swarm-advanced

Manages distributed workflows with swarm orchestration across multiple topologies and agent strategies via CLI commands and MCP tools.

Updated Jun 5, 2026

Search Tags:#research automation #development automation #testing automation #swarm orchestration #advanced workflows

Ready to run

npx skills add https://github.com/LGugui/cerebro-template --skill swarm-advanced-lgugui

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/LGugui/cerebro-template/tree/main/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/LGugui/cerebro-template --skill swarm-advanced-lgugui

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude-flow, mcp, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing complex, distributed workflows by providing advanced swarm orchestration patterns for research, development, testing, and other complex tasks.

Core Features & Use Cases

Advanced Swarm Patterns: Utilizes mesh, hierarchical, star, and ring topologies for different workflow needs.
Agent Strategies: Implements adaptive, balanced, specialized, and parallel strategies for task execution.
Use Case: Ideal for research projects, full-stack development, comprehensive testing, and in-depth analysis.

Quick Start

To start an advanced swarm for research, use the command: claude mcp add claude-flow npx claude-flow@alpha mcp start and initialize the swarm with mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 }).

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 distributed swarm for complex development workflows?▼

Swarm orchestration coordinates distributed agent networks across mesh, hierarchical, star, and ring topologies. It manages complex workflows by applying adaptive, balanced, specialized, or parallel agent strategies for research, development, testing, and analysis tasks.

How do I initialize an advanced swarm using MCP and CLI commands?▼

Initialize an advanced swarm by running `claude mcp add claude-flow npx claude-flow@alpha mcp start` to configure MCP tools, then call `mcp__claude-flow__swarm_init` with parameters like topology and maxAgents to orchestrate distributed agents.

What swarm topologies and agent strategies are available for distributed task execution?▼

Available swarm topologies include mesh, hierarchical, star, and ring configurations. Agent strategies encompass adaptive, balanced, specialized, and parallel execution modes to handle diverse distributed workflow requirements across research and testing tasks.

Do I need MCP tools and claude-flow to run advanced swarm orchestration?▼

Yes, advanced swarm orchestration requires MCP tools and claude-flow dependencies to function. The MCP server provides the orchestration interface, while claude-flow supplies the CLI commands needed to initialize and manage distributed agent networks.

When should I use mesh topology versus a hierarchical swarm for research automation?▼

Mesh topology suits collaborative research automation requiring peer-to-peer agent communication across distributed workflows. Hierarchical swarms benefit structured development and testing tasks where centralized coordination and specialized agent roles improve execution efficiency.