Cursor AI Agent Skills

Enable MCP features for any Gin API with a line of code. Gin-MCP is an opinionated, zero-configuration library that automatically exposes your existing Gin endpoints as Model Context Protocol (MCP) tools, making them instantly usable by MCP-compatible clients like Cursor , Claude Desktop , Continue , Zed , and other MCP-enabled tools. Our philosophy is simple: minimal setup, maximum productivity .

501 algorithmic trading skills · 16 engineering domains · 5 regulatory & exchange frameworks · 501 working reference implementations backed by 20,291 unit tests · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI and any tool that reads SKILL.md · Apache 2.0 Get Started · What's Inside · How It's Verified · Frameworks & Standards · Platforms · Contribu

MCP (Model Context Protocol) server that brings deterministic tech stack recommendations to LLM clients like Claude, Cursor, Windsurf, and other MCP-compatible tools. Try it free — 8 tools work without an account, including a daily demo recommendation and MCP project kit. Add to ~/Library/Application Support/Claude/claudedesktopconfig.json (macOS) or %APPDATA%\Claude\claudedesktopconfig.json (Wind

RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers. - Ingests the official MCP Registry, enriches records for RAG use-cases, and serves a subregistry API. - Exposes an MCP server (remote Streamable HTTP + local stdio) so agents can search/filter RAG MCP servers. MapRag is a discovery + routing layer for retrieval. It helps agents and humans

Drive four external coding CLIs — Google's Antigravity (Gemini 3.6 Flash), OpenAI Codex, the GitHub Copilot CLI, and Cursor — as sub-agents inside Claude Code. Text answers, image generation, real repo work, and parallel swarms, on quota you already pay for. One MCP server, four backends. It exposes Google Antigravity, OpenAI Codex, the GitHub Copilot CLI, and Cursor to Claude Code as clean MCP to

Agent Interviews is a cutting-edge AI interview-as-a-service platform that helps companies streamline their technical assessment process. Our platform allows you to: - Create AI interviewers tailored to specific roles and skills - Conduct consistent, bias-reduced technical interviews at scale - Collect structured data on candidate performance - Save engineering time while maintaining high hiring s

Xian Smart Contract Development - Cursor Rules

MCP Unity is an implementation of the Model Context Protocol for Unity Editor, allowing AI assistants to interact with your Unity projects. This package provides a bridge between Unity and a Node.js server that implements the MCP protocol, enabling AI agents like Cursor, Windsurf, Claude Code, Codex CLI, GitHub Copilot, and Google Antigravity to execute operations within the Unity Editor. MCP Unit

Daisys-mcp is a beta version and doesn't have a stable release yet. But you can try it out by doing the following: 1. Get an account on Daisys and create an username and password. If you run on mac os run the following command: If you run on linux run the following command: 2. Add the following configuration to the mcp config file in your MCP client (Claude Desktop, Cursor, mcp-cli, mcp-vscode, et

Firebase MCP enables AI assistants to work directly with Firebase services, including: - Firestore: Document database operations - Storage: File management with robust upload capabilities - Authentication: User management and verification The server works with MCP client applicatios such as Claude Desktop, Augment Code, VS Code, and Cursor. - Firebase project with service account credentials - Nod

日本語版 README An MCP (Model Context Protocol) server for searching, installing, and managing AI Agent Skills. Works with MCP-compatible clients like Claude Desktop, Cursor, and VS Code. CC BY-NC-SA 4.0 — see LICENSE.

A comprehensive Model Context Protocol (MCP) server that provides Claude Desktop and other MCP clients with tools for Kafka Schema Registry operations. Features advanced schema context support, multi-registry management, and comprehensive schema export capabilities. Latest Version: v2.1.5 | Docker: aywengo/kafka-schema-reg-mcp:stable - 🚀 Quick Start - ✨ Key Features - 🛠️ Claude Code Skills - 📦

Live handbook: https://girijashankarj.github.io/cursor-handbook/ The open-source rules engine for Cursor IDE — 117 components (rules, agents, skills, commands, hooks) that turn your AI into a senior engineer who follows your standards, knows your codebase, and never wastes a token. If cursor-handbook helps you, consider giving it a ⭐ — it helps others discover it. - Ways to use - The Problem -

English | 简体中文 🧠 The data layer for AI systems. Skill Seekers turns any documentation, GitHub repo, or PDF into structured knowledge assets—ready to power AI Skills (Claude, Gemini, OpenAI), RAG pipelines (LangChain, LlamaIndex, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline) in minutes, not hours. Skill Seekers is the universal preprocessing layer that sits between raw documentatio

This is a Model Context Protocol (MCP) server that integrates AgentQL's data extraction capabilities. - extract-web-data - extract structured data from a given 'url', using 'prompt' as a description of actual data and its fields to extract. To use AgentQL MCP Server to extract data from web pages, you need to install it via npm, get an API key from our Dev Portal, and configure it in your favorite

An MCP server that enables AI agents to control web browsers using browser-use. - uv - Fast Python package manager - Playwright - Browser automation - mcp-proxy - Required for stdio mode Create a .env file: - [x] Browser Automation: Control browsers through AI agents - [x] Dual Transport: Support for both SSE and stdio protocols - [x] VNC Streaming: Watch browser automation in real-time - [x] Asyn

mem0-mcp-server wraps the official Mem0 Memory API as a Model Context Protocol (MCP) server so any MCP-compatible client (Claude Desktop, Cursor, custom agents) can add, search, update, and delete long-term memories. The server exposes the following tools to your LLM: All responses are JSON strings returned directly from the Mem0 API. There are three ways to use the Mem0 MCP Server: 1. Python Pack

A Model Context Protocol (MCP) server that provides gene set enrichment analysis using the Enrichr API. This server supports all available gene set libraries from Enrichr and returns only statistically significant results (corrected-$p$ File -> Settings and drag and drop the downloaded file into the Settings window. Use the button below to install the MCP server to Cursor, VS Code, or VS Code Insi

A/B testing and content experimentation methodology for data-driven content optimization. Use when implementing experiments, analyzing results, or building experimentation infrastructure.

MCP (Model Context Protocol) server for domain and URL security analysis powered by GridinSoft Inspector. This MCP server allows your AI agent (Claude, Cursor, etc.) to verify any website or link. It helps answer the critical question: "Can I trust this site?" - Domain Reputation - Check if a site is well-known, safe, or suspicious. - Phishing Detection - Identify fraudulent pages designed to stea

Official Agent Skills for the ProofCore Protocol. These skills grant autonomous AI agents (in Cursor, Windsurf, Claude Code, etc.) the procedural knowledge to mathematically notarize their outputs, audits, and decisions on the TON Blockchain using a strict Zero-Storage policy. To give your CLI agent (e.g., Claude Code) access to all ProofCore skills: These skills use the Model Context Protocol (MC

4 skills

A Model Context Protocol server that enables LLMs to interact with MongoDB databases. This server provides capabilities for inspecting collection schemas and executing MongoDB operations through a standardized interface. - Smart ObjectId Handling - Configurable auto/none/force modes for string-to-ObjectId conversion - Read-Only Mode - Protection against write operations, uses secondary read prefer

� Migrate to MCP Platform • 💬 Discord Community • � Legacy Docs Zero-configuration deployment of production-ready MCP servers with Docker containers, comprehensive CLI tools, and intelligent caching. Focus on AI integration, not infrastructure setup. That's it! Your MCP server is running at http://localhost:8080 Perfect for: AI developers, data scientists, DevOps teams building with MCP. Deploy M

All servers: 100 vendors, US region, individual plans, stateless, no auth, 100 req/min. Start local dev server: In a separate terminal: Use workflow endpoints when you want one request to return: - decision classification (yes | no | tie) from /api/decide - policy result from the relevant notary endpoint - recommended Zendesk action + tags + private note with requestid - POST https://refund.decide