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 skernelx · MCP Server · ★ 147
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MySearch-Proxy safe to install? View the security audit →
MySearch Proxy English Guide 是一套给 AI 助手准备的统一搜索栈。 它把原本分散的 4 件事收成了同一个仓库： - 真正可安装的 MySearch MCP - 给 Codex / Claude Code 的 skill 与安装说明 - 给 OpenClaw / ClawHub 的独立 skill bundle - 给团队或公开部署使用的控制台与代理层 支持的搜索能力： Tavily Firecrawl Exa 可选 X / Social 目标很简单： 让本地 AI 助手先用起来 让 OpenClaw 直接装上去 让团队共享一套统一搜索后端 让调用方尽量少关心底层 provider 差异 项目入口： GitHub： skernelx/MySearch-Proxy Docker Hub： skernelx/mysearch-proxy ClawHub： clawhub.ai/skernelx/mysearch 为什么做这个项目 很多搜索类项目只解决其中一小段： 只给一个 只会搜，不会抓正文 只会调官方 API，不方便接自建网关 只给 prompt，不给真正能安装的运行时 只做 key 面板，不解决 AI 如何调用 选择直接把整条链补齐： 推荐架构 当前最推荐的是 ： 这条路的好处很直接： 客户端只需要一组 Tavily / Firecrawl / Exa 不再散落到每台机器 可以统一管理 token、调用统计和额度同步 OpenClaw、本地 Codex、团队代理都能复用同一套配置 如果你暂时还没有 Proxy，也可以让 或 直接连官方 provider。 ##
| Stars | 147 |
| Forks | 26 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 62.2524173329328/100 |
| Open Issues | 4 |
| Last Updated | 2026-03-23 |
| Created | 2026-03-16 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~118k |
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MySearch-Proxy is Unified search MCP, proxy console, and skill for Tavily, Firecrawl, and Social / X.. It is categorized as a MCP Server with 147 GitHub stars.
MySearch-Proxy is primarily written in Python. It covers topics such as ai-search, claude-code, codex.
You can find installation instructions and usage details in the MySearch-Proxy GitHub repository at github.com/skernelx/MySearch-Proxy. The project has 147 stars and 26 forks, indicating an active community.
The top alternatives to MySearch-Proxy on Agent Skills Hub include mcp-omnisearch, Overture, kindly-web-search-mcp-server. 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: