Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionteam-composition-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches team-composition-patterns from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate team-composition-patterns. Access via /team-composition-patterns in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Best practices for composing multi-agent teams, selecting team sizes, choosing agent types, and configuring display modes for Claude Code's Agent Teams feature.
| 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.
team-reviewerteam-debuggerteam-lead + 2x team-implementerteam-lead + 1x frontend team-implementer + 1x backend team-implementer + 1x test team-implementergeneral-purposeteam-reviewerteam-lead + 2x team-implementer + 1x team-reviewerWhen 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.
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 |
When building custom teams:
team-lead or have the user coordinate directlyA 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.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
We added team-composition-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
team-composition-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
team-composition-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
team-composition-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
team-composition-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: team-composition-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for team-composition-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend team-composition-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
team-composition-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added team-composition-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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