swarm-advanced

Orchestrate distributed multi-agent swarms for parallel research and development workflows.

Updated Dec 12, 2025

Search Tags:#automation #multi-agent #orchestration #parallel #distributed #swarm

Ready to run

npx skills add https://github.com/MichelMokbel/RMS-1 --skill swarm-advanced-michelmokbel

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/MichelMokbel/RMS-1/tree/main/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/MichelMokbel/RMS-1 --skill swarm-advanced-michelmokbel

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of managing complex, multi-step distributed workflows by providing a structured framework for agent coordination, task parallelization, and fault-tolerant execution.

Core Features & Use Cases

Swarm Topologies: Deploy specialized agents in Mesh, Hierarchical, Star, or Ring configurations to match specific project needs.
Workflow Automation: Automate end-to-end processes like full-stack development, deep research, and comprehensive security auditing.
Adaptive Learning: Utilize neural pattern recognition to optimize coordination and improve performance over time.

Quick Start

Initialize a research swarm with six agents in a mesh topology to begin parallel information gathering and analysis.

Frequently Asked Questions about swarm-advanced

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

FAQPage Schema

How do I orchestrate distributed multi-agent AI swarms for parallel task execution?▼

You can orchestrate distributed multi-agent AI swarms by deploying specialized agents in Mesh, Hierarchical, Star, or Ring configurations to execute complex research, development, and testing workflows in parallel. This approach provides structured agent coordination and automated fault-tolerant execution.

What is the best way to automate full-stack development workflows using multiple AI agents?▼

Automating full-stack development workflows is best handled by deploying a structured multi-agent swarm topology. This framework enables coordinated parallel task execution, hierarchical command structures, and automated fault recovery for scalable end-to-end development processes.

Can I use hierarchical command structures to manage complex distributed AI workflows?▼

Yes, you can use hierarchical command structures to manage complex distributed AI workflows. The framework supports Hierarchical topologies, allowing you to coordinate specialized agents, manage persistent memory, and apply neural-pattern-based performance optimization across the swarm.

Does multi-agent orchestration support automated fault recovery for parallel tasks?▼

Multi-agent orchestration supports automated fault recovery for parallel tasks. The framework provides fault-tolerant execution to ensure distributed workflows continue running reliably, applying adaptive learning through neural pattern recognition to optimize coordination over time.

How do I initialize a research swarm for parallel information gathering and analysis?▼

To initialize a research swarm for parallel information gathering, you can deploy six agents in a Mesh topology configuration. This setup enables distributed multi-agent coordination, allowing the swarm to conduct deep research and complex analysis concurrently.