agent-team

name	agent-team
description	This skill should be used when the user asks to "create a team", "spawn agents", "parallel execution", "delegate tasks to agents", "use a swarm", "team workflow", "split work across agents", "run tasks in parallel", or mentions wanting multiple agents to collaborate on a complex task.

Agent Team Orchestration

Orchestrate multi-agent teams to decompose complex tasks, select optimal sub-agents, and execute work in parallel.

When to Use

• Tasks requiring parallel work across multiple domains (frontend + backend, research + implementation)
• Complex multi-step tasks benefiting from specialization (plan, implement, test, review)
• Large-scale refactoring or feature development spanning many files
• Tasks requiring both research and implementation phases

Core Workflow

1. Task Decomposition

Analyze the user's request and break it into independent, parallelizable units:

1. Identify the goal and deliverables
2. Split into sub-tasks that can run concurrently
3. Identify dependencies between sub-tasks (what blocks what)
4. Assign each sub-task to the optimal agent type

2. Agent Type Selection

Select the optimal subagent_type for each sub-task based on the work required.

Built-in Agents

Agent Type	Best For	Tools Available
general-purpose	Implementation, file editing, full-stack work	All tools (Read, Write, Edit, Bash, Glob, Grep, etc.)
Explore	Codebase research, finding patterns, understanding architecture	Read-only (Glob, Grep, Read, WebFetch, WebSearch)
Plan	Design decisions, architecture planning, implementation strategy	Read-only (same as Explore)
Bash	Git operations, build commands, CLI tasks	Bash only
code-simplifier	Refactoring, cleanup, code quality improvements	All tools

Project Custom Agents (.claude/agents/)

Specialized agents with project-specific context pre-loaded. Prefer these over general-purpose when the task falls within their domain.

Agent Type	Domain	Scope Restriction
backend-dev	Hono + Drizzle ORM + SQLite API 実装	packages/server/ のみ
frontend-dev	Next.js 16 + React 19 + Tailwind UI 実装	packages/web/ のみ
tester	Vitest テスト設計・実装・実行	packages/server/src/__tests__/

Custom agents already know the project's coding conventions, file structure, and reference files -- no need to repeat this context in the prompt.

Selection Rules

• Research/investigation -> Explore (fast, read-only)
• Architecture planning -> Plan (structured output, read-only)
• Backend API changes -> backend-dev (project-aware, scoped to server)
• Frontend UI changes -> frontend-dev (project-aware, scoped to web)
• Test writing/execution -> tester (project-aware, knows mock patterns)
• Cross-package or general changes -> general-purpose (full access)
• Build/deploy commands -> Bash (minimal overhead)
• Refactoring -> code-simplifier (specialized for cleanup)

3. Team Spawn Pattern

For parallel independent tasks (no team coordination needed):

Use multiple Task tool calls in a single message:
- Task 1: subagent_type="Explore", prompt="Research X"
- Task 2: subagent_type="general-purpose", prompt="Implement Y"
- Task 3: subagent_type="Bash", prompt="Run tests"

For coordinated multi-step tasks (team with shared task list):

1. TeamCreate -> create team with name and description
2. TaskCreate -> create all tasks with dependencies
3. Task tool -> spawn teammates with team_name parameter
4. TaskUpdate -> assign tasks to teammates
5. Monitor progress via TaskList
6. SendMessage -> coordinate as needed
7. Shutdown teammates when done
8. TeamDelete -> clean up

4. Model Selection

Optimize cost and latency by selecting the right model per agent:

Model	When to Use
haiku	Simple tasks: file lookups, running commands, straightforward edits
sonnet	Most tasks: implementation, research, moderate complexity
opus	Complex tasks: architecture decisions, nuanced analysis, multi-file refactoring

Default: inherit parent model. Override with the model parameter on the Task tool.

Common Team Patterns

Pattern A: Research-then-Implement

Parallel research phase, then sequential implementation:

Phase 1 (parallel):
  - Explore agent: "Investigate current codebase patterns for X"
  - Explore agent: "Find all files related to Y"
  - Plan agent: "Design approach for Z"

Phase 2 (after results):
  - general-purpose agent: "Implement based on research findings"

Pattern B: Parallel Feature Development

Split frontend/backend using project custom agents:

Parallel:
  - backend-dev agent: "Implement API endpoint for user settings"
  - frontend-dev agent: "Build settings page UI"
  - tester agent: "Write tests for the new settings route"

Pattern C: Review Pipeline

Multi-stage quality assurance:

Sequential:
  1. general-purpose: "Implement feature"
  2. Parallel review:
     - Explore: "Check for security issues"
     - Explore: "Verify test coverage"
     - code-simplifier: "Simplify and refactor"

Pattern D: Large-Scale Refactoring

Divide-and-conquer across file groups:

Phase 1 (Explore): "Map all files needing changes"
Phase 2 (parallel general-purpose agents):
  - Agent 1: "Refactor module A files"
  - Agent 2: "Refactor module B files"
  - Agent 3: "Refactor module C files"
Phase 3 (Bash): "Run full test suite"

Key Guidelines

Parallelization Rules

• Launch independent tasks in a single message with multiple Task tool calls
• Use run_in_background: true for long-running tasks that don't need immediate results
• Check background task results with the Read tool on the output_file

Agent Communication

• Teammates communicate via SendMessage (plain text, not JSON)
• Messages are delivered automatically - no polling needed
• Use broadcast only for critical team-wide issues
• Idle state is normal - send a message to wake a teammate

Task Dependencies

• Use addBlockedBy / addBlocks to establish ordering
• Teammates should check TaskList after completing each task
• Prefer ID order (lowest first) when multiple tasks are available

Cost Optimization

• Use haiku model for simple, well-defined subtasks
• Use Explore instead of general-purpose for read-only work (faster, cheaper)
• Avoid spawning teams for tasks completable in a single agent turn
• Minimize broadcasts - prefer targeted messages

Additional Resources

Reference Files

For detailed agent selection criteria and advanced patterns:

• references/subagent-types.md - Complete guide to each agent type with capabilities and limitations
• references/team-patterns.md - Advanced team patterns with real-world examples

Example Files

Working team configurations:

• examples/parallel-feature.md - Parallel frontend + backend development
• examples/research-implement.md - Research phase followed by implementation
• examples/review-pipeline.md - Multi-agent code review pipeline