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Agent Skill · 研究检索

claude-code-teams(Claude 代码 teams)

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。

claude-code-teams 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 3,633。

总安装 41,885周安装 1,711GitHub Stars 2下载 13,414核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install claude-code-teams;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

claude-code-teams 怎么安装?

在终端执行 openclaw skills install claude-code-teams。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

claude-code-teams 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

claude-code-teams 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

claude-code-teams 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

claude-code-teams 用的人多吗?

公开数据是累计安装 41,885 次,近一周安装 1,711 次,下载 13,414 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

claude-code-teams 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
claude-code-teams

Claude 代码 teams · 本页

41,8852接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

OpenClaw

92.7%
按下载量换算12,435

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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