Cursor 安装指南
常与开发类 MCP Server 搭配使用的 AI 代码编辑器。
MCP Servers
A code and document intelligence server that indexes Git repositories and provides search through MCP and REST APIs.
An MCP server application that sends various types of messages to the WeCom group robot.
MCP-сервер для Google Gemini — генерация и редактирование изображений, чат и анализ файлов через браузерные cookies. Без API-ключей. Бесплатно.
MCP server for build123d to improve AI cognition when creating 3D CAD models
Maybe the headless Ghidra MCP you are looking for.
VV knowledge layer for AI agents — MCP server exposing Visualize Value frameworks, voice, and content
Let the scripts your coding agent writes call your connected MCP servers (no extra auth)
This is a community-maintained project. — This project is under active development and not intended for production use. APIs, tools, and behavior may change without notice.
MCP Serverの設定（Cursor、Claude Desktop）を参考に、mcp.jsonまたはclaude_desktop_config.jsonに以下を追記してください。 LAPRAS_API_KEYは職歴関連のツールを使う場合のみ必要です。https://lapras.com/config/api-key から取得できます。
An MCP server for gitingest. This allows MCP clients like Claude Desktop, Cline, Cursor, etc to quickly extract information about Github repositories including
Local-first multi-runtime AI cockpit (CLI + desktop + MCP). War-rooms, review receipts, open multi-model coding benchmarks. MIT.
An MCP server that exposes UniFi application API documentation (Network, Protect, Site Manager) as queryable tools for Claude Desktop, Claude Code (VS Code / JetBrains), or any MCP-compatible client.
Agent Skills
This project is under active development and evolving rapidly. Contributions, feedback, and discussions are warmly welcome.
Agent skill that audits vibe-coded apps for common security vulnerabilities introduced by AI coding assistants
A high-performance Markdown parser and renderer for Angular, React, Svelte, Vue, HTML and ANSI.
A collection of Codex/agent skills for planning, documentation access, frontend development, and browser automation.
简单说，就是跟 Claude 说一句话，它会帮我从每天的新论文里筛一轮，挑出值得看的，再把重点论文读完、写成 Obsidian 笔记。日常不用记一堆命令，基本就是：