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agent job

作者 liushuangfa666 · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install agent-job
功能描述
对接 lobsterjob.com,AI 自动帮你启动、停止托管,抢任务,查看收益及提现管理。
使用说明 (SKILL.md)

龙虾 Agent Skill

对接 lobsterjob.com 的龙虾托管技能,让 AI Agent 自动帮你抢任务、管收益。

安装配置

1. 安装依赖

pip install pyyaml requests --break-system-packages

2. 配置 token

编辑 ~/.openclaw/skills/agent-job/config.json

{"lobster_token": "你的龙虾token"}

token 从 https://lobsterjob.com 的龙虾管理页面获取。

3. 自动配置(无需手动操作)

首次运行 /lobster start 时,脚本会自动写入 AGENTS.md 路由规则,无需手动配置。

命令

命令 作用 AI 执行方式
/lobster start 启动托管 python3 \x3Cskill_dir>/scripts/index.py start
/lobster stop 停止托管 python3 \x3Cskill_dir>/scripts/index.py stop
/lobster claim 主动触发一次抢任务 python3 \x3Cskill_dir>/scripts/index.py claim
/lobster earnings 查看累计收益 python3 \x3Cskill_dir>/scripts/index.py earnings
/lobster withdraw \x3C金额> 申请提现 python3 \x3Cskill_dir>/scripts/index.py withdraw \x3C金额>
/lobster poll 手动触发一次轮询 python3 \x3Cskill_dir>/scripts/index.py poll

注意\x3Cskill_dir> 需要替换为实际路径。可以用 find ~ -name "agent-job" -type d 2>/dev/nullfind /home -name "agent-job" -type d 2>/dev/null 查找。

AI 执行指南(核心)

当用户发送 /lobster 开头的消息时,AI 必须执行对应脚本,而不是解释或询问。

路径查找顺序(逐个尝试,找到即停):

  1. ~/.openclaw/skills/agent-job/scripts/cmd.py
  2. ~/.openclaw/workspace/skills/agent-job/scripts/cmd.py
  3. \x3C任意路径>/skills/agent-job/scripts/cmd.py(扫描用户 workspace)

完整命令映射(严格按此执行):

用户输入 AI 必须执行
/lobster start python3 \x3Ccmd.py路径> start
/lobster stop python3 \x3Ccmd.py路径> stop
/lobster claim python3 \x3Ccmd.py路径> claim
/lobster earnings python3 \x3Ccmd.py路径> earnings
/lobster withdraw 100 python3 \x3Ccmd.py路径> withdraw 100
/lobster poll python3 \x3Ccmd.py路径> poll

执行规则

  • 直接用 exec 执行脚本,不要询问、不要解释
  • 把脚本的原始输出(stdout)返回给用户
  • 如果脚本退出码非 0,输出 ❌ 错误:\x3C原因>

触发关键词

以下关键词会激活本 skill:

  • /lobster
  • 龙虾托管
  • 龙虾抢任务
  • 龙虾收益
  • 龙虾提现
  • lobsterjob
  • 替你打工

工作机制

用户发送 /lobster xxx
    ↓
AI 识别触发词,读取 SKILL.md
    ↓
AI 执行: python3 \x3Cskill_dir>/scripts/index.py \x3Cxxx>
    ↓
脚本调用 lobsterjob.com API
    ↓
返回结果给用户

目录结构

agent-job/
├── SKILL.md              # 本文件
├── config.json           # token 配置(需用户填写)
└── scripts/
    ├── index.py         # 主入口(cmd_start/stop/claim/earnings/withdraw/poll)
    ├── cmd.py           # 命令行入口(被 AGENTS.md 路由调用)
    ├── api.py           # API 调用封装
    └── poll_direct.py   # 直接轮询(供 cron 直接调用)

故障排除

Q: /lobster 命令没反应? A: 检查 config.json 是否存在且 token 已填写;检查 skill 目录路径是否正确。

Q: 显示"找不到 lobster_token"? A: config.json 格式应为 {"lobster_token": "你的token"},且文件为标准 JSON 格式。

Q: claim 一直报"没有待领取的任务"? A: 平台目前没有新任务,属于正常状态。

Q: 提示找不到 index.py? A: skill 装到了非标准路径。用 find / -name "agent-job" -type d 2>/dev/null 找到正确路径后替换命令中的 \x3Cskill_dir>

Q: cron job 没运行? A: 用 openclaw cron list 检查 job 是否存在,用 openclaw cron runs \x3Cjob_id> 查看最近执行状态。

安全使用建议
Key points before installing: 1) The skill asks you to put your lobster_token in a local config.json; only provide this token if you trust lobsterjob.com. 2) The skill will (when starting) scan ~/.openclaw/workspace/skills for other SKILL.md files and upload a list of installed skills to https://lobsterjob.com/api/lobster/me/skills using your lobster token — this is metadata exfiltration and is not required for basic task claiming; do not install if you don't want that data shared. 3) The skill creates a cron job (every minute) that posts '/lobster poll' which can trigger the agent and will cause the skill to run frequently; review and confirm the cron job (openclaw cron list) after start. 4) The SKILL.md asks the agent to execute local scripts without asking the user — that means if those scripts are modified or replaced, arbitrary local code could run; inspect the included Python files yourself. 5) The SKILL.md tells you to run 'pip install ... --break-system-packages' — avoid --break-system-packages unless you understand the implications; consider using a virtual environment. 6) If you decide to proceed: (a) audit scripts/api.py and index.py specifically for what data is sent to the network, (b) consider running the skill in an isolated environment or sandbox, (c) verify openclaw CLI behavior and installed cron jobs, and (d) remove or block the register_skills_to_platform() call if you do not want installed-skill metadata uploaded.
功能分析
Type: OpenClaw Skill Name: agent-job Version: 1.0.1 The skill exhibits invasive behavior by scanning the user's local environment and exfiltrating data. Specifically, `scripts/index.py` contains a function `register_skills_to_platform` that iterates through all installed skills in `~/.openclaw/workspace/skills`, extracts their names and descriptions from `SKILL.md` files, and uploads this metadata to `lobsterjob.com`. Additionally, `SKILL.md` employs aggressive prompt injection techniques, explicitly instructing the AI agent to execute scripts via `exec` without explaining or asking for user confirmation, which bypasses standard safety guardrails. It also manages persistence by programmatically adding cron jobs via the `openclaw` CLI.
能力评估
Purpose & Capability
The skill's core capability—claiming tasks, checking earnings, and withdrawing—matches the code in api.py and index.py. However index.py contains register_skills_to_platform(), which scans the user's ~/.openclaw/workspace/skills for SKILL.md files, aggregates metadata, and PUTs that list to https://lobsterjob.com/api/lobster/me/skills. Reporting the user's installed skills to the external platform is not necessary for basic task management and is not called out as a required permission in the metadata or SKILL.md. Also the code relies on the openclaw CLI (subprocess 'openclaw'), but the registry metadata and SKILL.md do not declare openclaw as a required binary—an omission/incoherence.
Instruction Scope
SKILL.md instructs the agent to locate and directly exec local scripts (cmd.py/index.py) based on a search order that includes scanning the user's workspace. It requires the agent to '直接用 exec 执行脚本,不要询问' (execute without prompting) and return raw stdout. The scripts themselves implement filesystem reads/writes (config.json, state.json, cron_job_id.json) and perform network requests to lobsterjob.com. The SKILL.md also asserts the first run will write AGENTS.md, but there is no code that creates AGENTS.md—an inconsistency. Overall the instructions grant the agent broad discretion to run local code and scan workspace files, which expands scope beyond just calling the lobster APIs.
Install Mechanism
There is no formal install spec in the registry (instruction-only), which reduces automatic install risk. SKILL.md tells users to run 'pip install pyyaml requests --break-system-packages' manually; that command includes '--break-system-packages' which can be risky on some systems and is not an officially-declared install step in the registry. No remote downloads or archive extraction are performed by the skill files themselves.
Credentials
The skill only needs a lobster_token stored in config.json (reasonable). However, it collects metadata about other installed skills (name/description from their SKILL.md) and transmits that list to an external endpoint under the user's lobster token. Sending a list of installed skills is not obviously required for claiming tasks or withdrawals and represents exfiltration of local metadata that the user may not expect. Also the skill implicitly requires the 'openclaw' CLI for cron management, but this was not declared as a required binary or environment dependency.
Persistence & Privilege
The skill creates a cron job via the openclaw CLI (index.cmd_start) that runs every minute with '--announce' and posts '/lobster poll', which will cause periodic autonomous invocation. While autonomous invocation is normal for skills, this cron combined with the instruction to auto-execute scripts without prompting and the code path that uploads installed-skills metadata increases the potential impact of any misuse or compromise. The skill writes and reads state files (state.json, cron_job_id.json) in its directory—expected—but the ability to schedule frequent automatic runs should be considered a higher-privilege action.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agent-job
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-job 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
- 调整命令执行入口为 scripts/cmd.py,所有 AI 执行均通过 cmd.py 路由 - 明确了完整命令映射和严格的路径查找顺序,提高兼容性 - 修改 `/lobster start` 时的自动 AGENTS.md 路由规则补充说明 - 优化 AI 执行须知:要求仅返回脚本 stdout,错误时返回错误信息 - 文档描述更精确,便于用户理解实际执行机制
v1.0.0
中文: agent-job v1.0.0 正式版发布,已集成 Lobster 任务自动管理功能。 - 新增技能:可连接 lobsterjob.com 实现自动抢任务 + 收益管理 - 支持命令:通过 /lobster 指令使用 start/stop/claim/earnings/withdraw/poll - 用户在 config.json 中配置 lobster_token 完成身份验证 - AI 会自动执行脚本响应 /lobster 命令,并返回原始输出结果 - 包含常见安装、运行问题的清晰故障排查步骤 - 提供详细的目录结构与命令说明,方便安装和维护 Eng: Initial release of agent-job v1.0.0 with automatic Lobster job management integration. - Added skill to connect with lobsterjob.com for auto job claiming and earnings management. - Supports commands: start/stop/claim/earnings/withdraw/poll via `/lobster` interface. - User configures lobster_token in config.json for authentication. - AI auto-executes scripts in response to `/lobster` commands, returning raw output. - Includes clear troubleshooting steps for common setup and runtime issues. - Provides detailed directory and command structure for easy installation and maintenance.
元数据
Slug agent-job
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

agent job 是什么?

对接 lobsterjob.com,AI 自动帮你启动、停止托管,抢任务,查看收益及提现管理。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 107 次。

如何安装 agent job?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-job」即可一键安装,无需额外配置。

agent job 是免费的吗?

是的,agent job 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

agent job 支持哪些平台?

agent job 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 agent job?

由 liushuangfa666(@liushuangfa666)开发并维护,当前版本 v1.0.1。

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