agent-team-coordination

Coordinate multi-role AI workflows via shared blackboard and file-based handoffs.

5|1|Updated Feb 7, 2026

Search Tags:#multi-agent #handoff #workflows #project-management #task-board #team-coordination #shared-blackboard

Ready to run

npx skills add https://github.com/boparaiamrit/skills-by-amrit --skill agent-team-coordination

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-team-coordination
Source: https://github.com/boparaiamrit/skills-by-amrit/tree/main/skills/agent-team-coordination
Command: npx skills add https://github.com/boparaiamrit/skills-by-amrit --skill agent-team-coordination

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrate coordinated multi-role AI teamwork inside single-agent environments by simulating team dynamics with sequential role-switching, shared blackboard, and handoffs.

Core Features & Use Cases

File-based team protocol with shared blackboard, boards, and phase-driven handoffs to preserve context across roles.
Pre-built role templates (researcher, architect, planner, executor, reviewer) that guide specialist attachments in a single agent.
Works across Antigravity, Cursor, Claude Code setups and other single-agent environments requiring coordinated task execution.

Quick Start

Start a team session for coordinating a multi-role feature using a shared blackboard and handoffs.

Frequently Asked Questions about agent-team-coordination

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

FAQPage Schema

How do I coordinate multi-role AI teams in a single agent environment like Cursor or Claude Code?▼

Multi-role AI team coordination in a single agent environment is achieved by orchestrating sequential role-switching through a shared blackboard and file-based handoffs. This simulates team dynamics using pre-built role templates like researcher, architect, and executor.

What is a shared blackboard approach for single-agent workflows?▼

A shared blackboard approach for single-agent workflows uses a central file-based protocol to preserve context across different AI roles. It enables phase-driven handoffs so that each specialist role can access and build upon previous task outputs.

How do I set up file-based handoffs for AI project management tasks?▼

File-based handoffs for AI project management tasks require explicit role templates, persistent memory integration, and handoff artifacts. This setup enforces sequence, traceability, and recoverability when switching between roles like planner and executor.

Does this multi-agent handoff protocol work with Antigravity and other single-agent setups?▼

Yes, this multi-agent handoff protocol works with Antigravity, Cursor, and Claude Code setups. It is designed specifically for single-agent environments that require structured task execution and coordinated multi-role workflows.

What do I need to enforce sequence and traceability when switching AI roles?▼

To enforce sequence and traceability when switching AI roles, you need persistent memory integration and file-based handoff artifacts. These components ensure that context is preserved and recoverable across phase-driven task transitions.

What's the best way to simulate team dynamics in a single LLM agent?▼

The best way to simulate team dynamics in a single LLM agent is by using sequential role-switching guided by pre-built role templates. A central blackboard and phase-driven handoffs manage the shared context and workflow progression.