MCPInstall

subagent-teams

Orchestrate in-session Task tool teams for parallel work. Fan-out research, implementation, review, and documentation across subagents. Use when: parallel tasks, fan-out, subagent team, Task tool, in-session agents.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/scientiacapital/skills --skill subagent-teams
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads subagent-teams automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

<objective> Orchestrate teams of Task tool subagents within a single Claude Code session. Unlike agent-teams-skill (which uses worktrees + terminals for full parallel sessions), this skill uses the Task tool for lightweight, in-session parallelism with shared codebase access. </objective>

<quick_start> Research fan-out:

Launch 3 Explore agents in parallel:
- Agent 1: Search for authentication patterns
- Agent 2: Search for database schema
- Agent 3: Search for API endpoints

Implementation fan-out:

1. Plan agent designs architecture
2. 3 general-purpose agents build components in parallel
3. code-reviewer agent validates all changes

</quick_start>

<success_criteria>

  • Subagents spawned with correct model tiers (Haiku for search/review, Sonnet for code, Opus for architecture)
  • Parallel agents complete independently without conflicting file edits
  • Fan-in synthesis captures key findings from all background agents
  • Total parallel agents stays within 5-7 limit to avoid context overflow
  • TaskCreate/TaskUpdate used for progress tracking with live UI spinners </success_criteria>
<triggers> - "set up subagent team", "fan out", "parallel tasks", "Task tool team" - "research in parallel", "explore in parallel", "review in parallel" - "spawn subagents", "in-session agents" </triggers>

When to Use This vs agent-teams

Factorsubagent-teams (this)agent-teams
IsolationShared codebase, shared contextFull worktree isolation
OverheadLightweight — just Task tool callsHeavy — terminals, git branches, ports
Best forResearch, review, doc updatesFeature builds, conflicting file edits
Max agents5-7 (context limit)2-3 (M1 8GB RAM limit)
DurationMinutesHours
CoordinationTeamCreate + TaskList/TaskUpdateWORKTREE_TASK.md + git branches

Rule of thumb: If agents will edit the same files → use agent-teams (worktree isolation). If agents read-only or edit different files → use subagent-teams (faster, lighter).


Task Tool Parameters (Complete Reference)

Core Parameters

{
  subagent_type: "Explore" | "general-purpose" | "Plan" | ...,
  model: "haiku" | "sonnet" | "opus",
  prompt: "...",
  description: "3-5 word summary",        // Required
  run_in_background: true,                 // For parallel execution
  team_name: "my-team",                    // Scope to a team's task list
  name: "agent-1",                         // Name for team messaging
  mode: "default"                          // Permission mode (see below)
}

Agent Frontmatter Fields (for .md agent files)

FieldTypePurpose
namestringAgent identifier
descriptionstringWhat the agent does (shown in routing)
modelstringDefault model: haiku, sonnet, opus
toolslistAllowed tools (restrict agent capabilities)
disallowedToolslistExplicitly blocked tools
permissionModestringdefault, acceptEdits, dontAsk, plan
mcpServerslistMCP servers available to the agent
hooksobjectEvent-driven automation (PostToolUse, etc.)
maxTurnsnumberMax API round-trips before stopping
skillslistSkills available to the agent
memoryobjectPersistent state (see Memory Scopes below)

Memory Scopes

The memory field gives agents persistent state across sessions:

# User-scoped: shared across all projects for this user
memory:
  scope: user    # Stored in ~/.claude/agent-memory/

# Project-scoped: shared across sessions within one project
memory:
  scope: project # Stored in .claude/agent-memory/

# Local-scoped: private to this machine + project combo
memory:
  scope: local   # Stored in .claude/local/agent-memory/

When to use: user for personal preferences/patterns. project for shared team knowledge. local for machine-specific paths or credentials.

Background Execution

Use run_in_background: true for agents that don't block your next action:

// Launch in background — returns immediately with output_file path
Task({
  subagent_type: "Explore",
  prompt: "Search for all auth patterns",
  run_in_background: true  // Non-blocking
})

// Check results later
TaskOutput({ task_id: "agent-id", block: false })  // Non-blocking check
TaskOutput({ task_id: "agent-id", block: true })    // Wait for completion

Foreground vs background:

  • Foreground (default): Use when you need results before proceeding — research that informs next steps
  • Background: Use when you have independent work to do in parallel — observers, linters, long searches

Tip: Background agents are ideal for observer-lite/observer-full, security scans, and parallel research where you can synthesize results later.

Permission Modes

ModeBehavior
defaultNormal approval flow
acceptEditsAuto-approve file edits, prompt for Bash
dontAskAuto-approve everything (use with trusted agents)
planAgent must get plan approved before implementing
delegateAgent can only delegate to sub-agents

Spawning Restrictions

Restrict which subagents an agent can spawn using Task(agent_type) in the tools field:

tools:
  - Read
  - Glob
  - Task(Explore)        # Can only spawn Explore subagents
  - Task(code-reviewer)  # Can also spawn code reviewers

Built-in agent types and their tool access:

Agent TypeToolsBest For
ExploreGlob, Grep, Read, LS, WebFetch, WebSearchFast codebase search (read-only)
PlanGlob, Grep, Read, LS, WebFetch, WebSearchArchitecture design (read-only)
general-purposeAll toolsImplementation, full access
feature-dev:code-reviewerGlob, Grep, Read, LS, WebFetchCode review (read-only)
feature-dev:code-explorerGlob, Grep, Read, LS, WebFetchDeep feature analysis (read-only)
feature-dev:code-architectGlob, Grep, Read, LS, WebFetchArchitecture blueprints (read-only)
observer-liteRead, Glob, Grep, Bash, WriteQuick quality checks
observer-fullRead, Glob, Grep, Bash, WriteFull drift detection

Custom agents: Define in .claude/agents/*.md with frontmatter. Reference by filename (without .md).

Model Selection Guide

TaskModelWhy
File search, pattern matchinghaikuFast, cheap, sufficient
Code review, bug findinghaikuPattern matching, not generation
Code generation, refactoringsonnetQuality matters for code
Architecture decisionsopusComplex reasoning needed
Documentation writingsonnetNeeds context understanding

Team Patterns

1. Research Team (3 Explore agents)

Fan-out 3 search strategies, fan-in to synthesize:

Task 1 (Explore, haiku): "Search for [pattern] in src/"
Task 2 (Explore, haiku): "Search for [pattern] in tests/"
Task 3 (Explore, haiku): "Search for [pattern] in docs/"
→ Fan-in: Synthesize findings into summary

When: Exploring unfamiliar codebase, understanding how a feature works across layers.

2. Implement Team (architect → builders → reviewer)

Sequential pipeline with parallel build phase:

Phase 1: Plan agent designs architecture (1 agent)
Phase 2: 2-3 general-purpose agents build components (parallel)
Phase 3: code-reviewer validates (1 agent)

When: Building a feature with multiple independent components.

3. Review Team (3 reviewers in parallel)

Task 1 (code-reviewer, haiku): "Review src/auth/ for security"
Task 2 (code-reviewer, haiku): "Review src/api/ for consistency"
Task 3 (code-reviewer, haiku): "Review src/db/ for performance"
→ Fan-in: Aggregate findings, deduplicate

When: Pre-PR review of large changesets.

4. Explore Team (3 search strategies)

Task 1 (Explore, haiku): Glob for file patterns
Task 2 (Explore, haiku): Grep for code patterns
Task 3 (Explore, haiku): Read key entry points
→ Fan-in: Build mental model of codebase area

When: First time working in a new area of the codebase.

5. Doc Team (N independent file updaters)

Task 1 (general-purpose, haiku): "Update README.md with new API"
Task 2 (general-purpose, haiku): "Update CHANGELOG.md"
Task 3 (general-purpose, haiku): "Update API docs"
→ No fan-in needed (independent files)

When: Updating multiple independent documentation files.


Progress Rendering

Native Progress (TaskCreate/TaskUpdate)

Use TaskCreate with activeForm for live UI spinners during execution:

// Create tasks for each agent's work
TaskCreate({ subject: "Search auth patterns", activeForm: "Searching auth patterns" })
TaskCreate({ subject: "Search DB schema", activeForm: "Searching DB schema" })
TaskCreate({ subject: "Search API endpoints", activeForm: "Searching API endpoints" })

// Track status transitions
TaskUpdate({ taskId: "1", status: "in_progress" })  // → shows spinner
TaskUpdate({ taskId: "1", status: "completed" })     // → shows checkmark

Task Dependencies (Sequential Phases)

Use addBlockedBy to sequence phases:

// Phase 1: Architecture (runs first)
TaskCreate({ subject: "Design architecture" })  // → task #1

// Phase 2: Implementation (blocked by Phase 1)
TaskCreate({ subject: "Build backend" })   // → task #2
TaskCreate({ subject: "Build frontend" })  // → task #3
TaskUpdate({ taskId: "2", addBlockedBy: ["1"] })
TaskUpdate({ taskId: "3", addBlockedBy: ["1"] })

// Phase 3: Review (blocked by Phase 2)
TaskCreate({ subject: "Code review" })  // → task #4
TaskUpdate({ taskId: "4", addBlockedBy: ["2", "3"] })

Summary Rendering (Markdown)

After all agents complete, render a markdown summary:

## Research Complete: 3/3 agents finished

| Agent | Scope | Findings | Time |
|-------|-------|----------|------|
| Auth search | src/auth/ | 12 files, JWT + session | 8s |
| DB search | src/db/ | 8 tables, RLS policies | 5s |
| API search | src/api/ | 15 endpoints, REST | 6s |

### Key Insights
- [Synthesized finding 1]
- [Synthesized finding 2]

Team Coordination (Native Agent Teams API)

For complex multi-agent work, use the native Teams API:

// Create a team with shared task list
TeamCreate({ team_name: "research-sprint" })

// Spawn teammates into the team
Task({ subagent_type: "Explore", team_name: "research-sprint", name: "searcher-1" })
Task({ subagent_type: "Explore", team_name: "research-sprint", name: "searcher-2" })

// Teammates coordinate via shared TaskList
// Send messages between teammates
SendMessage({ type: "message", recipient: "searcher-1", content: "Focus on auth/" })

// Shutdown when done
SendMessage({ type: "shutdown_request", recipient: "searcher-1" })

Prompt Templates

Research Spawn

Search the codebase for [PATTERN]. Look in [SCOPE].
Report: file paths, line numbers, and a 2-sentence summary of each match.
Do NOT modify any files.

Build Spawn

Implement [COMPONENT] in [FILE_PATH].
Requirements: [SPEC]
Follow existing patterns in [EXAMPLE_FILE].
Write code only — do not run tests.

Review Spawn

Review [FILE_PATH] for [CONCERN: security|performance|consistency].
Report only HIGH confidence issues.
Format: file:line — issue — suggestion

Constraints

  • Max 5-7 parallel agents — beyond this, context window fills up
  • No conflicting file edits — if agents might edit the same file, use agent-teams instead
  • Fan-in is manual — you (team lead) synthesize results from background agents
  • Background agents can't see each other — design tasks to be independently completable

Deep dive: See reference/task-tool-guide.md, reference/team-patterns.md, reference/prompt-templates.md

Emit Outcome Sidecar

As the final step, write to ~/.claude/skill-analytics/last-outcome-subagent-teams.json:

{"ts":"[UTC ISO8601]","skill":"subagent-teams","version":"1.1.0","variant":"default",
 "status":"[success|partial|error]","runtime_ms":[estimated ms from start],
 "metrics":{"agents_spawned":[n],"tasks_completed":[n],"tasks_failed":[n]},
 "error":null,"session_id":"[YYYY-MM-DD]"}

Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.

FAQ

What does the subagent-teams skill do?

Orchestrate in-session Task tool teams for parallel work. Fan-out research, implementation, review, and documentation across subagents. Use when: parallel tasks, fan-out, subagent team, Task tool, in-session agents.

How do I install subagent-teams?

Run npx skills add https://github.com/scientiacapital/skills --skill subagent-teams in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/subagent-teams/ (personal) or .claude/skills/subagent-teams/ (project).

Which AI agents can use subagent-teams?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is subagent-teams safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains subagent-teams?

It is published by scientiacapital in scientiacapital/skills. It has 70 recorded installs.

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