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Team Composition Patterns

CommunityPopular
wshobson
team-composition-patterns

Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection. Use this skill when deciding how many agents to spawn for a task, when choosing between a review team versus a feature team versus a debug team, when selecting the correct subagent_type for each role to ensure agents have the tools they need, when configuring display modes (tmux, iTerm2, in-process) for a CI or local environment, or when building a custom team composition for a non-standard workflow such as a migration or security audit.

Overview

Publisherwshobson
Repositoryagents
Skill nameteam-composition-patterns
Stars
39.8K
Forks
4.2K
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Team Composition Patterns 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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/agent-teams/skills/team-composition-patterns .claude/skills/team-composition-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Team Composition Patterns 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 Composition Patterns 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 Composition Patterns 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 Composition Patterns

Best practices for composing multi-agent teams, selecting team sizes, choosing agent types, and configuring display modes for Claude Code's Agent Teams feature.

When to Use This Skill

  • Deciding how many teammates to spawn for a task
  • Choosing between preset team configurations
  • Selecting the right agent type (subagent_type) for each role
  • Configuring teammate display modes (tmux, iTerm2, in-process)
  • Building custom team compositions for non-standard workflows

Team Sizing Heuristics

ComplexityTeam SizeWhen to Use
Simple1-2Single-dimension review, isolated bug, small feature
Moderate2-3Multi-file changes, 2-3 concerns, medium features
Complex3-4Cross-cutting concerns, large features, deep debugging
Very Complex4-5Full-stack features, comprehensive reviews, systemic issues

Rule of thumb: Start with the smallest team that covers all required dimensions. Adding teammates increases coordination overhead.

Preset Team Compositions

Review Team

  • Size: 3 reviewers
  • Agents: 3x team-reviewer
  • Default dimensions: security, performance, architecture
  • Use when: Code changes need multi-dimensional quality assessment

Debug Team

  • Size: 3 investigators
  • Agents: 3x team-debugger
  • Default hypotheses: 3 competing hypotheses
  • Use when: Bug has multiple plausible root causes

Feature Team

  • Size: 3 (1 lead + 2 implementers)
  • Agents: 1x team-lead + 2x team-implementer
  • Use when: Feature can be decomposed into parallel work streams

Fullstack Team

  • Size: 4 (1 lead + 3 implementers)
  • Agents: 1x team-lead + 1x frontend team-implementer + 1x backend team-implementer + 1x test team-implementer
  • Use when: Feature spans frontend, backend, and test layers

Research Team

  • Size: 3 researchers
  • Agents: 3x general-purpose
  • Default areas: Each assigned a different research question, module, or topic
  • Capabilities: Codebase search (Grep, Glob, Read), web search (WebSearch, WebFetch)
  • Use when: Need to understand a codebase, research libraries, compare approaches, or gather information from code and web sources in parallel

Security Team

  • Size: 4 reviewers
  • Agents: 4x team-reviewer
  • Default dimensions: OWASP/vulnerabilities, auth/access control, dependencies/supply chain, secrets/configuration
  • Use when: Comprehensive security audit covering multiple attack surfaces

Migration Team

  • Size: 4 (1 lead + 2 implementers + 1 reviewer)
  • Agents: 1x team-lead + 2x team-implementer + 1x team-reviewer
  • Use when: Large codebase migration (framework upgrade, language port, API version bump) requiring parallel work with correctness verification

Agent Type Selection

When spawning teammates with the Agent tool, choose subagent_type based on what tools the teammate needs:

Agent TypeTools AvailableUse For
general-purposeAll tools (Read, Write, Edit, Bash, etc.)Implementation, debugging, any task requiring file changes
ExploreRead-only tools (Read, Grep, Glob)Research, code exploration, analysis
PlanRead-only toolsArchitecture planning, task decomposition
agent-teams:team-reviewerRead/search/Bash plus TaskList/TaskGet/TaskUpdate/SendMessageCode review with structured findings
agent-teams:team-debuggerRead/search/Bash plus TaskList/TaskGet/TaskUpdate/SendMessageHypothesis-driven investigation
agent-teams:team-implementerRead/Write/Edit/search/Bash plus TaskList/TaskGet/TaskUpdate/SendMessageBuilding features within file ownership boundaries
agent-teams:team-leadRead/search/Bash plus Agent Teams coordination toolsTeam orchestration and coordination

Key distinction: Read-only agents (Explore, Plan) cannot modify files. Never assign implementation tasks to read-only agents.

Display Mode Configuration

Configure in ~/.claude/settings.json:

json
{
  "teammateMode": "tmux"
}
ModeBehaviorBest For
"tmux"Each teammate in a tmux paneDevelopment workflows, monitoring multiple agents
"iterm2"Each teammate in an iTerm2 tabmacOS users who prefer iTerm2
"in-process"All teammates in same processSimple tasks, CI/CD environments

Custom Team Guidelines

When building custom teams:

  1. Every team needs a coordinator — Either designate a team-lead or have the user coordinate directly
  2. Match roles to agent types — Use specialized agents (reviewer, debugger, implementer) when available
  3. Avoid duplicate roles — Two agents doing the same thing wastes resources
  4. Define boundaries upfront — Each teammate needs clear ownership of files or responsibilities
  5. Keep it small — 2-4 teammates is the sweet spot; 5+ requires significant coordination overhead

Troubleshooting

A teammate was spawned as Explore but needs to write files. Explore and Plan are read-only agents. Change the subagent_type to general-purpose or an appropriate specialized agent type. Never assign implementation tasks to read-only agents.

The team is growing too large and coordination is slowing everything down. Each additional teammate adds communication overhead. Consolidate roles: can one agent cover two dimensions? A 4-person team doing 6 independent tasks is usually better served by 3 agents covering 2 tasks each.

tmux mode is not showing panes. Ensure tmux is installed and a session is already running before spawning teammates. The in-process mode works without tmux and is suitable for CI or scripted environments.

Two reviewers are flagging the same issues. The review dimensions overlap. Redefine each reviewer's focus area: one on correctness/logic, one on security, one on performance/scalability. Overlapping coverage wastes tokens and produces duplicate findings.

A team-lead is spawning teammates but they are not receiving tasks. Verify that the lead is using the Agent tool to spawn teammates and passing complete context in the prompt. Teammates start fresh with no prior conversation history — they need all relevant information in their initial prompt.

Related Skills

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 Composition Patterns AI skill do?

Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection. Use this skill when deciding how many agents to spawn for a task, when choosing between a review team versus a feature team versus a debug team, when selecting the correct subagent_type for each role to ensure agents have the tools they need, when configuring display modes (tmux, iTerm2, in-process) for a CI or local environment, or when building a custom team composition for a non-standard workflow such as a migration or security audit.

Why use Team Composition Patterns on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/agent-teams/skills/team-composition-patterns. 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 Composition Patterns?

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 Composition Patterns?

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

Is the Team Composition Patterns AI skill free?

Yes. It is published on GitHub by wshobson 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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