Agent Team
VerifiedAgent Team — AI and machine learning tool.
$ Add to .claude/skills/ About This Skill
Content available in Chinese
# agent-team - 多 Agent 团队管理系统
管理和调用具有不同"灵魂"的子 Agent 团队,每个 Agent 拥有独特的身份定义和专用模型。
使用方法
基础命令 ```bash agent-team list # 列出所有 Agent agent-team show <name> # 查看 Agent 详情 agent-team spawn <name> [task] # 启动 Agent 执行任务 agent-team chat <name> # 与 Agent 交互对话 ```
示例 ```bash # 查看团队所有成员 agent-team list
# 查看 Coder 的详细信息 agent-team show coder
# 让 Coder 帮你写代码 agent-team spawn coder "写一个快速排序算法"
# 与 Writer 对话讨论写作 agent-team chat writer ```
内置 Agent 团队
| Agent | 角色 | 主模型 | 专长 | |:---:|:---|:---|:---| | 🧑💻 coder | 代码专家 | qwen3-coder-next | 编程、重构、测试 | | ✍️ writer | 写作专家 | qwen3.5-plus | 文档、博客、创意写作 | | 📊 analyst | 数据专家 | qwen3.5-plus | 数据分析、可视化 | | 🔍 researcher | 调研专家 | gemini-3.1-pro | 文献调研、竞品分析 | | 👀 reviewer | 审查专家 | qwen3-max | 代码审查、质量把关 |
创建新 Agent
1. 创建目录 ```bash mkdir -p ~/.openclaw/workspace/agents/<agent-name> ```
2. 定义 SOUL.md ```markdown # SOUL.md - <Agent Name>
身份 你是...
性格特质 - ...
专业领域 - ...
沟通风格 - ... ```
3. 创建 config.json ```json { "name": "AgentName", "role": "角色描述", "emoji": "🤖", "model": { "primary": "dashscope/qwen3.5-plus", "fallback": "google/gemini-3-flash-preview" }, "capabilities": ["能力 1", "能力 2"] } ```
模型配置说明
每个 Agent 可以配置不同的模型:
- dashscope/qwen3-coder-next: 编码专用
- dashscope/qwen3.5-plus: 通用中文优化
- dashscope/qwen3-max: 最强推理
- google/gemini-3.1-pro: 深度研究
- google/gemini-3-flash-preview: 快速响应
工作模式
1. 任务模式 (spawn) ```bash agent-team spawn coder "优化这个函数" ``` - 创建独立子代理会话 - 执行指定任务 - 完成后返回结果
2. 对话模式 (chat) ```bash agent-team chat analyst ``` - 进入交互式对话 - 保持角色一致性 - 适合复杂协作
高级用法
并行多 Agent ```bash # 同时启动多个 Agent agent-team spawn coder "写代码" & agent-team spawn writer "写文档" & agent-team spawn reviewer "审查代码" & wait ```
链式调用 ```bash # Coder 写代码 → Reviewer 审查 → Writer 写文档 agent-team spawn coder "实现功能" agent-team spawn reviewer "审查代码" agent-team spawn writer "编写文档" ```
文件结构
``` ~/.openclaw/workspace/ ├── agents/ │ ├── coder/ │ │ ├── SOUL.md # 身份定义 │ │ └── config.json # 模型配置 │ ├── writer/ │ └── ... └── skills/ └── agent-team/ ├── agent-team.py # 管理脚本 └── SKILL.md # 本文档 ```
故障排除
- Agent 未找到: 检查 `~/.openclaw/workspace/agents/` 目录
- 模型不可用: 确认模型配置正确且 API Key 有效
- 会话无法创建: 检查 OpenClaw 网关状态
相关技能
- `sessions_spawn`: OpenClaw 原生子代理创建
- `self-improving`: 自我进化记忆系统
Use Cases
- Manage a team of sub-agents with different identities and specialized models
- Spawn named agents with unique soul definitions for role-specific tasks
- Switch between agent team members for interactive conversations
- List and inspect all available agents with their capabilities and status
- Assign tasks to specific agents based on their expertise and model strengths
Pros & Cons
Pros
- +Each agent gets a unique identity ('soul') — enables genuine specialization
- +Supports different AI models per agent for optimal task-model matching
- +Simple CLI interface: list, show, spawn, chat commands
Cons
- -Documentation in Chinese — limits international accessibility
- -Running multiple agents simultaneously increases resource and API costs
- -No built-in task routing — manual agent selection required
FAQ
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