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creat-anget

v1.0.1

创建具有独立工作区和配置的独立 OpenClaw Agent。适用于用户需要完全隔离的 Agent(独立工作区、配置、身份),而不是临时子 Agent 会话的情况。支持飞书机器人绑定,用于多机器人部署。

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for song5749/creat-anget.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "creat-anget" (song5749/creat-anget) from ClawHub.
Skill page: https://clawhub.ai/song5749/creat-anget
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install creat-anget

ClawHub CLI

Package manager switcher

npx clawhub@latest install creat-anget
Security Scan
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OpenClawOpenClaw
Benign
high confidence
Purpose & Capability
The name/description (create standalone agent, optional Feishu binding) match the provided scripts and SKILL.md. The scripts create agent/workspace directories, update openclaw.json, copy auth-profiles.json and models.json from the main agent, and optionally add Feishu credentials — all expected for this feature.
!
Instruction Scope
Instructions direct the agent to read and modify ~/.openclaw/openclaw.json, copy files from the main agent dir, and create workspace files. This is coherent, but the script will copy auth-profiles.json (which commonly contains sensitive credentials) and will create/overwrite workspace files if they don't exist (create_template_file writes files). The SKILL.md warns to use a new empty workspace but the script does not refuse to run against existing directories, so accidental overwrites are possible. No external network endpoints or hidden remote exfiltration are present in the code.
Install Mechanism
There is no install spec (instruction-only with included scripts); the action is local file operations (no downloads or package installs). This is low-risk from an install perspective.
Credentials
The skill declares no required environment variables or external credentials, which aligns with its local-file behavior. However, it is explicitly designed to accept and write Feishu appId/appSecret into openclaw.json and to copy auth-profiles.json from the main agent — both are sensitive operations. Storing secrets in openclaw.json and copying auth files is expected for this use case but raises the usual secrets-handling concerns (file permissions, rotation, avoiding commits). The SKILL.md also contains example secret values (placeholders) — do not reuse those.
Persistence & Privilege
The skill does not request always:true or any elevated platform privilege. It modifies only its own OpenClaw config (openclaw.json) and filesystem locations under the user's .openclaw; this is appropriate for an agent creation utility. It does not alter other skills' configurations beyond adding entries to the main openclaw.json.
Assessment
What to check before running/installing: - Inspect the two included scripts (scripts/create_standalone_agent.py and scripts/create_agent.py) yourself; they run locally and are short enough to review. They do not make network calls. - Backup ~/.openclaw/openclaw.json (the script creates a .json.bak but you may want your own copy) and any existing workspace directories. The script will write to openclaw.json and create/modify workspace files. - Be cautious about auth-profiles.json and models.json: the script copies these from the main agent. Those files often contain credentials or tokens; verify their contents and avoid unintentionally duplicating sensitive credentials to new agent directories. - Protect any Feishu appId/appSecret you provide: they will be written into openclaw.json in plaintext. Ensure file permissions restrict access and rotate keys if needed. - Use the --dry-run flag first to see what would change. Prefer creating the new agent/workspace in a temporary location to verify behavior before deploying to production. - If you want stricter safety, run the script in a sandbox or under a non-privileged user account so accidental overwrites are contained. Overall: the skill appears to do what it claims, but it performs sensitive local configuration changes and handles secrets — review and secure your config files before use.

Like a lobster shell, security has layers — review code before you run it.

latestvk97bq6raqcf2798vve0qp0math839fz3
201downloads
0stars
2versions
Updated 20h ago
v1.0.1
MIT-0

Agent Creator (Agent 创建器)

此技能帮助你创建具有独立工作区、配置和可选飞书机器人绑定的 独立 OpenClaw Agent

何时使用

当用户希望执行以下操作时使用此技能:

  • 创建一个拥有自己工作区的完全隔离的 Agent
  • 为特定项目/客户设置专用 Agent
  • 部署多个飞书机器人,每个机器人具有独立的身份
  • 创建长期运行的专用 Agent(而不是临时子 Agent)

触发请求示例:

  • "帮我创建一个客服 agent,用独立的 workspace"
  • "我需要一个专门处理设计师工作的 agent"
  • "创建一个新的飞书机器人,用于内部 IT 支持"
  • "Create a standalone agent for customer support"
  • "Set up a new Feishu bot for the HR team"

两种模式

模式 1: Standalone Agent (独立 Agent - 推荐)

创建一个完全隔离的 Agent,包含:

  • 独立的工作区目录
  • openclaw.json 中单独的 Agent 配置
  • 可选的飞书机器人绑定
  • 独立的身份和记忆

模式 2: Subagent Session (子 Agent 会话 - 临时)

创建一个临时子 Agent 会话(用于快速任务)。仅用于短期的辅助工作。


独立 Agent 创建流程

第 1 步:收集需求

询问用户(如果尚未提供):

问题目的
Agent ID唯一标识符(例如:support, hr-bot, designer
Agent Name易读的名称
Workspace Path存储 Agent 文件的位置(默认:~/.openclaw/workspace-{id}
Feishu Bot?此 Agent 是否需要绑定飞书机器人?
Feishu Account ID如果绑定到飞书,使用哪个账户 ID?(例如:support, hr
Model此 Agent 应该使用哪个模型?

第 2 步:创建 Agent 目录结构

创建 Agent 目录:

~/.openclaw/agents/{agent-id}/
├── agent/
│   ├── auth-profiles.json
│   └── models.json
└── sessions/

第 3 步:更新 openclaw.json

将 Agent 添加到 agents.list

{
  "agents": {
    "list": [
      {
        "id": "{agent-id}",
        "name": "{agent-name}",
        "workspace": "{workspace-path}",
        "agentDir": "{agent-dir-path}"
      }
    ]
  }
}

第 4 步:创建飞书绑定(可选)

如果 Agent 需要飞书机器人,添加绑定:

{
  "bindings": [
    {
      "type": "route",
      "agentId": "{agent-id}",
      "match": {
        "channel": "feishu",
        "accountId": "{account-id}"
      }
    }
  ]
}

第 5 步:配置飞书账户(可选)

如果使用单独的飞书应用,添加到 channels.feishu.accounts

{
  "channels": {
    "feishu": {
      "accounts": {
        "{account-id}": {
          "enabled": true,
          "appId": "{feishu-app-id}",
          "appSecret": "{feishu-app-secret}",
          "domain": "feishu",
          "groupPolicy": "open"
        }
      }
    }
  }
}

第 6 步:初始化 Agent 工作区

创建包含必要文件的工作区目录:

{workspace-path}/
├── AGENTS.md
├── SOUL.md
├── USER.md
├── IDENTITY.md
├── MEMORY.md
├── TOOLS.md
├── HEARTBEAT.md
└── memory/

第 7 步:重启 Gateway

修改 openclaw.json 后,Gateway 需要重新加载:

openclaw gateway restart

或者通知用户手动重启。


脚本使用

使用提供的脚本自动创建 Agent:

python scripts/create_standalone_agent.py \
  --agent-id support \
  --agent-name "Customer Support Bot" \
  --workspace "C:\Users\Administrator\.openclaw\workspace-support" \
  --feishu-account support \
  --feishu-app-id cli_xxx \
  --feishu-app-secret xxx

快速参考

所需信息

字段必填示例
agent-idsupport, hr-bot, designer
agent-name"Customer Support", "HR Assistant"
workspace-path~/.openclaw/workspace-support
feishu-bindingtruefalse
feishu-account-idsupport, hr
feishu-app-idcli_a9249b9ee9785cee
feishu-app-secretPAY8vhyLkiLpfmun09sXSboJyoSQXK3g

配置示例

简单 Agent(无飞书)

{
  "agent-id": "research-assistant",
  "agent-name": "Research Assistant",
  "workspace": "C:\\Users\\Administrator\\.openclaw\\workspace-research"
}

带飞书绑定的 Agent(共享应用)

{
  "agent-id": "support",
  "agent-name": "Customer Support",
  "workspace": "C:\\Users\\Administrator\\.openclaw\\workspace-support",
  "feishu-binding": true,
  "feishu-account-id": "support"
}

带专用飞书应用的 Agent

{
  "agent-id": "hr-bot",
  "agent-name": "HR Assistant",
  "workspace": "C:\\Users\\Administrator\\.openclaw\\workspace-hr",
  "feishu-binding": true,
  "feishu-account-id": "hr",
  "feishu-app-id": "cli_xxx",
  "feishu-app-secret": "xxx"
}

注意事项

  • Agent ID 在所有 Agent 中必须唯一
  • Workspace 应该是一个新的空目录
  • Feishu Account ID 用于绑定匹配,而不是实际的 App ID
  • Gateway Restart 修改 openclaw.json 后必须重启
  • Auth ProfilesModels 默认从主 Agent 复制
  • Security: 小心处理飞书应用密钥;存储在 openclaw.json 中应具有适当的权限

子 Agent 模式(旧版)

对于临时助手,你仍然可以使用 sessions_spawn

sessions_spawn({
  task: "<任务描述>",
  runtime: "subagent" | "acp",
  mode: "session" | "run",
  label: "<Agent 名称>",
  model: "<模型别名>",
  thread: true | false
})

但对于生产环境 Agent,请首选独立模式。


参考文件

  • 详细指南: 见 references/standalone-agent-guide.md 以获取完整文档
  • 脚本帮助: 运行 python scripts/create_standalone_agent.py --help

快速开始

# 简单 Agent(无飞书)
python scripts/create_standalone_agent.py \
  --agent-id my-agent \
  --agent-name "My Agent"

# 带飞书绑定的 Agent
python scripts/create_standalone_agent.py \
  --agent-id support \
  --agent-name "Support Bot" \
  --feishu-account support

# 然后重启 Gateway
openclaw gateway restart

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