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agent-collaboration

Standardize AI agent collaboration with worktree awareness and decision recording.

3|Updated Mar 15, 2026
Search Tags:#workflow#decision making#collaboration#communication#agent#teamwork
One-click install
npx skills add https://github.com/bradygaster/docx-mcp-server --skill agent-collaboration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-collaboration
Source: https://github.com/bradygaster/docx-mcp-server/tree/main/.squad/templates/skills/agent-collaboration
Command: npx skills add https://github.com/bradygaster/docx-mcp-server --skill agent-collaboration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill centralizes and standardizes essential collaboration patterns for AI agents within a team, preventing redundant context and ensuring consistent operational behavior across all team members.

Core Features & Use Cases

  • Worktree Awareness: Ensures agents correctly identify and use the project's root directory, regardless of their current working directory.
  • Decision Recording: Provides a standardized format and process for agents to record decisions that impact the team.
  • Cross-Agent Communication: Guides agents on how to request help or input from other team members.
  • Reviewer Protocol: Defines clear steps for handling rejections and reassignments of work.
  • Use Case: When an agent needs to make a decision that affects another agent's task, it follows the defined pattern to record that decision in a shared inbox, ensuring transparency and coordination.

Quick Start

Follow the documented patterns for worktree awareness, decision recording, and cross-agent communication.

Frequently Asked Questions about agent-collaboration

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

FAQPage Schema
How do I standardize AI agent collaboration patterns in a multi-agent system?▼

Standardize AI agent collaboration by enforcing worktree awareness, decision recording via a shared inbox system, and inter-agent communication protocols. This centralizes teamwork patterns to prevent redundant context and ensure consistent operational behavior across all team members.

How do AI agents record decisions that impact other team members' tasks?▼

Agents record decisions using a standardized format and process, writing entries into a shared inbox system. This decision recording mechanism ensures transparency and coordination when an agent makes a choice that affects another agent's workflow.

Why does my AI agent fail to find the project root directory during teamwork workflows?▼

AI agents fail to find the project root due to lack of worktree awareness. This skill ensures agents correctly identify and use the project's root directory regardless of their current working directory, preventing path resolution errors during collaboration.

What's the best way to handle work rejections and reassignments between AI agents?▼

Handle work rejections and reassignments using a defined reviewer protocol. This protocol provides clear steps for cross-agent communication when rejecting or reassigning work, ensuring coordinated task execution within the multi-agent system.

Can I use this agent collaboration framework without predefined team workflows?▼

No, this framework enforces adherence to team-defined workflows and reviewer protocols. Predefined workflow rules are required to ensure efficient and coordinated task execution within the multi-agent system.

How does cross-agent communication work when an AI agent needs help from another teammate?▼

Cross-agent communication works through guided protocols that direct agents on how to request help or input from other team members. This ensures structured inter-agent communication rather than ad-hoc requests.

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