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swarm

Decompose tasks into parallel work units and orchestrate agent teams.

2|3|Updated Oct 20, 2025
Search Tags:#parallel processing#workflow automation#agent orchestration#task decomposition#multi-agent systems#teamwork
One-click install
npx skills add https://github.com/psd401/psd-claude-plugins --skill swarm-psd401
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm
Source: https://github.com/psd401/psd-claude-plugins/tree/main/plugins/psd-coding-system/skills/swarm
Command: npx skills add https://github.com/psd401/psd-claude-plugins --skill swarm-psd401

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines complex, multi-agent workflows by enabling parallel execution and coordination of specialized AI agents, significantly accelerating task completion.

Core Features & Use Cases

  • Parallel Task Execution: Decomposes large tasks into smaller, independent units that can be processed concurrently by different agents.
  • Dependency Management: Intelligently handles task dependencies, ensuring that sequential steps are executed in the correct order.
  • Agent Team Coordination: Leverages Claude Code's experimental Agent Teams feature for advanced orchestration or falls back to parallel Task dispatch for stability.
  • Use Case: For a full-stack feature development, you can dispatch frontend and backend specialists in parallel, followed by a validator agent, all managed by the swarm skill.

Quick Start

Use the swarm skill to decompose and parallelize the task of researching and implementing a new feature.

Frequently Asked Questions about swarm

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

FAQPage Schema
How do I orchestrate parallel multi-agent workflows for complex task decomposition?▼

Parallel multi-agent workflows are orchestrated by decomposing user tasks into independent work units, identifying agent type, dependencies, inputs, and outputs for each unit, and dispatching them for concurrent execution. The system then synthesizes results and resolves conflicts.

What is the best way to manage dependencies when dispatching parallel AI agents?▼

Managing dependencies in parallel AI agents involves intelligently identifying sequential steps within the decomposed work units. The orchestration pattern ensures that tasks requiring specific inputs from previous steps are executed only after their dependencies are resolved.

Can I use Claude Code's experimental Agent Teams feature for parallel task execution?▼

Yes, Claude Code's experimental Agent Teams feature can be used for advanced parallel task execution. The orchestration pattern leverages this feature for advanced coordination, or falls back to parallel Task dispatch for stability.

How does multi-agent task decomposition handle conflict resolution and identify gaps?▼

Multi-agent task decomposition handles conflict resolution by synthesizing the results from all dispatched agents. After synthesis, the orchestration pattern resolves any conflicts between outputs and identifies remaining gaps in the completed work.

When do I need agent orchestration for full-stack feature development?▼

Agent orchestration is needed for full-stack feature development when you want to dispatch frontend and backend specialists in parallel. This multi-agent coordination significantly accelerates task completion by processing independent work units concurrently.

Does parallel agent execution work without external dependencies?▼

Yes, parallel agent execution works without external dependencies. The orchestration pattern operates using either the built-in Task tool for parallel dispatch or Claude Code's experimental Agent Teams feature, requiring no additional setup.

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