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

# 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

// 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


## Reference


The full detail lives in `references/` and loads only when needed:

- [`references/patterns.md`](references/patterns.md) — research / development / testing / analysis swarm patterns & advanced techniques.

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## Best Practices

### 1. Choosing the Right Topology

- **Mesh**: Research, brainstorming, collaborative analysis
- **Hierarchical**: Structured development, sequential workflows
- **Star**: Testing, validation, centralized coordination
- **Ring**: Pipeline processing, staged workflows

### 2. Agent Specialization

- Assign specific capabilities to each agent
- Avoid overlapping responsibilities
- Use coordination agents for complex workflows
- Leverage memory for agent communication

### 3. Parallel Execution

- Identify independent tasks for parallelization
- Use sequential execution for dependent tasks
- Monitor resource usage during parallel execution
- Implement proper error handling

### 4. Memory Management

- Use namespaces to organize memory
- Set appropriate TTL values
- Create regular backups
- Implement state snapshots for checkpoints

### 5. Monitoring and Optimization

- Monitor swarm health regularly
- Collect and analyze metrics
- Optimize topology based on performance
- Use neural patterns to learn from success

### 6. Error Recovery

- Implement fault tolerance strategies
- Use auto-recovery mechanisms
- Analyze error patterns
- Create fallback workflows

## Real-World Examples

### Example 1: AI Research Project
```javascript
// Research AI trends, analyze findings, generate report
mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
// Spawn: 2 researchers, 2 analysts, 1 synthesizer, 1 documenter
// Parallel gather → Analyze patterns → Synthesize → Report

Example 2: Full-Stack Application

// Build complete web application with testing
mcp__claude-flow__swarm_init({ topology: "hierarchical", maxAgents: 8 })
// Spawn: 1 architect, 2 devs, 1 db engineer, 2 testers, 1 reviewer, 1 devops
// Design → Parallel implement → Test → Review → Deploy

Example 3: Security Audit

// Comprehensive security analysis
mcp__claude-flow__swarm_init({ topology: "star", maxAgents: 5 })
// Spawn: 1 coordinator, 1 code analyzer, 1 security scanner, 1 penetration tester, 1 reporter
// Parallel scan → Vulnerability analysis → Penetration test → Report

Example 4: Performance Optimization

// Identify and fix performance bottlenecks
mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 4 })
// Spawn: 1 profiler, 1 bottleneck analyzer, 1 optimizer, 1 tester
// Profile → Identify bottlenecks → Optimize → Validate

Troubleshooting

Common Issues

Issue: Swarm agents not coordinating properly Solution: Check topology selection, verify memory usage, enable monitoring

Issue: Parallel execution failing Solution: Verify task dependencies, check resource limits, implement error handling

Issue: Memory persistence not working Solution: Verify namespaces, check TTL settings, ensure backup configuration

Issue: Performance degradation Solution: Optimize topology, reduce agent count, analyze bottlenecks

Related Skills

sparc-methodology - Systematic development workflow
github-integration - Repository management and automation
neural-patterns - AI-powered coordination optimization
memory-management - Cross-session state persistence

References

Version: 2.0.0 Last Updated: 2025-10-19 Skill Level: Advanced Estimated Learning Time: 2-3 hours