by fastclaw-ai · Codex Skill · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is weclaw safe to install? View the security audit →
WeClaw 中文文档 WeChat AI Agent Bridge — connect WeChat to AI agents (Claude, Codex, Gemini, Kimi, etc.). This project is inspired by @tencent-weixin/openclaw-weixin. For personal learning only, not for commercial use. Quick Start That's it. On first start, WeClaw will: Show a QR code — scan with WeChat to login Auto-detect installed AI agents (Claude, Codex, Gemini, etc.) Save config to Start receiving and replying to WeChat messages Use to add additional WeChat accounts. Other install methods How It Works Agent modes: | CLI
go install github.com/fastclaw-ai/weclaw@latest
docker run -it -v ~/.weclaw:/root/.weclaw ghcr.io/fastclaw-ai/weclaw startTaken from the project's README on GitHub. Check the security audit before running it.
Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 1,626 |
| Forks | 209 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 72.6879158052754/100 |
| Open Issues | 45 |
| Last Updated | 2026-04-01 |
| Created | 2026-03-22 |
| Platforms | go |
| Est. Tokens | ~118k |
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weclaw is Connect to any agents with WeChat ClawBot.. It is categorized as a Codex Skill with 1.6k GitHub stars.
weclaw is primarily written in Go. It covers topics such as clawbot, openclaw, openclaw-weixin.
The README on GitHub (github.com/fastclaw-ai/weclaw) gives: go install github.com/fastclaw-ai/weclaw@latest , then docker run -it -v ~/.weclaw:/root/.weclaw ghcr.io/fastclaw-ai/weclaw start. Usage details are in the same README.
weclaw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to weclaw on Agent Skills Hub include nexu, openclaw-china, clawbot. 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: