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Agent Team Orchestration

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aAAaqwq
agent-team-orchestration

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.

Overview

PublisheraAAaqwq
RepositoryAGI-Super-Team
Skill nameagent-team-orchestration
Stars
98
Forks
23
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aAAaqwq on GitHub. Read the source before you install it.

Installation

Install the Agent Team Orchestration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git /tmp/AGI-Super-Team
mkdir -p .claude/skills
cp -r /tmp/AGI-Super-Team/skills/agent-team-orchestration .claude/skills/agent-team-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Team Orchestration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Team Orchestration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Team Orchestration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Team Orchestration

Production playbook for running multi-agent teams with clear roles, structured task flow, and quality gates.

Quick Start: Minimal 2-Agent Team

A builder and a reviewer. The simplest useful team.

1. Define Roles

Orchestrator (you) — Route tasks, track state, report results
Builder agent     — Execute work, produce artifacts

2. Spawn a Task

1. Create task record (file, DB, or task board)
2. Spawn builder with:
   - Task ID and description
   - Output path for artifacts
   - Handoff instructions (what to produce, where to put it)
3. On completion: review artifacts, mark done, report

3. Add a Reviewer

Builder produces artifact → Reviewer checks it → Orchestrator ships or returns

That's the core loop. Everything below scales this pattern.

Core Concepts

Roles

Every agent has one primary role. Overlap causes confusion.

RolePurposeModel guidance
OrchestratorRoute work, track state, make priority callsHigh-reasoning model (handles judgment)
BuilderProduce artifacts — code, docs, configsCan use cost-effective models for mechanical work
ReviewerVerify quality, push back on gapsHigh-reasoning model (catches what builders miss)
OpsCron jobs, standups, health checks, dispatchingCheapest model that's reliable

Read references/team-setup.md when defining a new team or adding agents.

Task States

Every task moves through a defined lifecycle:

Inbox → Assigned → In Progress → Review → Done | Failed

Rules:

  • Orchestrator owns state transitions — don't rely on agents to update their own status
  • Every transition gets a comment (who, what, why)
  • Failed is a valid end state — capture why and move on

Read references/task-lifecycle.md when designing task flows or debugging stuck tasks.

Handoffs

When work passes between agents, the handoff message includes:

  1. What was done — summary of changes/output
  2. Where artifacts are — exact file paths
  3. How to verify — test commands or acceptance criteria
  4. Known issues — anything incomplete or risky
  5. What's next — clear next action for the receiving agent

Bad handoff: "Done, check the files." Good handoff: "Built auth module at /shared/artifacts/auth/. Run npm test auth to verify. Known issue: rate limiting not implemented yet. Next: reviewer checks error handling edge cases."

Reviews

Cross-role reviews prevent quality drift:

  • Builders review specs — "Is this feasible? What's missing?"
  • Reviewers check builds — "Does this match the spec? Edge cases?"
  • Orchestrator reviews priorities — "Is this the right work right now?"

Skip the review step and quality degrades within 3-5 tasks. Every time.

Read references/communication.md when setting up agent communication channels.Read references/patterns.md for proven multi-step workflows.

Reference Files

FileRead when...
team-setup.mdDefining agents, roles, models, workspaces
task-lifecycle.mdDesigning task states, transitions, comments
communication.mdSetting up async/sync communication, artifact paths
patterns.mdImplementing specific workflows (spec→build→test, parallel research, escalation)

Common Pitfalls

Spawning without clear artifact output paths

Agent produces great work, but you can't find it. Always specify the exact output path in the spawn prompt. Use a shared artifacts directory with predictable structure.

No review step = quality drift

"It's a small change, skip review." Do this three times and you have compounding errors. Every artifact gets at least one set of eyes that didn't produce it.

Agents not commenting on task progress

Silent agents create coordination blind spots. Require comments at: start, blocker, handoff, completion. If an agent goes silent, assume it's stuck.

Not verifying agent capabilities before assigning

Assigning browser-based testing to an agent without browser access. Assigning image work to a text-only model. Check capabilities before routing.

Orchestrator doing execution work

The orchestrator routes and tracks — it doesn't build. The moment you start "just quickly doing this one thing," you've lost oversight of the rest of the team.

When NOT to Use This Skill

  • Single-agent setups — Just follow standard AGENTS.md conventions. Team orchestration adds overhead that solo agents don't need.
  • One-off task delegation — Use sessions_spawn directly. This skill is for sustained workflows with multiple handoffs.
  • Simple question routing — If you're just forwarding a question to a specialist, that's a message, not a workflow.

This skill is for sustained team workflows — recurring collaboration patterns where agents depend on each other's output over multiple tasks.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Team Orchestration AI skill do?

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.

Why use Agent Team Orchestration on TypingMind?

Because you install it once and use it with any model. Agent Team Orchestration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Team Orchestration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/agent-team-orchestration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Team Orchestration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Team Orchestration?

As many as you like. As long as a model supports skills, you can use Agent Team Orchestration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Team Orchestration AI skill free?

Yes. It is published on GitHub by aAAaqwq under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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