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claude-code-teamsClaude 代码 teams

Agent Skill

claude-code-teams 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:claude-code-teams(Claude 代码 teams)
来源仓库:https://github.com/matthew-a-gordon/claude-code-teams
安装命令:
openclaw skills install claude-code-teams
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install claude-code-teams

简介

协调多个 Claude Code 代理并行执行独立任务。

  • 适用于全栈功能开发或多镜头审查等复杂工作流。
  • 支持定义文件驱动的分布式任务调度机制。
  • 需评估系统资源是否满足并发代理的运行需求。
  • 建议从小型任务集开始验证稳定性。claude-code-teams 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
claude-code-teams
description
Production skill for orchestrating Claude Code's native agent teams feature. Use when: (1) Multi-lens reviews, (2) Competing hypotheses debugging, (3) Full-stack features, (4) Architecture debates, (5) Cross-domain investigations.
metadata
openclaw
emoji
🎭
requires
bins
["claude"]
env
["CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS"]
version
1.0.0
author
Matthew Gordon
tags
["agents", "teams", "claude-code", "orchestration", "coordination"]

Claude Code Agent Teams

Orchestrate multiple Claude Code agents working together on complex tasks. Agents coordinate through a shared task list, message each other directly, and work in parallel on independent scopes.

When to Use

Use for parallel independent work:

  • Multi-lens code reviews (security, performance, tests in parallel)
  • Debugging with competing hypotheses (test theories simultaneously)
  • Full-stack features (frontend/backend/tests by separate specialists)
  • Architecture decisions (adversarial debate produces stronger ADRs)
  • Cross-domain investigations (bottleneck analysis across systems)
  • Data-parallel work (inventory classification, bulk refactoring)

Don't use for:

  • Sequential work with tight dependencies
  • Same-file edits (conflict risk)
  • Simple tasks (coordination overhead > benefit)
  • Learning/exploration (single agent with full context is better)

Rule of thumb: Teams multiply token cost 3-4x. Use only when parallelization benefit exceeds coordination overhead.

Quick Start

1. Enable Feature

Environment variable:

export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1

Or settings file (~/.claude/settings.json):

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

2. Validate Setup

./scripts/validate-setup.sh

Checks for: env var set, claude CLI available, tmux installed (optional).

3. Pick Template

TemplateUse Case
parallel-review.mdMulti-lens code review (security + performance + tests)
competing-hypotheses.mdDebug by testing competing theories
fullstack-feature.mdFrontend/backend/tests coordination
architecture-decision.mdADR with adversarial debate
bottleneck-analysis.mdCross-domain performance investigation
inventory-classification.mdData-parallel classification/refactoring

Each template includes: when to use, team structure, copy-paste spawn prompt, expected outcomes.

4. Spawn Team

claude --pty
# Paste spawn prompt from template

Monitor with ./scripts/team-monitor.sh (separate terminal) or Ctrl+T (in Claude Code).

Core Principles

  1. Clear file boundaries - Each teammate owns different files/directories (prevents conflicts)
  2. Use delegate mode - Lead coordinates, teammates implement (include in spawn prompt)
  3. Size tasks appropriately - 5-6 self-contained tasks per teammate
  4. Wait for completion - Don't let lead merge results while teammates are mid-task
  5. Start with research - Spawn researchers before builders to establish shared understanding

Cost Management

Agent teams multiply token usage 3-4x vs single session. Each teammate has its own context window.

Optimize with:

  • Model mixing - Opus for lead (strategic), Sonnet for teammates (tactical) = ~40% savings
  • Minimize broadcasts - Direct messages only (broadcasts copy to all teammates)
  • Right-size teams - 3 teammates usually optimal; 6+ only for highly parallel work

See references/cost-management.md for detailed strategies.

Troubleshooting

Teammates not spawning?

  • Verify CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set
  • Restart Claude Code after setting env var

File conflicts?

  • Define explicit file boundaries in spawn prompt (directory-level ownership)

Lead implementing instead of coordinating?

  • Add "Use delegate mode: coordinate but don't implement" to spawn prompt
  • Or press Shift+Tab to cycle into delegate mode

See references/troubleshooting.md for full list.

Automation

Monitor progress:

./scripts/team-monitor.sh

Cleanup (graceful shutdown):

./scripts/team-cleanup.sh

Display Modes

In-process (default) - All teammates in main terminal

  • Shift+Up/Down - Select teammate
  • Ctrl+T - Toggle task list
  • Escape - Interrupt teammate

Split-pane (requires tmux or iTerm2) - Each teammate gets own pane

  • Set teammateMode: "tmux" in ~/.claude/settings.json

References

  • templates/ - 6 copy-paste ready spawn prompts with usage guidance
  • scripts/ - Automation helpers (validate, monitor, cleanup)
  • references/best-practices.md - When to use teams, task sizing, file boundaries
  • references/cost-management.md - Token optimization strategies
  • references/troubleshooting.md - Common issues and solutions
  • examples/ - Real-world walkthroughs (PR review, bug hunt, feature build)

Comparison

ApproachBest For
Single sessionSequential work, exploration
Agent teams (this skill)Parallel independent tasks with coordination
OpenClaw subagentsBackground work, long-running tasks

Contributing

Found a useful pattern? Submit PR with:

  1. Template or example
  2. When to use + team structure
  3. Spawn prompt + expected outcomes

Credits

Built with insights from Claude Code documentation, ClaudeFast patterns, and OpenClaw community.


Repository: https://github.com/matthew-a-gordon/claude-code-teams License: MIT

适合场景

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能力概览

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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