Prompt file imported from BURHANDEV-ENTERPRISE/BURHAN-WEB-DEV (.claude/commands/swarm/analysis.md). Copyright stays with the author.

Analysis Swarm Strategy

Name: analysis
Author: BURHANDEV-ENTERPRISE

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"
})

# 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" }) ```

{ "prompt": "# Analysis Swarm Strategy\r\n\r\n## Purpose\r\nComprehensive analysis through distributed agent coordination.\r\n\r\n## Activation\r\n\r\n### Using MCP Tools\r\n```javascript\r\n// Initialize analysis swarm\r\nmcp__claude-flow__swarm_init({\r\n \"topology\": \"mesh\",\r\n \"maxAgents\": 6,\r\n \"strategy\": \"adaptive\"\r\n})\r\n\r\n// Orchestrate analysis task\r\nmcp__claude-flow__task_orchestrate({\r\n \"task\": \"analyze system performance\",\r\n \"strategy\": \"parallel\",\r\n \"priority\": \"medium\"\r\n})\r\n```\r\n\r\n### Using CLI (Fallback)\r\n`npx claude-flow swarm \"analyze system performance\" --strategy analysis`\r\n\r\n## Agent Roles\r\n\r\n### Agent Spawning with MCP\r\n```javascript\r\n// Spawn analysis agents\r\nmcp__claude-flow__agent_spawn({\r\n \"type\": \"analyst\",\r\n \"name\": \"Data Collector\",\r\n \"capabilities\": [\"metrics\", \"logging\", \"monitoring\"]\r\n})\r\n\r\nmcp__claude-flow__agent_spawn({\r\n \"type\": \"analyst\",\r\n \"name\": \"Pattern Analyzer\",\r\n \"capabilities\": [\"pattern-recognition\", \"anomaly-detection\"]\r\n})\r\n\r\nmcp__claude-flow__agent_spawn({\r\n \"type\": \"documenter\",\r\n \"name\": \"Report Generator\",\r\n \"capabilities\": [\"reporting\", \"visualization\"]\r\n})\r\n\r\nmcp__claude-flow__agent_spawn({\r\n \"type\": \"coordinator\",\r\n \"name\": \"Insight Synthesizer\",\r\n \"capabilities\": [\"synthesis\", \"correlation\"]\r\n})\r\n```\r\n\r\n## Coordination Modes\r\n- Mesh: For exploratory analysis\r\n- Pipeline: For sequential processing\r\n- Hierarchical: For complex systems\r\n\r\n## Analysis Operations\r\n```javascript\r\n// Run performance analysis\r\nmcp__claude-flow__performance_report({\r\n \"format\": \"detailed\",\r\n \"timeframe\": \"24h\"\r\n})\r\n\r\n// Identify bottlenecks\r\nmcp__claude-flow__bottleneck_analyze({\r\n \"component\": \"api\",\r\n \"metrics\": [\"response-time\", \"throughput\"]\r\n})\r\n\r\n// Pattern recognition\r\nmcp__claude-flow__pattern_recognize({\r\n \"data\": performanceData,\r\n \"patterns\": [\"anomaly\", \"trend\", \"cycle\"]\r\n})\r\n```\r\n\r\n## Status Monitoring\r\n```javascript\r\n// Monitor analysis progress\r\nmcp__claude-flow__task_status({\r\n \"taskId\": \"analysis-task-001\"\r\n})\r\n\r\n// Get analysis results\r\nmcp__claude-flow__task_results({\r\n \"taskId\": \"analysis-task-001\"\r\n})\r\n```", "variables": [], "notes": "// Orchestrate analysis task mcp__claude-flow__task_orchestrate({ \"task\": \"analyze system performance\", \"strategy\": \"parallel\", \"priority\": \"medium\" }) ```", "metadata": { "source": { "provider": "github-claude-commands", "repository": "https://github.com/BURHANDEV-ENTERPRISE/BURHAN-WEB-DEV", "path": ".claude/commands/swarm/analysis.md", "ref": "08de06f5ac73e8d9d292245d33356e02eaa19f76", "url": "https://github.com/BURHANDEV-ENTERPRISE/BURHAN-WEB-DEV/blob/08de06f5ac73e8d9d292245d33356e02eaa19f76/.claude/commands/swarm/analysis.md", "key": "BURHANDEV-ENTERPRISE/BURHAN-WEB-DEV/.claude/commands/swarm/analysis.md" } } }

analysis

About