swarm-advanced

Distributed agent orchestration coordinates multiple AI agents to execute tasks simultaneously using swarm topologies like mesh or hierarchical structures. This Skill enables parallel task execution, dynamic agent spawning, and role assignment across workflows, maximizing efficiency for research, development, and testing at scale.

Swarm topologies define how agents communicate and coordinate. This Skill supports Mesh (full connectivity), Hierarchical (manager-worker), Star (central hub), and Ring (sequential) architectures. Choose based on your workflow complexity, communication overhead, and fault tolerance requirements.

Yes. This Skill integrates MCP APIs and CLI commands for dynamic memory management, workflow control, and task orchestration. You can spawn agents, assign roles, manage task queues, and persist state across distributed workflows using these interfaces.

Adaptive task distribution assigns work to agents based on their specialization and current capacity. This Skill provides Adaptive, Balanced, Specialized, and Parallel strategies to route tasks intelligently, ensuring optimal resource utilization across research, development, and testing workflows.

Sequential orchestration executes tasks in order, with each completing before the next starts. Parallel orchestration runs multiple tasks simultaneously. This Skill supports both patterns plus adaptive strategies, letting you choose based on task dependencies and performance goals.

Yes. Claude Flow is a required dependency for this Skill. It provides the underlying framework for defining and executing distributed workflows, enabling the topology management and agent coordination this Skill orchestrates.