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 wq19901103wq · Agent Tool · ★ 113
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wechat-mac-rpa safe to install? View the security audit →
WeChat Mac RPA 中文 | English 让 AI 像人一样"看"着微信界面，自动回复消息。默认 OCR / SmartPipeline 模式不碰协议、不读数据库、不注入代码；可选 WeFlow 模式会读取用户授权的本地数据库副本，用于初始化历史记忆。 基于多模态视觉感知与LLM Agent的 macOS 微信自动化框架。默认模式不是协议逆向或 Hook，而是把微信当作纯黑盒 GUI 应用：用计算机视觉读取界面，用大语言模型理解对话，用系统级自动化操作界面。 核心设计：感知 → 推理 → 行动 → 记忆 → 数据飞轮，五个子系统构成完整的认知闭环。每一次认知循环的完整链路都被结构化日志逐条记录，形成可追溯、可回归、可量化的生产质量资产。 快速开始 环境：macOS 12+，Python 3.10+，微信 Mac 版 依赖： 配置：复制 为 ，填入 API Key；复制 为 ，填写本地人设 隐私： 与 默认不进入 Git；不要把 API Key、聊天记录或真实人设提交到仓库 权限：Bot 依赖 macOS 辅助功能/屏幕录制权限，详见下文权限说明 启动： 管理后台（LaunchAgent 常驻）： bash # 首次加载（用户登录时自动启动，崩溃自动重启） launchctl bootstrap gui/$(id -u) /Library
| Stars | 113 |
| Forks | 33 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.0209455497516/100 |
| Open Issues | 5 |
| Last Updated | 2026-10-03 |
| Created | 2026-05-02 |
| Platforms | python |
| Est. Tokens | ~17k |
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wechat-mac-rpa is 基于多模态视觉感知与 LLM Agent 的 macOS 微信自动化框架 | Visual RPA for WeChat. It is categorized as a Agent Tool with 113 GitHub stars.
wechat-mac-rpa is primarily written in Python. It covers topics such as agent, ai-agent, ai-automation.
You can find installation instructions and usage details in the wechat-mac-rpa GitHub repository at github.com/wq19901103wq/wechat-mac-rpa. The project has 113 stars and 33 forks, indicating an active community.
wechat-mac-rpa is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to wechat-mac-rpa on Agent Skills Hub include CodexBridge, clawdcursor, wechat-rpa-bot-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: