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cc-ref-multi-agent

Document multi-agent orchestration patterns, token economics, and prompt engineering principles.

Updated Mar 21, 2026
Search Tags:#reference#multi-agent#orchestration#system-architecture#prompt-engineering#agent-design
One-click install
npx skills add https://github.com/UsernameTron/Claude-Code-Kickstart --skill cc-ref-multi-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cc-ref-multi-agent
Source: https://github.com/UsernameTron/Claude-Code-Kickstart/tree/main/plugins/claude-code-factory/skills/cc-ref-multi-agent
Command: npx skills add https://github.com/UsernameTron/Claude-Code-Kickstart --skill cc-ref-multi-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides authoritative guidance on multi-agent system design and orchestration for robust, scalable AI workflows.

Core Features & Use Cases

  • Authoritative orchestration guidance including context-centric decomposition, the three-part gate, and evaluator-optimizer patterns for coordinating agents.
  • Practical design reference covering orchestrator-workers, routing, and parallelization to improve reliability and efficiency.
  • Use cases include architecture reviews, educational material for engineers, and policy drafting for agent-based systems.

Quick Start

Consult this reference when you need high-level guidance on multi-agent orchestration patterns and subagent design.

Frequently Asked Questions about cc-ref-multi-agent

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

FAQPage Schema
What is multi-agent orchestration and when do I need it for AI workflows?▼

Multi-agent orchestration coordinates multiple AI agents to handle complex workflows. You need it for robust, scalable architecture when decomposing context and routing tasks across specialized agents.

How do I design a multi-agent system architecture for complex AI workflows?▼

Design multi-agent systems using context-centric decomposition, orchestrator-workers patterns, and routing. Apply the three-part gate and evaluator-optimizer patterns to coordinate agents and improve reliability.

What are the best prompt engineering principles for multi-agent systems?▼

The best multi-agent prompt engineering relies on eight authoritative principles. This reference details them alongside token economics to optimize context decomposition and subagent design.

Can I use orchestrator-workers and parallelization patterns for agent-based systems?▼

Yes, orchestrator-workers, parallelization, and routing patterns are core to multi-agent orchestration. They improve efficiency and reliability when coordinating agents for complex AI tasks.

Does this multi-agent reference cover token economics and context decomposition?▼

Yes, this multi-agent reference covers token economics and context-centric decomposition. It provides authoritative guidance on managing token limits during agent orchestration.

When should I not use a multi-agent orchestration architecture?▼

Multi-agent orchestration may not suit simple AI workflows. If tasks lack complexity requiring context decomposition, evaluator-optimizer patterns, or parallelization, a single agent is more efficient.

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