript
// 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
```javascript
// 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
```javascript
// 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
```javascript
// Monitor analysis progress
mcp__claude-flow__task_status({
"taskId": "analysis-task-001"
})
// Get analysis results
mcp__claude-flow__task_results({
"taskId": "analysis-task-001"
})
```