据 AlphaMoat 统计,截至 2026-09,Claude Code Agent Teams 累计下载 2,543 次、获 3 个星标,在全部 5,078 个 AI 技能中排名第 4,063 位,在「AI 智能体」分类(969 个)中排名第 822 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 74 | 2,489 | 2 | - |
| SkillHub ↗ | 105 | 2,543 | 3 | — |
各平台的计数口径不同,统计时间不一致。
文档建议先明确任务边界、角色和整合步骤,并权衡协调成本。强制清理可能终止目标团队之外的 PTY 会话并丢失未保存工作;应先检查清理脚本,优先采用优雅关闭。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 2,543 | — | 3 |
| 2026-08 | 2,369 | +6.6% | 3 |
| 2026-07 | 2,222 | +13.9% | 3 |
| 2026-06 | 1,951 | +9.5% | 3 |
| 2026-05 | 1,781 | — | 3 |
| 2026-03 | 1,195 | — | 2 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
---
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:**
```bash
export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
```
**Or settings file** (`~/.claude/settings.json`):
```json
{
"env": {
"CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
}
}
```
### 2. Validate Setup
```bash
./scripts/validate-setup.sh
```
Checks for: env var set, `claude` CLI available, `tmux` installed (optional).
### 3. Pick Template
| Template | Use Case |
|----------|----------|
| `parallel-review.md` | Multi-lens code review (security + performance + tests) |
| `competing-hypotheses.md` | Debug by testing competing theories |
| `fullstack-feature.md` | Frontend/backend/tests coordination |
| `architecture-decision.md` | ADR with adversarial debate |
| `bottleneck-analysis.md` | Cross-domain performance investigation |
| `inventory-classification.md` | Data-parallel classification/refactoring |
Each template includes: when to use, team structure, copy-paste spawn prompt, expected outcomes.
### 4. Spawn Team
```bash
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:**
```bash
./scripts/team-monitor.sh
```
**Cleanup (graceful shutdown):**
```bash
./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
| Approach | Best For |
|----------|----------|
| Single session | Sequential work, exploration |
| Agent teams (this skill) | Parallel independent tasks with coordination |
| OpenClaw subagents | Background 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
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把 agent 的记忆与跨技能状态存成带类型和约束的知识图谱的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。