96 lines
1.9 KiB
Markdown
96 lines
1.9 KiB
Markdown
# Analysis Swarm Strategy
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## Purpose
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Comprehensive analysis through distributed agent coordination.
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## Activation
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### Using MCP Tools
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```javascript
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// Initialize analysis swarm
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mcp__claude-flow__swarm_init({
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"topology": "mesh",
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"maxAgents": 6,
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"strategy": "adaptive"
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})
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// Orchestrate analysis task
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mcp__claude-flow__task_orchestrate({
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"task": "analyze system performance",
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"strategy": "parallel",
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"priority": "medium"
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})
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```
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### Using CLI (Fallback)
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`npx claude-flow swarm "analyze system performance" --strategy analysis`
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## Agent Roles
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### Agent Spawning with MCP
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```javascript
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// Spawn analysis agents
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mcp__claude-flow__agent_spawn({
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"type": "analyst",
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"name": "Data Collector",
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"capabilities": ["metrics", "logging", "monitoring"]
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})
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mcp__claude-flow__agent_spawn({
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"type": "analyst",
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"name": "Pattern Analyzer",
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"capabilities": ["pattern-recognition", "anomaly-detection"]
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})
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mcp__claude-flow__agent_spawn({
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"type": "documenter",
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"name": "Report Generator",
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"capabilities": ["reporting", "visualization"]
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})
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mcp__claude-flow__agent_spawn({
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"type": "coordinator",
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"name": "Insight Synthesizer",
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"capabilities": ["synthesis", "correlation"]
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})
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```
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## Coordination Modes
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- Mesh: For exploratory analysis
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- Pipeline: For sequential processing
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- Hierarchical: For complex systems
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## Analysis Operations
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```javascript
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// Run performance analysis
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mcp__claude-flow__performance_report({
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"format": "detailed",
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"timeframe": "24h"
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})
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// Identify bottlenecks
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mcp__claude-flow__bottleneck_analyze({
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"component": "api",
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"metrics": ["response-time", "throughput"]
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})
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// Pattern recognition
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mcp__claude-flow__pattern_recognize({
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"data": performanceData,
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"patterns": ["anomaly", "trend", "cycle"]
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})
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```
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## Status Monitoring
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```javascript
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// Monitor analysis progress
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mcp__claude-flow__task_status({
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"taskId": "analysis-task-001"
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})
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// Get analysis results
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mcp__claude-flow__task_results({
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"taskId": "analysis-task-001"
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})
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```
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