ai-team-orchestration
Lets your agent organize and manage an AI team for development. It's useful for starting projects, planning tasks, and ensuring everyone works together coordinately.
What it does
This tool helps organize an artificial intelligence team to work on a project. It defines clear roles for each AI agent (producer, developer, tester) and manages the workflow from planning to delivery.
When to use it
- ✓When starting a new software project.
- ✓When planning and coordinating development tasks.
- ✓When needing different perspectives to solve a problem.
Example of what to ask your AI
“Organize an AI team to create a new to-do list app, starting with planning.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses specialized AI agents: one for planning and coordinating, another for coding and testing, and a third for quality assurance. They follow a defined workflow to deliver the work.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill ai-team-orchestrationOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/ai-team-orchestration/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
AI Team Orchestration
Use three stable agents:
| Agent | Purpose |
|---|---|
@ai-team-producer |
Clarify scope, plan proportionately, coordinate, and merge |
@ai-team-dev |
Implement, test, self-review, and prepare the pull request |
@ai-team-qa |
Independently test behavior when dedicated QA is useful |
Nova, Sage, and Milo are perspectives inside the Dev agent, not mandatory project layers.
Default Workflow
Plan -> Implement -> Test -> optional review or QA -> Merge -> update project state
Keep the workflow proportional:
- Skip formal planning for small, obvious changes.
- Use a short plan for multi-step or cross-cutting work.
- Add independent review or QA when risk, uncertainty, or repository policy justifies it.
- Let branch protection, required checks, permissions, and merge queues enforce repository merge policy.
Start or Adopt a Project
- Read existing repository instructions and documentation.
- Discover the actual stack, architecture, commands, deployment model, and risks.
- Create or update
PROJECT_BRIEF.mdonly when durable cross-session context is useful. Start from the project brief template and omit irrelevant sections. - For substantial work, create a concise plan from the sprint plan template.
- Use a separate branch or clone when parallel sessions could conflict, following the repository's own Git policy.
Execute
Producer
- Define the outcome, constraints, acceptance criteria, and explicit exclusions.
- Choose review and QA based on risk rather than ceremony.
- Keep durable project state concise and current.
Dev
- Follow repository conventions and implement the smallest complete solution.
- Run relevant checks and inspect the final diff.
- Open or update the pull request with summary, verification, and limitations.
QA
- Use only when dedicated behavioral verification adds value.
- Test the requested change and important regressions.
- Report reproducible findings and verify fixes.
Brainstorms
Use the brainstorm format for product or architecture decisions that benefit from competing perspectives. For ordinary implementation choices, let Dev decide using repository conventions.
Context Recovery
Before ending a long or interrupted session:
- Update the active plan or progress note if one exists.
- Record material decisions, blockers, and the next action in repository context.
- Use a cold-start prompt such as:
Read the repository instructions, then read whichever sources exist for this
work: the active issue or request, PROJECT_BRIEF.md, and the active plan or
progress note.
Continue from the recorded next action.
Tool and Model Inheritance
The bundled agents intentionally omit tools and model frontmatter:
- available built-in, MCP, and extension tools remain usable;
- developers keep control of model selection;
- role boundaries are defined by instructions and normal trust, permission, authentication, and approval controls.
If the environment exposes too many tools, deselect irrelevant tools or MCP servers, or use VS Code virtual-tool management. Do not add a machine-specific plugin allowlist.
Principles
- Prefer working software and clear handoffs over process artifacts.
- Follow repository policy instead of embedding universal Git commands.
- Preserve unknown work and ask before destructive or privileged actions.
- Keep bugs and important decisions in durable project systems, not only chat.
- See anti-patterns for concise lessons.
FAQ
What are the `@ai-team-producer`, `@ai-team-dev`, and `@ai-team-qa` agents?
These are the AI team members. The producer defines the scope and coordinates, the developer implements and tests, and the tester verifies the behavior.
What is `PROJECT_BRIEF.md`?
It's a document that summarizes the project, useful for maintaining context across different work sessions.
How does the tool handle small projects?
For simple tasks, it can skip formal planning steps, focusing on direct execution.
Details
Original description by the author
Bootstrap and run a lightweight multi-agent development team. Use when starting or adopting a project, planning work, coordinating implementation and optional QA, brainstorming with distinct perspectives, or preserving context across sessions.
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #4992
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
acquire-codebase-knowledge
Helps your agent understand and document an existing code project. It's useful for onboarding new people or getting an overview of the architecture.
github/awesome-copilot
acreadiness-assess
Enables your agent to assess a project's readiness for AI and generate a report. It's useful for understanding how well a project is prepared to use AI tools.
github/awesome-copilot
acreadiness-generate-instructions
Helps your agent create instructions for AI tools based on an assessment. It's useful for improving how AI interacts with code and closing identified gaps.
github/awesome-copilot
acreadiness-policy
Enables your agent to help define or apply policies for AI project assessment. It's useful for standardizing how projects are evaluated and ensuring compliance.
github/awesome-copilot
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC