team-composition-patterns▌
wshobson/agents · updated Apr 8, 2026
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Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection.
- ›Seven preset team configurations (Review, Debug, Feature, Fullstack, Research, Security, Migration) with recommended agent counts and types for common workflows
- ›Team sizing heuristic table matching task complexity (simple to very complex) with recommended team size (1–5 agents) and coordination overhead guidance
- ›Agent type selection guide covering general-purpose, read-only
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
| Complexity | Team Size | When to Use |
|---|---|---|
| Simple | 1-2 | Single-dimension review, isolated bug, small feature |
| Moderate | 2-3 | Multi-file changes, 2-3 concerns, medium features |
| Complex | 3-4 | Cross-cutting concerns, large features, deep debugging |
| Very Complex | 4-5 | Full-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+ 2xteam-implementer - Use when: Feature can be decomposed into parallel work streams
Fullstack Team
- Size: 4 (1 lead + 3 implementers)
- Agents: 1x
team-lead+ 1x frontendteam-implementer+ 1x backendteam-implementer+ 1x testteam-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+ 2xteam-implementer+ 1xteam-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 Task tool, choose subagent_type based on what tools the teammate needs:
| Agent Type | Tools Available | Use For |
|---|---|---|
general-purpose |
All tools (Read, Write, Edit, Bash, etc.) | Implementation, debugging, any task requiring file changes |
Explore |
Read-only tools (Read, Grep, Glob) | Research, code exploration, analysis |
Plan |
Read-only tools | Architecture planning, task decomposition |
agent-teams:team-reviewer |
All tools | Code review with structured findings |
agent-teams:team-debugger |
All tools | Hypothesis-driven investigation |
agent-teams:team-implementer |
All tools | Building features within file ownership boundaries |
agent-teams:team-lead |
All tools | Team 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"
}
| Mode | Behavior | Best For |
|---|---|---|
"tmux" |
Each teammate in a tmux pane | Development workflows, monitoring multiple agents |
"iterm2" |
Each teammate in an iTerm2 tab | macOS users who prefer iTerm2 |
"in-process" |
All teammates in same process | Simple tasks, CI/CD environments |
Custom Team Guidelines
When building custom teams:
- Every team needs a coordinator — Either designate a
team-leador have the user coordinate directly - Match roles to agent types — Use specialized agents (reviewer, debugger, implementer) when available
- Avoid duplicate roles — Two agents doing the same thing wastes resources
- Define boundaries upfront — Each teammate needs clear ownership of files or responsibilities
- 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 Task 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
- parallel-feature-development — Decompose work streams and assign file ownership once the team is composed
- team-communication-protocols — Establish messaging norms and shutdown procedures for the assembled team
How to use team-composition-patterns on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add team-composition-patterns
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches team-composition-patterns from GitHub repository wshobson/agents and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate team-composition-patterns. Access the skill through slash commands (e.g., /team-composition-patterns) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★34 reviews- ★★★★★Pratham Ware· Dec 24, 2024
We added team-composition-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Neel Garcia· Dec 20, 2024
team-composition-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Advait Robinson· Nov 19, 2024
team-composition-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Yash Thakker· Nov 15, 2024
team-composition-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Naina Perez· Nov 11, 2024
team-composition-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Liam Patel· Oct 10, 2024
Keeps context tight: team-composition-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Dhruvi Jain· Oct 6, 2024
Registry listing for team-composition-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Naina Choi· Oct 2, 2024
I recommend team-composition-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Mei Robinson· Sep 13, 2024
team-composition-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Harper Garcia· Sep 5, 2024
We added team-composition-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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