What problem does it solve?
Coordinating multiple AI agents for complex, distributed tasks can be inefficient and difficult to manage. This Skill provides advanced swarm orchestration patterns and strategies, enabling efficient parallel execution, robust error handling, and continuous learning for research, development, and testing workflows.
Core Features & Use Cases
- Diverse Swarm Topologies: Choose from Mesh (research), Hierarchical (development), Star (testing), or Ring (pipelines) to optimize agent communication and coordination.
- Specialized Agent Strategies: Deploy agents with Adaptive, Balanced, or Specialized strategies for optimal task distribution and execution.
- Memory & State Management: Ensure cross-session persistence, create state snapshots, and manage knowledge graphs for continuous context.
- Use Case: Orchestrate a full-stack application development. A hierarchical swarm is initialized with specialized agents (architect, backend, frontend, tester, reviewer). Tasks are distributed in parallel, architecture decisions are stored in shared memory, and a comprehensive testing swarm validates the output, ensuring a high-quality product.
Quick Start
1. Initialize swarm topology (e.g., mesh)
mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
2. Spawn specialized agents (e.g., researcher)
mcp__claude-flow__agent_spawn({ type: "researcher", name: "Agent 1" })
3. Orchestrate tasks (e.g., parallel execution)
mcp__claude-flow__task_orchestrate({ task: "research AI trends", strategy: "parallel" })
