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swarm-advanced

Coordinate distributed agent swarms across multiple topologies and strategies.

Updated Sep 16, 2025
Search Tags:#testing#development#research#multi-agent#orchestration#parallel#distributed#swarm
One-click install
npx skills add https://github.com/ellisapotheosis/Project-Nyra --skill swarm-advanced-ellisapotheosis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/ellisapotheosis/Project-Nyra/tree/main/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/ellisapotheosis/Project-Nyra --skill swarm-advanced-ellisapotheosis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude-flow@alpha.

What problem does it solve?

This Skill provides advanced orchestration patterns for AI agent swarms, enabling them to tackle complex distributed workflows in research, development, and testing. It solves challenges related to agent coordination, task distribution, and fault tolerance, ensuring efficient and reliable execution of large-scale AI-powered projects.

Core Features & Use Cases

  • Diverse Swarm Topologies: Utilize Mesh, Hierarchical, Star, or Ring topologies for optimal coordination based on task type.
  • Specialized Agent Strategies: Employ Adaptive, Balanced, or Specialized strategies for dynamic workload management.
  • Fault Tolerance & Memory Management: Implement robust error handling and cross-session state persistence.
  • Use Case: Orchestrate a full-stack development project using a hierarchical swarm, where specialized agents (architects, coders, testers) work in parallel, with their progress monitored and decisions stored in a shared memory system.

Quick Start

Initialize a mesh topology swarm and spawn a researcher agent: mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 }) mcp__claude-flow__agent_spawn({ type: "researcher", name: "Agent 1" }) mcp__claude-flow__task_orchestrate({ task: "...", strategy: "parallel" })

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 in parallel across distributed tasks?▼

Orchestrate parallel agents by initializing a swarm topology (mesh, hierarchical, star, or ring), spawning specialized agents with specific roles, and configuring task distribution via parallel execution strategy. This enables simultaneous information gathering and workload balance across your workflow.

What swarm topologies work best for research, development, and testing workflows?▼

Swarm topologies—mesh, hierarchical, star, and ring—each suit different coordination needs. Hierarchical works for full-stack projects with specialized roles; mesh for peer collaboration; star for centralized control; ring for sequential workflows. Choose based on your task structure and agent communication pattern.

How do I manage agent state and memory across distributed swarm sessions?▼

Implement cross-session state persistence through shared memory storage integrated with your swarm. This preserves agent decisions, progress, and knowledge across sessions, enabling fault tolerance and continuous workflow execution without losing coordination state.

Can I use swarm orchestration for full-stack development projects with specialized roles?▼

Yes. Deploy a hierarchical swarm where specialized agents—architects, coders, testers—work in parallel with defined roles. Shared memory tracks decisions and progress while monitoring ensures coordinated execution across all development phases.

What agent strategies should I use for dynamic workload distribution?▼

Adaptive, balanced, and specialized strategies enable dynamic workload management. Adaptive adjusts to runtime conditions; balanced distributes evenly; specialized assigns distinct capabilities. Select based on whether your tasks need flexibility, equal distribution, or role-specific expertise.

How do MCP tools and CLI commands control swarm initialization and execution?▼

MCP tools and CLI commands satisfy functional and technical requirements for swarm control: initialize topology, spawn agents, configure orchestration, enable parallel execution, manage memory storage, monitor progress, and control workflow. They provide the interface for end-to-end swarm management.

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