GitHub AI Agent Skills

You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C

An AI coding assistant built in Rust that provides both command-line and graphical interfaces for autonomous code analysis and modification. Multi-Modal Tool Execution: Adapts to different LLM capabilities with pluggable tool invocation modes - native function calling, XML-style tags, and triple-caret blocks - ensuring compatibility across various AI providers. Real-Time Streaming Interface: Advan

English Version 一个用于与语雀 API 集成的 Model-Context-Protocol (MCP) 服务器。此实现受 Figma-Context-MCP 的启发，并使用 语雀开放 API。 该服务器提供了与语雀知识库平台交互的 MCP 工具，允许 AI 模型： - 获取用户和文档信息 - 创建、读取、更新和删除文档 - 获取统计数据和分析信息 要使用 Smithery 将 Yuque MCP Server 自动安装到 Claude 桌面端： - Node.js 18+ (推荐) - 拥有 API 令牌的语雀账号 3. 基于 .env.example 创建 .env 文件： 4. (可选) 在 .env 文件中添加你的语雀 API 令牌： 你也可以选择在连接到服务器时通过查询参数提供令牌，而不是在 .env 文件中设置。 然后在 HTTP 或 CLI 模式下运行： 本项

- Requirements - Entity Operations - Telemetry Management - Relations - OTA Packages - Entity Data Query - Quick Start Guide - Installation - Docker Image - Build from Sources - Client Configuration - Binary Configuration - Docker Configuration - Environment Variables - Tool Groups Configuration - Available Tools - Device Tools - Asset Tools - Customer Tools - User Tools - Alarm Tools - OTA Tools

Dependency intelligence infrastructure for AI assistants and agents working with JVM projects. Traditional upgrade workflow: 1. Google "Spring Boot 2.7 to 3.2 migration guide" (5 minutes) 2. Read 50-page migration docs, cross-reference breaking changes (15 minutes) 3. Check Maven Central for compatible dependency versions (5 minutes) 4. Update pom.xml, compile, fix breaking changes (30 minutes) 5.

This is an MCP server that analyzes the screen with OmniParser and automatically operates the GUI. Confirmed on Windows. This is MIT license, but Excluding submodules and sub packages. OmniParser's repository is CC-BY-4.0. Each OmniParser model has a different license (reference). 1. Please do the following: (Other than Windows, use export instead of set.) (If you want langchainexample.py to work,

[![MIT licensed][mit-badge]][mit-url] [![Python Version][python-badge]][python-url] [![PyPI version][pypi-badge]][pypi-url] [mit-badge]: https://img.shields.io/pypi/l/mcp.svg [mit-url]: https://github.com/reeeeemo/ancestry-mcp/blob/main/LICENSE [python-badge]: https://img.shields.io/pypi/pyversions/mcp.svg [python-url]: https://www.python.org/downloads/ [pypi-badge]: https://badge.fury.io/py/mcp-s

This project provides a Model Context Protocol (MCP) server wrapper around the esp-rainmaker-cli Python library. It allows MCP-compatible clients (like LLMs or applications such as Cursor, Claude Desktop, and Windsurf) to interact with your ESP RainMaker devices using the official CLI. The Model Context Protocol (MCP) is a standardized framework that enables AI systems to interact with external to

Download the MCPBundles Hub .mcpb file directly: Download hub.mcpb This .mcpb package contains a pre-configured MCP server proxy that connects to the MCPBundles Hub endpoint. Once installed, your AI assistant can discover and execute tools from all your enabled bundles. MCP Bundles is the simplest way to connect AI assistants to real-world tools. Instead of configuring hundreds of individual MCP s

Build security Blue Books for sensitive apps

MCP (Model Context Protocol) server for SynergyAge database This server implements the Model Context Protocol (MCP) for SynergyAge, providing a standardized interface for accessing synergistic genetic intervention and aging research data. MCP enables AI assistants and agents to query comprehensive longevity datasets through structured interfaces. The server automatically downloads the latest Syner

Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.

An MCP server that exposes PraisonAI AI agents and tools for use with Claude Desktop, Cursor, VS Code, Windsurf, and other MCP clients. - 🤖 AI Agents as Tools - Run PraisonAI agents directly from MCP - 🔄 Workflow Orchestration - Multi-step agent workflows - 🛠️ 64 Built-in Tools - Complete coverage of all PraisonAI features - ⚡ Easy Setup - Works with uvx or pip install The core tools for runnin

Terraform infrastructure as code best practices

Cyclops MCP allows your favorite AI agent to manage your Kubernetes applications. Cyclops MCP servers provide tools for agents to create and update existing applications safely. This means it can check all of your existing templates and the schema of those templates to create accurate and production-ready applications. Your agent now has much less room to make a misconfiguration since it creates h

Golang-based MCP server connecting to Kubernetes Features ⚙ Browse With Inspector ⚙ Use With Claude ⚙ Contributing ↗ ⚙ About MCP ↗ MCP 💬 prompt 🗂️ resource 🤖 tool - 🗂️🤖 List Kubernetes contexts - 💬🤖 List Kubernetes namespaces - 🤖 List, get, create and modify any Kubernetes resources - includes custom mappings for resources like pods, services, deployments - 🤖 List Kubernetes nodes - 💬 Li

MCP Server that can connect to a Kubernetes cluster and manage it. Supports loading kubeconfig from multiple sources in priority order. Before using this MCP server with any tool, make sure you have: 1. kubectl installed and in your PATH 2. A valid kubeconfig file with contexts configured 3. Access to a Kubernetes cluster configured for kubectl (e.g. minikube, Rancher Desktop, GKE, etc.) 4. Helm v

Guides and best practices for working with Neon Serverless Postgres. Covers getting started, local development with Neon, choosing a connection method, Neon features, authentication (@neondatabase/auth), PostgREST-style data API (@neondatabase/neon-js), Neon CLI, and Neon's Platform API/SDKs. Use for any Neon-related questions.

Give AI assistants the power to query your Prometheus metrics. A [Model Context Protocol][mcp] (MCP) server that provides access to your Prometheus metrics and queries through standardized MCP interfaces, allowing AI assistants to execute PromQL queries and analyze your metrics data. [mcp]: https://modelcontextprotocol.io - Prometheus server accessible from your environment - MCP-compatible client

Browse, read, and interact with web pages using PageMap MCP tools. Covers navigation, element interaction, form filling, and page state management.

Lightweight logging & observability for agent work across clients (Codex, Claude, Cursor, ChatGPT, OpenClaw, and others). - One MCP tool (logwork) that aggregates work done across different agents for single or multiple users. - Define your own logging structure with custom schemas (or use built-in profiles) once, and the MCP schema will guide clients to log with correct payloads. - This lets team

A CLI inspector for the Model Context Protocol - Run MCP servers from various sources - List Tools, Resources, Prompts - Call Tools, Read Resources, Read Prompts - OAuth support for SSE and Streamable HTTP servers This will use the config file of Claude Desktop. The config file has the same format as the Claude Desktop config file. Run a specific tool, resource, or prompt without interactive promp

MCP (Model Context Protocol) server for Biothings.io This server implements the Model Context Protocol (MCP) for BioThings, providing a standardized interface for accessing and manipulating biomedical data. MCP enables AI assistants and agents to access specialized biomedical knowledge through structured interfaces to authoritative data sources. Supported BioThings data sources include: - mygene.i

AI-powered debugging and inspection for Kubernetes clusters using Inspektor Gadget. - AI-powered interface for Kubernetes troubleshooting and monitoring - One-click Inspektor Gadget deployment and removal - Intelligent output summarization and analysis - Automatic gadget discovery from Artifact Hub 1. Ensure you have Docker and a valid kubeconfig file 2. Configure the MCP server in VS Code (see IN