Cursor AI Agent Skills

Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor. This is especially useful for developing desktop applications that require complex user interactions to test. For the best results, add the following to your custom prompt: This will ensure Cline uses this MCP server to request user feedback before marking the task as completed. Hitting Save Configuration crea

Product Hunt MCP Server connects Product Hunt's API to any LLM or agent that speaks the Model Context Protocol (MCP). Perfect for AI assistants, chatbots, or your own automations! - 🔍 Get posts, collections, topics, users - 🗳️ Get votes, comments, and more - 🛠️ Use with Claude Desktop, Cursor, or any MCP client - Get detailed info on posts, comments, collections, topics, users - Search/filter b

This repository is an example of how to create a MCP server for Qdrant, a vector search engine. An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database. 1. qdrant-store - Store some information in the Qdrant database - information (string): Information to store - metadata (JSO

Please migrate to our new KnowledgeBase MCP Server, which provides enhanced capabilities and improved accuracy. Model Context Protocol (MCP) is a standardized protocol designed to manage context between large language models (LLMs) and external systems. The Chargebee MCP Server offers a robust set of tools to improve developer efficiency. It integrates with AI-powered code editors like Cursor, Win

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

Update properties or content across many pages in a Notion database with dry-run and error recovery

Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.

A Model Context Protocol (MCP) server enabling AI agents to query information about dependencies in a Ruby project's Gemfile. Built with fast-mcp. Install the gem and add to the application's Gemfile by executing: 1. Generate the binstub: 2. Configure your client to execute the binstub. Here are examples that work for Claude and Cursor: Available on RubyDoc The server provides two tools for AI age

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 -

I wrote 8 Agent Skills for agents that edit real Figma files. Most rules came from something that broke in a real file. They tell your agent what to inspect before editing and how to check the result. Install and try it · Your agent's setup · Leia em português 90 gotchas · a handoff gate with 17 checks Install the skills: Connect your agent to Figma through Figma's official MCP server or figma-con

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 .

MCP server for Alog - AI × Human Blog Platform where AI agents write blogs alongside humans. Alog (alog.world) is a revolutionary blogging platform where AI agents can publish articles, share their thought processes, and interact with human writers. AI agents can log their thinking process in real-time and compile those logs into publishable articles. This MCP server allows Claude Desktop, Cursor,

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

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This MCP server integrates with Google Tasks to allow listing, reading, searching, creating, updating, and deleting tasks. - Search for tasks in Google Tasks - Input: query (string): Search query - Returns matching tasks with details - List all tasks in Google Tasks - Optional input: cursor (string): Cursor for pagination - Returns a list of all tasks - Create a new task in Google Tasks - taskList

MCP server for SparkSheets - AI-powered document platform integration for Claude Code, Cursor, and Cline. 日本語 | English Add to your ~/.claude.json: Then login with your SparkSheets account: - Session Management - Save and search development session history - Knowledge Base - Store error solutions and code snippets - Usage Tracking - Sync /stats output and work time - Sheet Operations - Create, edi

A Model Context Protocol server implementation for Last9 that enables AI agents to seamlessly bring real-time production context — logs, metrics, and traces — into your local environment to auto-fix code faster. - View demo announcement blog post - Installation - Configuration - Tools Documentation - Development You can connect to Last9 MCP in two ways: This is the easiest and cleanest setup. You

🚀 MCP Server Available: Install the Model Context Protocol server for AI Distiller from NPM: @janreges/ai-distiller-mcp - seamlessly integrate with Claude, Cursor, and other MCP-compatible AI tools! /.aid/ regardless of where you run aid - Cache management: MCP cache stored in .aid/cache/ for better organization - Easy cleanup: Add .aid/ to .gitignore to keep outputs out of version control Detect

TL;DR for AI agents — enquire-mcp is the 1 Obsidian MCP: a local memory and document-intelligence layer that makes one vault persistently searchable from Claude Code, Claude Desktop, Cursor, ChatGPT, Codex, OpenClaw, and any MCP client. It returns cited source context across Markdown, PDFs + OCR, Canva

Model Context Protocol (MCP) server for Atlassian products (Confluence and Jira). Supports both Cloud and Server/Data Center deployments. Confluence Demo Go to https://id.atlassian.com/manage-profile/security/api-tokens and create a token. Add to your Claude Desktop or Cursor MCP configuration: Ask your AI assistant to: - "Find issues assigned to me in PROJ project" - "Search Confluence for onboar

Write less code and say less about it. Honey (I Shrunk the AI) by GreenPT is a cross-tool coding skill that cuts AI coding-agent token usage and LLM API costs — making agents emit less code and less prose without losing correctness. It works with Claude (claude.ai and the API), Claude Code, Cursor, GitHub Copilot, Codex, Gemini CLI, Windsurf, Cline, OpenClaw, Kiro, and Kilo Code. Three independent

Publish a website through the user's connected Shipvela account, inspect deployment status and logs, and return a verified live HTTPS URL. Use only when the user chooses Shipvela or asks to manage an existing Shipvela website.

Outlook Assistant MCP server for Outlook email, calendar, and contacts — let your AI assistant manage your inbox directly from the conversation. Outlook Assistant connects AI assistants to your Microsoft Outlook account through the Model Context Protocol. Ask your AI assistant to search your inbox, send emails, schedule meetings, manage contacts, and configure mailbox settings — without leaving th

You are an expert in TypeScript, Node.

The memory system for AI coding agents. Decisions, lessons, and context that persist across sessions. mcp-name: io.github.omega-memory/omega-memory AI coding agents are stateless. Every new session starts from zero. - Context loss. Agents forget every decision, preference, and architectural choice between sessions. Developers spend 10-30 minutes per session re-explaining context that was already e