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 HenryXiaoYang · Codex Skill · ★ 640
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wechat-openclaw-channel safe to install? View the security audit →
wechat-openclaw-channel v1.1.0 — 现已支持 WorkBuddy 模式！ 无需内测码，通过 WorkBuddy 账号即可登录使用。 OpenClaw 微信通路插件 — 支持 QClaw 和 WorkBuddy 双模式。 安装 快速开始 登录 交互式选择登录模式： QClaw — 微信扫码登录 目前需要内测码。 WorkBuddy — CodeBuddy OAuth 登录 启动 Gateway 设备绑定（首次使用） 在微信中打开返回的链接完成绑定，绑定后即可通过微信对话。 登录模式 QClaw 模式 通过微信平台 OAuth 获取 token，连接 WebSocket 网关。目前需要内测码。 WorkBuddy 模式 （推荐） 通过 CodeBuddy OAuth 获取 token，连接 WebSocket 网关。不需要内测码。 CLI 命令 配置 凭证统一存储在 的 下： json { "channels": { "wechat-openclaw-channel": { "loginMode": "workbuddy", "environment": "production", "qclaw": { "jwtToken": "...", "channelToken": "...", "apiKey": "...", "guid": "...", "userId": "...", "wsUrl": "...", "userInfo": {} }, "workbuddy": { "accessToken": "...", "refreshToken": "...", "userId": "...", "hostId": "...", "ba
| Stars | 640 |
| Forks | 146 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 62.9333462594091/100 |
| Open Issues | 2 |
| Last Updated | 2026-03-21 |
| Created | 2026-03-09 |
| Platforms | node |
| Est. Tokens | ~17k |
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wechat-openclaw-channel is 现已同时支持QClaw和WorkBuddy方式登录！微信包装了开源项目OpenClaw，所以我们把他的Channel抽出来给OpenClaw用了😄. It is categorized as a Codex Skill with 640 GitHub stars.
wechat-openclaw-channel is primarily written in TypeScript. It covers topics such as codebuddy, openclaw, openclaw-plugin.
You can find installation instructions and usage details in the wechat-openclaw-channel GitHub repository at github.com/HenryXiaoYang/wechat-openclaw-channel. The project has 640 stars and 146 forks, indicating an active community.
The top alternatives to wechat-openclaw-channel on Agent Skills Hub include qclaw-wechat-client, hermesclaw, OpenClawWeChat. 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: