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swarm-advanced

Automate multi-swarm orchestration across mesh, hierarchical, and star topologies.

2|Updated Jul 26, 2019
Search Tags:#orchestration#swarm#topology#claude-flow#parallel-execution#agent-management#MCP-tools
One-click install
npx skills add https://github.com/qiphon/learn --skill swarm-advanced-qiphon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/qiphon/learn/tree/main/.opencode/skills/swarm-advanced
Command: npx skills add https://github.com/qiphon/learn --skill swarm-advanced-qiphon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies creating and coordinating multiple autonomous agents across distributed experiments, reducing manual orchestration effort and accelerating complex workflows.

Core Features & Use Cases

  • Multi-topology swarm orchestration (mesh, hierarchical, star) for diverse research, development, and testing contexts
  • Dynamic agent spawning and role assignment to assemble specialized teams
  • Centralized monitoring, status tracking, and fault-tolerance mechanisms
  • Memory management and knowledge graph integration to reuse outcomes across runs
  • CLI/MCP-based workflow automation for repeatable pipelines

Quick Start

Install Claude Flow and MCP tooling, then initialize your first swarm and spawn agents with the provided commands. For example:

  • Initialize a mesh swarm: mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
  • Spawn agents for a research task: mcp__claude-flow__agent_spawn({ type: "researcher", name: "Web Researcher" })
  • Orchestrate a parallel task: mcp__claude-flow__parallel_execute({ "tasks": [ { "id": "web-search", "command": "search web" } ] }) Note: See the Skill's SKILL.md for full patterns and examples.

Frequently Asked Questions about swarm-advanced

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

FAQPage Schema
How do I orchestrate a multi-agent swarm for parallel research and testing workflows?▼

Multi-agent swarm orchestration coordinates distributed autonomous agents across research and testing pipelines using Claude Flow MCP tools. It supports mesh, hierarchical, and star topologies to automate complex workflows while reducing manual coordination effort.

What swarm topologies are supported for distributed agent management?▼

Supported swarm topologies include mesh, hierarchical, and star configurations. These topology patterns allow diverse structural arrangements for coordinating multi-agent teams across distributed research, development pipelines, and QA testing contexts.

How do I spawn and assign roles to autonomous agents in a mesh swarm?▼

Spawn agents and assign roles using the Claude Flow MCP agent_spawn command, specifying agent type and name. This dynamic spawning assembles specialized teams within your initialized swarm topology for targeted task execution.

Does Claude Flow support fault tolerance and monitoring for parallel execution?▼

Claude Flow supports fault tolerance and centralized monitoring for parallel execution. It provides status tracking and fault-tolerance mechanisms to manage automated parallel tasks and maintain stability across distributed multi-swarm scenarios.

Can I reuse knowledge and memory across multiple swarm orchestration runs?▼

Reuse knowledge and memory across multiple swarm orchestration runs using integrated memory management. The system incorporates knowledge graph integration to persist outcomes, enabling repeatable CLI-based pipelines and sustained context across separate executions.

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