No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by justlovemaki · Codex Skill · ★ 3.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openclaw-china-docker safe to install? View the security audit →
OpenClaw-China-Docker (暂停更新) 面向中国 IM 场景的 OpenClaw Docker 整合镜像，预装飞书、钉钉、QQ 机器人、企业微信等常用插件，适合快速搭建统一的 AI 机器人网关。 🚀 推荐搭配：OpenClaw 功能强大但 Token 消耗较大，推荐配合 AIClient-2-API 项目使用，将各大 AI 客户端转换为 API 接口，实现无限 Token 调用，彻底解决 Token 焦虑！本项目已支持 OpenAI 和 Claude 两种协议，可直接对接 AIClient-2-API 服务。 💡 AI 助手提示：克隆本项目并准备好渠道机器人与 AI API 信息填入 文件中，在 AI CLI（如 Claude Code/Gemini CLI）中输入：。 镜像下载量超100k 好用给个赞助吧 🚀 核心特性 本项目提供开箱即用的 OpenClaw 体验，并针对国内 IM 场景进行了深度增强： 全平台支持：预装飞书（官方/旧版）、钉钉、QQ 机器人、企业微信插件，并集成 Agent Reach 支持 Twitter、小红书、微博、抖音等全网渠道。 配置驱动：支持通过环境变量自动生成配置，提供 (完整版) 和 (精简版) 快速上手。 工具集增强：集成 [Agen
| Stars | 3,728 |
| Forks | 445 |
| Language | Shell |
| Category | Codex Skill |
| License | GPL-3.0 |
| Quality Score | 59.690116918768/100 |
| Open Issues | 12 |
| Last Updated | 2026-04-14 |
| Created | 2026-02-01 |
| Platforms | cli, docker |
| Est. Tokens | ~11k |
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openclaw-china-docker is OpenClaw 的中国IM平台整合Docker版本，预装并配置了飞书、钉钉、QQ机器人、企业微信等主流中国IM软件的插件，让您可以快速部署一个支持多个中国IM平台的 AI 机器人网关. It is categorized as a Codex Skill with 3.7k GitHub stars.
openclaw-china-docker is primarily written in Shell.
You can find installation instructions and usage details in the openclaw-china-docker GitHub repository at github.com/justlovemaki/openclaw-china-docker. The project has 3.7k stars and 445 forks, indicating an active community.
openclaw-china-docker is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-china-docker on Agent Skills Hub include superset, refly, Claude-to-IM-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: