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swarm-advanced

Coordinate distributed workflows across multiple agents using swarm topologies.

1|Updated Nov 24, 2025
Search Tags:#testing#development#research#orchestration#distributed#swarm#coordination
One-click install
npx skills add https://github.com/Human-Frontier-Labs-Inc/parencylegal --skill swarm-advanced-human-frontier-labs-inc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/Human-Frontier-Labs-Inc/parencylegal/tree/main/corn-docs/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/Human-Frontier-Labs-Inc/parencylegal --skill swarm-advanced-human-frontier-labs-inc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Advanced swarm orchestration patterns for researchers, developers, and testers to coordinate distributed workflows.

Core Features & Use Cases

  • Advanced swarm orchestration across mesh, hierarchical, star, and ring topologies.
  • Phase-based task coordination, agent spawning, and parallel execution.
  • Built-in memory/state persistence, lifecycle management, and monitoring for long-running workflows.
  • Use Case: Coordinate a multi-team lab experiment requiring simultaneous data gathering, processing, and reporting.

Quick Start

Prerequisites:

  • Install Claude Flow globally: npm install -g claude-flow@alpha

Basic setup:

  • Initialize a swarm: mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })

  • Spawn agents as needed: mcp__claude-flow__agent_spawn({ type: "researcher", name: "Agent 1" })

  • Orchestrate tasks: mcp__claude-flow__task_orchestrate({ task: "coordinate research tasks", 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 coordinate distributed workflows across multiple agents using different network topologies?▼

You can coordinate distributed workflows by initializing a swarm with mesh, hierarchical, star, or ring topologies, spawning agents, and orchestrating tasks in parallel. This enables phase-based coordination for complex multi-agent executions.

What is phase-based orchestration for parallel agent execution?▼

Phase-based orchestration is a coordination mechanism that manages parallel task execution across spawned agents. It enables synchronized progress through distinct workflow stages, ensuring complex distributed tasks complete in the correct sequence.

Do I need Claude Flow installed to use swarm orchestration patterns?▼

Yes, Claude Flow is required and must be installed globally via npm install -g claude-flow@alpha before initializing swarms and spawning agents through the MCP tooling.

How do I set up a mesh topology swarm for parallel task coordination?▼

Initialize a mesh topology swarm using mcp__claude-flow__swarm_init with topology set to mesh and a defined maxAgents limit, then spawn agents and orchestrate tasks using a parallel execution strategy.

Can I persist memory and state for long-running distributed agent workflows?▼

Yes, built-in memory and state persistence allows long-running distributed workflows to maintain context and monitor progress across multiple agents throughout the execution phases.

What is the best way to monitor a multi-team lab experiment requiring simultaneous data gathering and processing?▼

The best way is using swarm orchestration with a parallel execution strategy, spawning researcher agents to coordinate data gathering, processing, and reporting tasks while tracking the lifecycle through built-in monitoring.

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