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Agent Teams

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rohitg00
agent-teams

Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill nameagent-teams
Stars
2.9K
Forks
286
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Agent Teams 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/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/agent-teams .claude/skills/agent-teams
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Teams 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 Teams 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 Teams 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 Teams

Coordinate multiple Claude Code sessions working on the same codebase simultaneously.

Enable

bash
export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
claude  # starts as team lead

The first session becomes the team lead. Subsequent sessions in the same repo join as teammates.

Architecture

text
Team Lead (coordinates, delegates, reviews)
  ├── Teammate 1 (owns task A, messages lead + peers)
  ├── Teammate 2 (owns task B, messages lead + peers)
  └── Teammate 3 (owns task C, messages lead + peers)
      └── Shared: task list + mailbox + file locks

Key difference from subagents: Teammates are full Claude Code sessions. They have their own context window, can use all tools, and message each other directly — not just report back to a parent.

Team Sizing

Team SizeBest For
2One builds, one reviews
3-5Parallel features across layers (API, UI, tests)
> 5Coordination overhead outweighs parallelism

3-5 teammates is the productive range. Beyond that, the lead spends more time coordinating than the team saves.

Task granularity: Aim for 5-6 tasks per teammate. Fewer means underutilization; more means excessive context switching.

Display Modes

In-process navigation:

  • Shift+Down — cycle through teammates (wraps around)
  • See each teammate's current task and output

Split-pane (recommended for >2 teammates):

  • tmux: tmux split-window -h per teammate
  • iTerm2: Cmd+D for vertical split
  • Each pane runs its own claude session

Task Management

Tasks flow through states:

text
pending → in-progress → completed
              └── blocked (waiting on dependency)

Task Decomposition

Break work into units that:

  • Touch non-overlapping files
  • Can be verified independently
  • Have clear done criteria

Good decomposition:

text
Task 1: Add rate limiting middleware (src/middleware/rate-limit.ts)
Task 2: Add rate limit tests (tests/rate-limit.test.ts)
Task 3: Update API docs for rate limit headers (docs/api.md)
Task 4: Add Redis config for rate limit store (src/config/redis.ts)

Bad decomposition:

text
Task 1: Implement rate limiting
Task 2: Fix rate limiting bugs
Task 3: Improve rate limiting

Dependencies

Tasks can declare dependencies:

text
Task 3 (API docs) → depends on Task 1 (middleware)
Task 2 (tests) → depends on Task 1 (middleware)
Task 4 (Redis config) → no dependencies

Teammates pick up unblocked tasks automatically.

File-Lock Claiming

Teammates claim files before editing to prevent conflicts:

  1. Teammate checks if file is locked
  2. If free, claims it (file-lock-based)
  3. Edits the file
  4. Releases lock on task completion

If two teammates need the same file, one waits or the lead reassigns.

Plan Approval

Teammates plan before implementing:

  1. Teammate receives task
  2. Writes a brief plan (files to change, approach)
  3. Lead reviews plan
  4. Lead approves or redirects
  5. Teammate implements

This prevents wasted work from misunderstood requirements.

Delegate Mode

Shift+Tab toggles delegate mode for the lead:

  • Lead coordinates only — no direct code edits
  • All implementation delegated to teammates
  • Lead reviews, approves plans, manages task flow

Hook Events

HookFires When
TeammateIdleA teammate finishes its task and has no pending work
TaskCreatedNew task added to the shared list
TaskCompletedA teammate marks a task done

Use these to trigger notifications, auto-assign next tasks, or run integration tests when all tasks complete.

When to Use Teams vs Alternatives

ScenarioUse
Parallel work on non-overlapping filesAgent teams
Quick background explorationSubagent
Isolated feature branch workWorktree (claude -w)
Competing approaches to same problemWorktrees (compare results)
Cross-layer changes (API + UI + tests)Agent teams
One-off delegationSubagent with isolation: worktree
Large-scale migration (20+ files)/batch command

Anti-Patterns

  • More than 5 teammates — coordination overhead dominates
  • Tasks that share mutable state or the same files
  • Skipping plan approval — leads to rework
  • Lead doing implementation instead of coordinating (use delegate mode)
  • Not setting dependencies — teammates race on coupled tasks

Example Session

text
Lead: "We need rate limiting. Breaking into 4 tasks."

  Task 1: middleware implementation → Teammate 1
  Task 2: Redis config → Teammate 2 (no deps)
  Task 3: tests → Teammate 1 (after Task 1)
  Task 4: API docs → Teammate 2 (after Task 1)

Teammate 1 plans → Lead approves → implements Task 1
Teammate 2 plans → Lead approves → implements Task 2
Task 1 completes → unblocks Tasks 3 and 4
Teammate 1 picks up Task 3, Teammate 2 picks up Task 4
All complete → Lead runs integration tests → commits

Docs

https://code.claude.com/docs/agent-teams

Frequently asked questions

What does the Agent Teams AI skill do?

Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.

Why use Agent Teams on TypingMind?

Because you install it once and use it with any model. Agent Teams 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 Teams in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/agent-teams. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Teams?

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 Teams?

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

Is the Agent Teams AI skill free?

It is published on GitHub by rohitg00. Check the repository for licensing terms. 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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