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Team Mode

Community
oil-oil
team-mode

Use when a task may benefit from a bounded subagent for implementation, large-codebase discovery at task start, independent review, requested or pre-commit code simplification, or expert work on complex decisions, modeling, automation, or repeated failures. The main agent chooses whether to delegate and accepts the result. Do not use for simple questions or short work with no meaningful review or delegation.

Overview

Publisheroil-oil
Repositorycodex-team-mode
Skill nameteam-mode
Stars
226
Forks
25
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Team Mode 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.
    https://github.com/oil-oil/codex-team-mode/tree/main/skills/team-mode
  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/oil-oil/codex-team-mode.git /tmp/codex-team-mode
mkdir -p .claude/skills
cp -r /tmp/codex-team-mode/skills/team-mode .claude/skills/team-mode
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Team Mode 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 Team Mode 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 Team Mode 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.

Team Mode

The main agent decomposes the user's task, decides what to delegate, and accepts the result. A task's size alone does not require subagents. On activation, send one brief commentary update in the user's language prefixed with 👾 (in Chinese: 👾 已开启小队模式。).

When to dispatch

Delegate a defined part of the task when a child can make useful progress through implementation, codebase discovery, review, or expert work. The main agent owns how the parts fit together, unresolved product decisions, and final acceptance. There is no target number of agents or required sequence.

Choose the smallest count that covers the independent work. One child is the default for one bounded question or review lens. Add children only when each can work and report independently, with a distinct question, scope, or lens. A large diff alone is not a reason to fan out.

Consider coordination and usage overhead. Respect the host's active-agent limit; if parallel dispatch is unavailable, continue sequentially or inline without silently dropping coverage.

  • Explorer — use for substantial codebase discovery at the start of a task. Handle small lookups and general research directly. Read Explore for this route.
  • Executor — use when the intended result and file ownership are clear enough for independent implementation. Give each target one owner.
  • Reviewer — use when the user asks for review or a completed result has a meaningful risk of unnoticed defects. Review the assigned result without steering toward a suspected answer; save a Markdown report when a lasting record helps.
  • ExpertAdvisor — use for a complex architecture or high-impact decision, a problem unresolved after repeated attempts, or modeling or complex computer automation the main agent cannot handle well. Ask for an independent plan or assign the expert a concrete outcome to produce.

When the user asks to simplify code, or before committing a code change, follow Simplify for scoped cleanup and final review. The main agent decides which findings to apply and accepts the final result.

For each dispatch, name the intended agent_type, count, independent scope, expected return, and write ownership. Keep write scopes separate when agents share a workspace.

Other configured roles and models are allowed when appropriate. Model and effort defaults, plus the optional default.toml sentinel, are described in profile setup; normal dispatch does not require reading it.

Context and handoff

Start a new child with fork_turns="none" by default. Always use it for Reviewer and ExpertAdvisor, whose value depends on an independent view. Use it for Explorer too: a question and codebase path are usually enough.

An Executor may inherit a small number of recent parent turns when the assignment depends on decisions in that conversation that a short brief would likely omit. Use the smallest useful positive fork_turns value; inherit the full conversation only when the whole history is genuinely needed. Reuse an existing child for a direct continuation of its own assignment.

Give the child its assigned question or deliverable, relevant context, and scope. Include the broader user goal when it helps explain the assignment. Pass files, symptoms, and prior attempts as leads, not a fixed diagnosis or checklist. The child chooses its method within the assigned part; the main agent handles changes to the task breakdown and checks the returned work.

Expert help

When stronger expertise would help, use python3 scripts/current_model.py if the parent model is unclear. Choose an available stronger model and supported effort for the model-free ExpertAdvisor profile.

Give it the decision or outcome, relevant context, evidence, constraints, and scope without presenting a proposed cause as fact. The expert may produce a plan or carry out assigned modeling or complex automation. Check its result before accepting it. If no stronger model is available, continue without claiming a stronger-model consultation.

A model fixed in a custom profile takes precedence over a spawn-time choice. See profile setup for model changes; do not assume an explicit model overrides Explorer, Executor, or Reviewer.

Diagnostics

For a requested usage report, run python3 scripts/usage_by_model.py; read evaluation guidance when evaluating Team Mode itself. For an interactive-state task that code inspection cannot establish, read interactive testing guidance.

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 Team Mode AI skill do?

Use when a task may benefit from a bounded subagent for implementation, large-codebase discovery at task start, independent review, requested or pre-commit code simplification, or expert work on complex decisions, modeling, automation, or repeated failures. The main agent chooses whether to delegate and accepts the result. Do not use for simple questions or short work with no meaningful review or delegation.

Why use Team Mode on TypingMind?

Because you install it once and use it with any model. Team Mode 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 Team Mode in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oil-oil/codex-team-mode/tree/main/skills/team-mode. 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 Team Mode?

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 Team Mode?

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

Is the Team Mode AI skill free?

Yes. It is published on GitHub by oil-oil 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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