Name: analysis
Author: ziyabicilecommerce-hub

Prompt file imported from ziyabicilecommerce-hub/ai-cash-machine (.claude/commands/swarm/analysis.md). Copyright stays with the author.

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"
})

Install analysis and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

# 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/ziyabicilecommerce-hub/ai-cash-machine", "path": ".claude/commands/swarm/analysis.md", "ref": "e1e952ca4cb4f55e7ef39152b41fef1c07feee90", "url": "https://github.com/ziyabicilecommerce-hub/ai-cash-machine/blob/e1e952ca4cb4f55e7ef39152b41fef1c07feee90/.claude/commands/swarm/analysis.md", "key": "ziyabicilecommerce-hub/ai-cash-machine/.claude/commands/swarm/analysis.md" } } }

analysis

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