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
Native macOS client for Model Context Protocol (MCP)
Build, visualize, and run intelligent AI workflows without writing code. Drag-and-drop canvas · LLM & Agent nodes · RAG pipelines · Multi-agent orchestration · MCP support
Easily spin up an MCP Server on Next.js, Nuxt, Svelte, and more
Easily spin up an MCP Server on Next.js, Nuxt, Svelte, and more
A tool that converts OpenAPI specifications to MCP server
You use Claude for some things, ChatGPT for others, and Cursor for code. But your context, including your projects, decisions, and preferences, does not move with you.
现代化资产测绘与漏洞监控平台 (ARL-Next)。经典 ARL 架构重构，聚焦企业资产关联、异步解耦并发调度与原生 MCP 协议集成，容器化开箱部署。
An Mcp client inside Emacs
Intelligent token optimization through caching, compression, and smart tooling for Claude Code and Claude Desktop
30+ collectors, real-time alerts, built-in MCP server for AI analysis. Nothing phones home. Your data stays on your server and your machine.
Open-source, multi-platform harness for AI agents - a native, multi-threaded operator's console (JVM, not Electron) to run Claude Code, Codex, Gemini or OpenCode with a real browser, terminal,...
A MCP (Model Context Protocol) server for interacting with dbt.
Agent Skills
Shiny for Python is the best way to build fast, beautiful web applications in Python. You can build quickly with Shiny and create simple interactive visualizations and prototype applications in an...
One Python API: store media, run models, index embeddings, serve endpoints, and version everything in a single system instead of gluing together blob storage, a vector DB, an orchestrator, and edge...
Adala is an **utonomous **ta (**abeling) **gent framework.
Beautiful UI components built for Better Auth.
KAI Scheduler is a robust, efficient, and scalable Kubernetes scheduler that optimizes GPU resource allocation for AI and machine learning workloads.
Your AI skills and agents, finally organized.
Model export recipes, Python primitives, and Swift runtime utilities for building on-device AI with Core AI.
本项目是基于吴恩达老师在 DeepLearning.AI 平台推出的 agent-skills-with-anthropic 系列课程的中文学习资料整理项目。我们致力于为中文学习者提供高质量的课程内容翻译、系统的知识点梳理以及详细的示例代码解读，帮助大家更轻松地掌握 Agent Skills。
A collection of AI agent skills focused on resume optimization, job applications, and career development.
For more advanced capabilities and end-to-end machine learning, visit www.k-dense.ai.
upfetch is an advanced fetch client builder with standard schema validation, automatic response parsing, smart defaults and more.
📚 Check out the comprehensive docs with detailed guides, examples, and best practices!