swarm-advanced

# Advanced Swarm Orchestration Master advanced swarm patterns for distributed research, development, and testing workflows. This skill covers comprehensive orchestration strategies using both MCP tools and CLI commands. ## Quick Start ### Prerequisites ```bash # Ensure Claude Flow is installed npm install -g claude-flow@alpha # Add MCP server (if using MCP tools) claude mcp add claude-flow npx claude-flow@alpha mcp start ``` ### Basic Pattern ```javascript // 1. Initialize swarm topology mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 }) // 2. Spawn specialized agents mcp__claude-flow__agent_spawn({ type: "researcher", name: "Agent 1" }) // 3. Orchestrate tasks mcp__claude-flow__task_orchestrate({ task: "...", strategy: "parallel" }) ``` ## Core Concepts ### Swarm Topologies **Mesh Topology** - Peer-to-peer communication, best for research and analysis - All agents communicate directly - High flexibility and resilience - Use for: Research, analysis, brainstorming **Hierarchical Topology** - Coordinator with subordinates, best for development - Clear command structure - Sequential workflow support - Use for: Development, structured workflows **Star Topology** - Central coordinator, best for testing - Centralized control and monitoring - Parallel execution with coordination - Use for: Testing, validation, quality assurance **Ring Topology** - Sequential processing chain - Step-by-step processing - Pipeline workflows - Use for: Multi-stage processing, data pipelines ### Agent Strategies **Adaptive** - Dynamic adjustment based on task complexity **Balanced** - Equal distribution of work across agents **Specialized** - Task-specific agent assignment **Parallel** - Maximum concurrent execution ## Pattern 1: Research Swarm ### Purpose Deep research through parallel information gathering, analysis, and synthesis. ### Architecture ```javascript // Initialize research swarm mcp__claude-flow__swarm_init({ "topology": "mesh", "maxAgents": 6, "strategy": "adaptive" }) // Spawn research team const researchAgents = [ { type: "researcher", name: "Web Researcher", capabilities: ["web-search", "content-extraction", "source-validation"] }, { type: "researcher", name: "Academic Researcher", capabilities: ["paper-analysis", "citation-tracking", "literature-review"] }, { type: "analyst", name: "Data Analyst", capabilities: ["data-processing", "statistical-analysis", "visualization"] }, { type: "analyst", name: "Pattern Analyzer", capabilities: ["trend-detection", "correlation-analysis", "outlier-detection"] }, { type: "documenter", name: "Report Writer", capabilities: ["synthesis", "technical-writing", "formatting"] } ] // Spawn all agents researchAgents.forEach(agent => { mcp__claude-flow__agent_spawn({ type: agent.type, name: agent.name, capabilities: agent.capabilities }) }) ``` ### Research Workflow #### Phase 1: Information Gathering ```javascript // Parallel information collection mcp__claude-flow__parallel_execute({ "tasks": [ { "id": "web-search", "command": "search recent publications and articles" }, { "id": "academic-search", "command": "search academic databases and papers" }, { "id": "data-collection", "command": "gather relevant datasets and statistics" }, { "id": "expert-search", "command": "identify domain experts and thought leaders" } ] }) // Store research findings in memory mcp__claude-flow__memory_usage({ "action": "store", "key": "research-findings-" + Date.now(), "value": JSON.stringify(findings), "namespace": "research", "ttl": 604800 // 7 days }) ``` #### Phase 2: Analysis and Validation ```javascript // Pattern recognition in findings mcp__claude-flow__pattern_recognize({ "data": researchData, "patterns": ["trend", "correlation", "outlier", "emerging-pattern"] }) // Cognitive analysis mcp__claude-flow__cognitive_analyze({ "behavior": "research-synthesis" }) // Quality assessment mcp__claude-flow__quality_assess({ "target": "research-sources", "criteria": ["credibility", "relevance", "recency", "authority"] }) // Cross-reference validation mcp__claude-flow__neural_patterns({ "action": "analyze", "operation": "fact-checking", "metadata": { "sources": sourcesArray } }) ``` #### Phase 3: Knowledge Management ```javascript // Search existing knowledge base mcp__claude-flow__memory_search({ "pattern": "topic X", "namespace": "research", "limit": 20 }) // Create knowledge graph connections mcp__claude-flow__neural_patterns({ "action": "learn", "operation": "knowledge-graph", "metadata": { "topic": "X", "connections": relatedTopics, "depth": 3 } }) // Store connections for future use mcp__claude-flow__memory_usage({ "action": "store", "key": "knowledge-graph-X", "value": JSON.stringify(knowledgeGraph), "namespace": "research/graphs", "ttl": 2592000 // 30 days }) ``` #### Phase 4: Report Generation ```javascript // Orchestrate report generation mcp__claude-flow__task_orchestrate({ "task": "generate comprehensive research report", "strategy": "sequential", "priority": "high", "dependencies": ["gather", "analyze", "validate", "synthesize"] }) // Monitor research progress mcp__claude-flow__swarm_status({ "swarmId": "research-swarm" }) // Generate final report mcp__claude-flow__workflow_execute({ "workflowId": "research-report-generation", "params": { "findings": findings, "format": "comprehensive", "sections": ["executive-summary", "methodology", "findings", "analysis", "conclusions", "references"] } }) ``` ### CLI Fallback ```bash # Quick research swarm npx @claude-flow/cli@latest swarm "research AI trends in 2025" \ --strategy research \ --mode distributed \ --max-agents 6 \ --parallel \ --output research-report.md ``` ## Pattern 2: Development Swarm ### Purpose Full-stack development through coordinated specialist agents. ### Architecture ```javascript // Initialize development swarm with hierarchy mcp__claude-flow__swarm_init({ "topology": "hierarchical", "maxAgents": 8, "strategy": "balanced" }) // Spawn development team const devTeam = [ { type: "architect", name: "System Architect", role: "coordinator" }, { type: "coder", name: "Backend Developer", capabilities: ["node", "api", "database"] }, { type: "coder", name: "Frontend Developer", capabilities: ["react", "ui", "ux"] }, { type: "coder", name: "Database Engineer", capabilities: ["sql", "nosql", "optimization"] }, { type: "tester", name: "QA Engineer", capabilities: ["unit", "integration", "e2e"] }, { type: "reviewer", name: "Code Reviewer", capabilities: ["security", "performance", "best-practices"] }, { type: "documenter", name: "Technical Writer", capabilities: ["api-docs", "guides", "tutorials"] }, { type: "monitor", name: "DevOps Engineer", capabilities: ["ci-cd", "deployment", "monitoring"] } ] // Spawn all team members devTeam.forEach(member => { mcp__claude-flow__agent_spawn({ type: member.type, name: member.name, capabilities: member.capabilities, swarmId: "dev-swarm" }) }) ``` ### Development Workflow #### Phase 1: Architecture and Design ```javascript // System architecture design mcp__claude-flow__task_orchestrate({ "task": "design system architecture for REST API", "strategy": "sequential", "priority": "critical", "assignTo": "System Architect" }) // Store architecture decisions mcp__claude-flow__memory_usage({ "action": "store", "key": "architecture-decisions", "value": JSON.stringify(architectureDoc), "namespace": "development/design" }) ``` #### Phase 2: Parallel Implementation ```javascript // Parallel development tasks mcp__claude-flow__parallel_execute({ "tasks": [ { "id": "backend-api", "command": "implement REST API endpoints", "assignTo": "Backend Developer" }, { "id": "frontend-ui", "command": "build user interface components", "assignTo": "Frontend Developer" }, { "id": "database-schema", "command": "design and implement database schema", "assignTo": "Database Engineer" }, { "id": "api-documentation", "command": "create API documentation", "assignTo": "Technical Writer" } ] }) // Monitor development progress mcp__claude-flow__swarm_monitor({ "swarmId": "dev-swarm", "interval": 5000 }) ``` #### Phase 3: Testing and Validation ```javascript // Comprehensive testing mcp__claude-flow__batch_process({ "items": [ { type: "unit", target: "all-modules" }, { type: "integration", target: "api-endpoints" }, { type: "e2e", target: "user-flows" }, { type: "performance", target: "critical-paths" } ], "operation": "execute-tests" }) // Quality assessment mcp__claude-flow__quality_assess({ "target": "codebase", "criteria": ["coverage", "complexity", "maintainability", "security"] }) ``` #### Phase 4: Review and Deployment ```javascript // Code review workflow mcp__claude-flow__workflow_execute({ "workflowId": "code-review-process", "params": { "reviewers": ["Code Reviewer"], "criteria": ["security", "performance", "best-practices"] } }) // CI/CD pipeline mcp__claude-flow__pipeline_create({ "config": { "stages": ["build", "test", "security-scan", "deploy"], "environment": "production" } }) ``` ### CLI Fallback ```bash