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swarm-advanced

Coordinate distributed swarm agents across mesh, hierarchical, star, and ring topologies.

5|Updated Oct 23, 2025
Search Tags:#workflow#multi-agent#orchestration#parallel#distributed#swarm#fault tolerance
One-click install
npx skills add https://github.com/wrsmith108/figma-docker-init --skill swarm-advanced-wrsmith108
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/wrsmith108/figma-docker-init/tree/main/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/wrsmith108/figma-docker-init --skill swarm-advanced-wrsmith108

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude-flow@alpha.

What problem does it solve?

Coordinating multiple AI agents for complex, distributed tasks like full-stack development or deep research can be challenging without advanced orchestration. This Skill provides comprehensive patterns and techniques for managing AI swarms, enabling efficient parallel execution, fault tolerance, and intelligent resource allocation across various topologies.

Core Features & Use Cases

  • Advanced Swarm Topologies: Utilize Mesh (research), Hierarchical (development), Star (testing), and Ring (pipelines) topologies for optimal task execution.
  • Specialized Agent Strategies: Employ Adaptive, Balanced, Specialized, and Parallel strategies for dynamic workload distribution and task assignment.
  • Fault Tolerance & Memory Management: Implement robust error handling, cross-session memory persistence, and state snapshots for resilient operations.
  • Use Case: Orchestrate a full-stack application development project using a hierarchical swarm. Spawn specialized agents (architect, backend/frontend coder, tester, reviewer), then coordinate parallel implementation, comprehensive testing, and automated code reviews, all while monitoring progress and managing memory.

Quick Start

Initialize a mesh swarm with 6 agents, spawn a specialized "researcher" agent, and then orchestrate a task using a parallel strategy.

Frequently Asked Questions about swarm-advanced

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multiple AI agents across distributed systems?▼

Multi-agent orchestration coordinates specialized agents across mesh, hierarchical, star, and ring topologies using MCP API calls like mcp__claude-flow__swarm_init, mcp__claude-flow__agent_spawn, and mcp__claude-flow__task_orchestrate. This enables parallel execution, sequential workflows, and validated task completion with fault tolerance and memory persistence across distributed environments.

What swarm topologies work best for different development scenarios?▼

Swarm topologies optimize for specific tasks: mesh topologies suit research workflows, hierarchical topologies coordinate full-stack development with specialized agents, star topologies centralize testing operations, and ring topologies pipeline sequential processing. Each topology enables different agent strategies—adaptive, balanced, specialized, or parallel—for dynamic workload distribution.

How do I implement fault tolerance and state management in multi-agent workflows?▼

Implement fault tolerance through robust error handling, cross-session memory persistence, and state snapshots. The Skill manages agent state across parallel execution, enabling resilient operations where workflows continue despite failures and memory survives session boundaries for long-running distributed tasks.

Can I run parallel agent tasks with progress monitoring and automated reviews?▼

Yes. Spawn specialized agents for different roles—architect, coder, tester, reviewer—and orchestrate parallel implementation using parallel strategy execution. The Skill coordinates comprehensive testing and automated code reviews while monitoring progress and managing resource allocation across all spawned agents.

What's the entry point for initializing a multi-agent swarm workflow?▼

Initialize a swarm using mcp__claude-flow__swarm_init with topology configuration (mesh, hierarchical, star, or ring), then spawn specialized agents via mcp__claude-flow__agent_spawn, and orchestrate tasks with mcp__claude-flow__task_orchestrate. These MCP API calls establish the distributed agent network and task coordination framework.

Does this Skill require claude-flow dependency for agent orchestration?▼

Yes. The Skill depends on claude-flow@alpha for all orchestration capabilities. Claude-flow provides the MCP API surface and runtime for swarm initialization, agent spawning, task orchestration, and memory management across distributed agent topologies.

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