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Team Composition Patterns

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Optimize agent team setups for various tasks.

by wshobson38.7k stars on wshobson/agents
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Updated Jul 18, 2026
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Free · Opens the source repo

What Team Composition Patterns does

The Team Composition Patterns skill provides a structured approach to designing effective multi-agent teams tailored to specific tasks. It offers guidelines for selecting the optimal number of agents, choosing the right agent types, and configuring their display modes to enhance productivity. This skill is particularly useful when working with Claude Code's Agent Teams feature, allowing users to streamline their workflows by leveraging best practices in team composition.

When deciding how many agents to deploy for a given task, the skill provides heuristics based on task complexity. For instance, simple tasks may only require 1-2 agents, while more complex tasks might necessitate 3-5 agents. Additionally, the skill includes preset team compositions for various scenarios, such as review teams, debug teams, and feature teams, which help users quickly assemble teams based on their specific needs. Each preset comes with defined roles and responsibilities, ensuring that all necessary dimensions of the task are covered.

Moreover, the skill emphasizes the importance of selecting the appropriate agent types for each role within the team. By understanding the tools available to different agent types, users can ensure that each agent is equipped to handle their assigned tasks effectively. The skill also guides users in configuring display modes, such as tmux or iTerm2, to facilitate better collaboration and monitoring of agent activities during development.

Overall, the Team Composition Patterns skill is designed for developers and designers looking to optimize their use of AI agents in collaborative environments. By following the outlined best practices, users can create efficient, well-coordinated teams that enhance their workflow and productivity.

When to use it

Use this skill when you need to configure agent teams for specific tasks, ensuring the right size and type of agents are selected.

When not to use it

This skill may not be suitable for simple tasks that do not require multiple agents or for workflows that do not involve collaborative efforts.

What you can build with it

Code Review Process

Utilize a preset review team to ensure comprehensive quality assessment across multiple dimensions.

Debugging Complex Issues

Deploy a debug team with multiple investigators to explore various hypotheses for a bug's root cause.

Feature Development

Assemble a feature team to enable parallel workstreams for efficient implementation of new features.

How to install Team Composition Patterns

View source

1. Install with the skills CLI

npx skills add wshobson/agents/team-composition-patterns --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by wshobson

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:

{
  "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.

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