multi-agent-patterns

Name: multi-agent-patterns
Availability: InStock
Author: salmanparacha

Coordinate multi-agent tasks with supervisor, swarm, and hierarchical patterns.

Updated Jan 6, 2026

Search Tags:#multi-agent #consensus #coordination #swarm-pattern #hierarchical-pattern #supervisor-pattern #distributed-ai

Ready to run

npx skills add https://github.com/salmanparacha/speckitplus-calculator --skill multi-agent-patterns-salmanparacha

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-agent-patterns
Source: https://github.com/salmanparacha/speckitplus-calculator/tree/main/.claude/skills-nocontext/multi-agent-patterns
Command: npx skills add https://github.com/salmanparacha/speckitplus-calculator --skill multi-agent-patterns-salmanparacha

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Coordinated multi-agent patterns tackle tasks that exceed a single language model's context by distributing work across specialized agents, enabling longer reasoning, parallel exploration, and domain-focused tooling.

Core Features & Use Cases

Central supervisor for orchestration across workers
Peer-to-peer swarm coordination for exploration and handoffs
Hierarchical delegation for large, multi-step projects
Explicit context isolation and robust failure handling to mitigate drift and errors Common use cases include complex research, cross-domain automation, and enterprise workflows that benefit from specialized agents working in concert.

Quick Start

Provide a task and specify the coordination pattern to initialize a multi-agent workflow.

Frequently Asked Questions about multi-agent-patterns

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

FAQPage Schema

What are multi-agent coordination patterns for distributed AI?▼

Multi-agent coordination patterns distribute work across specialized agents using supervisor, swarm, or hierarchical structures to overcome single language model context limits and enable parallel exploration.

How do I coordinate multi-agent tasks to overcome context limits?▼

You coordinate multi-agent tasks by specifying a coordination pattern like a central supervisor for orchestration or peer-to-peer swarm for exploration, which isolates context and handles failures across specialized agents working in concert.

When should I use hierarchical delegation versus a supervisor pattern?▼

Use hierarchical delegation for large, multi-step projects requiring nested task breakdown, while the supervisor pattern suits centralized orchestration across workers, and swarm coordination enables peer-to-peer exploration with explicit handoffs.

Can I use multi-agent workflows for cross-domain automation?▼

Yes, multi-agent workflows apply to cross-domain automation by assigning specialized toolsets to different agents, enabling robust failure handling and consensus mechanisms to satisfy complex enterprise workflow requirements.

How does context isolation work in distributed AI agent systems?▼

Context isolation works by separating the operational state and reasoning boundaries of specialized agents, mitigating drift and errors while allowing parallel exploration across domains without exceeding a single model's context window.