swarm

Orchestrate hierarchical AI agent fleets for distributed task execution.

2|2|Updated Mar 6, 2026

Search Tags:#parallel processing #task automation #agent #orchestration #distributed computing #fleet management

Ready to run

npx skills add https://github.com/DUBSOpenHub/copilot-skills --skill swarm-dubsopenhub

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swarm
Source: https://github.com/DUBSOpenHub/copilot-skills/tree/main/swarm
Command: npx skills add https://github.com/DUBSOpenHub/copilot-skills --skill swarm-dubsopenhub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires node, sqlite3, python3, curl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the execution of large-scale, parallelizable tasks by orchestrating a hierarchical fleet of AI agents, significantly accelerating complex projects.

Core Features & Use Cases

Hierarchical Orchestration: Manages a Commander (Tier 1), Squad Leaders (Tier 2), and Worker Bees (Tier 3) for scalable task distribution.
Dynamic Task Generation: Can generate tasks from a goal or use a pre-defined task file.
Resource Management: Monitors system resources and adjusts concurrency for optimal performance.
Use Case: Process thousands of code files for a security audit by distributing the analysis across hundreds of worker agents, reporting back aggregated findings.

Quick Start

Run the swarm skill to process 50 tasks for the goal of analyzing code quality.

Frequently Asked Questions about swarm

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

FAQPage Schema

How do I orchestrate a hierarchical agent fleet for distributed task execution?▼

To orchestrate a hierarchical agent fleet, this Skill coordinates a Commander, Squad Leaders, and Worker Bees across multiple tiers, managing up to 750+ agents for scalable task distribution and parallel processing.

Can I generate parallel processing tasks dynamically from a goal or JSONL file?▼

Yes, you can generate parallel processing tasks dynamically from a high-level goal or use a pre-defined JSONL file to feed tasks directly into the hierarchical agent orchestration system for execution.

What do I need to run an agent fleet for distributed computing and task automation?▼

To run agent fleet task automation, you need an environment with node, sqlite3, python3, and curl installed to support the shell scripts and Node.js progress monitoring infrastructure.

How does distributed computing handle API rate limiting and worker crashes?▼

Distributed computing orchestration handles API rate limiting, worker crashes, and memory pressure through automatic backoff and adaptive concurrency adjustments to maintain optimal fleet performance.

What is the best way to monitor progress across hundreds of agents in a fleet?▼

The best way to monitor progress across hundreds of agents is using the provided shell scripts and Node.js modules, which track task completion and system resources during distributed execution.

When should I use a hierarchical agent fleet for parallel processing?▼

Use a hierarchical agent fleet for parallel processing when automating large-scale tasks like code security audits, where distributing analysis across hundreds of worker agents significantly accelerates complex projects.