Claude Flow Swarm

Orchestrate multi-agent swarms with hierarchical, mesh, star, and ring topologies.

1|Updated Feb 8, 2026

Search Tags:#ai agents #orchestration #multi-agent systems #distributed computing #hive mind #swarm intelligence

Ready to run

npx skills add https://github.com/ricable/cli-skills-builder --skill claude-flow-swarm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Claude Flow Swarm
Source: https://github.com/ricable/cli-skills-builder/tree/main/.claude/skills/claude-flow-swarm
Command: npx skills add https://github.com/ricable/cli-skills-builder --skill claude-flow-swarm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the complex process of coordinating multiple AI agents, enabling them to work together efficiently on tasks through various topologies and consensus mechanisms.

Core Features & Use Cases

Multi-Agent Swarm Coordination: Manages swarms of up to 100+ agents.
Topology Support: Implements hierarchical, mesh, star, and ring network structures.
Hive-Mind Consensus: Facilitates distributed decision-making and task execution.
Use Case: When launching a complex project requiring diverse AI agents (e.g., coding, research, content generation), use this Skill to initialize, manage, and scale the agent swarm for optimal collaboration.

Quick Start

Initialize a hierarchical swarm with 8 specialized agents for coding tasks.

Frequently Asked Questions about Claude Flow Swarm

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

FAQPage Schema

How do I orchestrate multi-agent swarms for complex distributed computing tasks?▼

To orchestrate multi-agent swarms, this Skill coordinates 100+ AI agents using hierarchical, mesh, star, and ring topologies. It facilitates distributed decision-making and dynamic scaling for complex agent collaboration via the @claude-flow/cli interface.

What is hive-mind consensus and how does it work for AI agent coordination?▼

Hive-mind consensus is a distributed decision-making mechanism for AI agent coordination. It enables swarms of specialized agents to collectively process tasks and reach agreement on execution paths, streamlining complex collaborative workflows.

How do I initialize a hierarchical swarm of specialized agents for coding tasks?▼

You initialize a hierarchical swarm by configuring specialized agents through the @claude-flow/cli interface. This Skill sets up the network topology and manages the coordination, allowing multiple coding agents to collaborate efficiently on complex projects.

Can I use this to scale AI agent networks to 100 or more agents?▼

Yes, multi-agent swarm coordination supports dynamic scaling up to 100+ agents. You can manage large-scale distributed intelligence networks across various topologies, including star and mesh structures, to handle extensive project requirements.

What is the best way to manage distributed intelligence across different network topologies?▼

The best way to manage distributed intelligence is using topology-specific orchestration with hive-mind consensus. This Skill supports hierarchical, mesh, star, and ring network structures, enabling optimal agent coordination based on your project's collaboration needs.

When should I use a mesh topology instead of a hierarchical swarm for multi-agent systems?▼

A mesh topology suits multi-agent systems requiring decentralized, peer-to-peer collaboration, while a hierarchical swarm fits structured, top-down task delegation. This Skill orchestrates both, alongside star and ring topologies, to match your specific distributed computing requirements.