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多 Agent 协作协议

Coordinate multi-agent workflows using Spec-First task definitions and DAG dependency management.

Updated Aug 23, 2026
Search Tags:#workflow#collaboration#task management#protocol#multi-agent#agent development
One-click install
npx skills add https://github.com/tanaer/lobster-university-v2 --skill agent-tanaer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: 多 Agent 协作协议
Source: https://github.com/tanaer/lobster-university-v2/tree/main/skills/courses/multi-agent-protocol
Command: npx skills add https://github.com/tanaer/lobster-university-v2 --skill agent-tanaer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complexity and potential chaos in multi-agent systems by establishing clear protocols for task definition, execution, and quality control, ensuring efficient and reliable collaboration.

Core Features & Use Cases

  • Spec-First Task Definition: Define tasks using a structured YAML format before execution.
  • Two-Phase Review: Implement a rigorous review process for both task specifications and execution results.
  • Dependency Management: Utilize a directed acyclic graph (DAG) to manage task dependencies.
  • Use Case: Coordinate multiple AI agents for complex software development, data analysis pipelines, or content creation workflows, ensuring each agent understands its role and the overall project flow.

Quick Start

Use the multi-agent-protocol skill to define a task for implementing user authentication with 'requirements.txt' as input and 'auth_module' as output.

Frequently Asked Questions about 多 Agent 协作协议

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

FAQPage Schema
How do I manage task dependencies in a multi-agent workflow?▼

You can manage multi-agent task dependencies by utilizing a directed acyclic graph (DAG) to map and execute complex workflows, ensuring agents process tasks in the correct order.

What is a spec-first approach for multi-agent collaboration?▼

A spec-first approach for multi-agent collaboration requires defining tasks using a structured YAML format before execution, ensuring clear task definitions and quality control across agents.

How do I implement quality control for AI agent task execution?▼

You can implement quality control for AI agent task execution by applying a two-phase review process that rigorously evaluates both task specifications and execution results.

Can I use this multi-agent protocol for software development pipelines?▼

Yes, you can use this multi-agent protocol to coordinate complex software development pipelines, data analysis workflows, and content generation by defining clear agent roles and project flows.

How do I define a task for an AI agent using a YAML format?▼

You define a task for an AI agent using a structured YAML format by specifying inputs and outputs, such as defining an authentication module task with 'requirements.txt' as input and 'auth_module' as output.

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