What problem does it solve? Coordinating multiple AI agents across complex tasks like research, full-stack development, and testing requires manual orchestration, which is error-prone and hard to scale. This Skill provides structured patterns for initializing, coordinating, and monitoring agent swarms using Claude Flow MCP tools and CLI commands. ## Core Features & Use Cases - Swarm Topologies: Configure mesh, hierarchical, star, or ring topologies matched to research, development, testing, or pipeline workflows. - Parallel Orchestration: Spawn specialized agents (researchers, coders, testers, analysts) and execute tasks in parallel with memory persistence and state snapshots. - Fault Tolerance & Learning: Apply auto-recovery strategies, neural pattern training, and performance monitoring to keep swarms healthy. - Use Case: Spin up a hierarchical development swarm with an architect, backend and frontend developers, testers, and a reviewer to design, implement, test, and deploy a REST API in coordinated phases. ## Quick Start Initialize a mesh swarm with six agents and orchestrate a parallel research task on a topic of your choice.
