GitHub AI Agent Skills

A Model Context Protocol server for document format conversion using pandoc. This server provides tools to transform content between different document formats while preserving formatting and structure. Please note that mcp-pandoc is currently in early development. PDF support is under development, and the functionality and available tools are subject to change and expansion as we continue to impr

Main entry-point skill for Transloadit. Route to the right `integrate-*`, `transform-*`, or `docs-*` skill, and prefer executing via `npx -y @transloadit/node ...` (CLI) for deterministic behavior.

Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates.

audit-prompt-caching is a portable Codex/agent skill for finding why LLM cache reuse fails across the request path: prompt/prefix caches, provider cache telemetry, cache-aware routing, agent tool stability, Bedrock checkpoints, OpenRouter routing drift, provider migration risk, and vLLM/SGLang KV reuse. LLM cache reuse usually fails silently. A timestamp in the system prompt, shuffled tool schemas

The MCPR (Model Context Protocol Tools for R) package addresses a fundamental limitation in the current paradigm of AI-assisted R programming. Existing AI agents operate in a stateless execution model, invoking Rscript for each command, which is antithetical to the iterative, state-dependent nature of serious data analysis. An analytical workflow is a cumulative process of exploration, modelling,

Publish articles to X/Twitter

This is a Model Context Protocol (MCP) server designed specifically for AI agents and LLM applications. It acts as a bridge between artificial intelligence systems and the comprehensive OpenStreetMap tagging knowledge base provided by the official @openstreetmap/id-tagging-schema library. Current Status: Production-ready MCP server, actively maintained and continuously improved. The service is dep

This is the MCP server of atest. Please start the MCP server with atest gRPC port. or start in Docker or start in npx You can also set the MCP server mode with the --mode flag. or as stdio mode: You can build the binary with the following command: You can build .dxt package with the following command: You can publish the package with the following command:

Production-Ready Model Context Protocol (MCP) server that enables AI assistants to fully interact with console applications, monitor output, detect errors, and automate terminal workflows - similar to how Playwright works for web browsers. This server is fully production-ready with: - ✅ No native compilation required (removed node-pty dependency) - ✅ Full cross-platform support (Windows, macOS, Li

A Golang-based Model Context Protocol (MCP) server implementation for Prometheus that enables natural language interactions with Prometheus metrics and queries. Built with Go, prometheus-mcp-server provides a robust, type-safe interface that maintains full consistency with the Prometheus HTTP API, allowing you to query and manage your Prometheus instance through natural language conversations with

if macbook or linux If the network is slow, you can set up a domestic mirror source. Method 1: On the setting page of cherry-studio, click on the MCP server, then click "Add Server", and subsequently configure it on the page. Method 2: Use the configuration parameters

A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously. - Parallel Searching: Supports searching with multiple keywords on Google simultaneously, improving search efficiency - Browser Optimization: Opens multiple tabs in a single browser instance for efficient parallel searching - Automatic Verification Handling: Intelligently detects CAPTCHA

下面是为你的 bilibili-mcp 工具生成的 README.md 示例： 一个用于获取 B 站热榜的 MCP 工具，基于 FastMCP 和 B 站公开接口。你可以通过注册到 Cursor 或其他 MCP 客户端，实时调用热门视频数据。 确保使用 Python ≥ 3.12，并安装以下依赖项： 你的主代码文件可以是 bilibilimcp.py，项目结构如下： 使用 uv 运行工具： FastMCP 会自动暴露一个名为 getpopular 的工具： 获取 B 站热榜视频信息，返回最多 topk 个条目（最大 10 条）。 - 使用 httpx 实现异步请求 - 通过 FastMCP 注册为标准 MCP 工具 - 支持通过 stdio 与 MCP 客户端通信（例如 Cursor） - 仅用于学习与研究，B 站接口为公开接口，使用时请避免过于频繁访问。 - 默认 User-Agent

iptables for MCP. Blocks dangerous tool calls, scans for secret leakage, logs everything. No AI, no cloud, pure rules. Sits between your AI coding tool (Claude Code, Cursor, Windsurf) and MCP servers, intercepting every JSON-RPC message and enforcing YAML-defined policies. MCP servers have full access to your filesystem, shell, databases, and APIs. When an AI agent calls tools/call, the server exe

mcp-name: io.github.wmarceau/rideshare-comparison Compare Uber and Lyft prices for any route using Claude Desktop. Add to your claudedesktopconfig.json: Compare Uber and Lyft prices for a specific route. Example prompt: "Compare Uber and Lyft prices from SFO airport to downtown San Francisco" Get a list of cities with rideshare pricing data. Example prompt: "Which cities are supported for rideshar

👋 A way to let LLMs speak Just A production-ready MCP server that provides seamless integration with Just command runner, enabling AI assistants to discover, execute, and introspect Justfile recipes through the standardized MCP protocol. If it isn't immediately obvious, the benefit of having LLMs use Just vs. bash is that running Just commands (via MCP) provides a context-saving abstraction where

Generate Apache ECharts with AI MCP dynamically for chart generation and data analysis. Also you can use mcp-server-chart to generate chart, graph, map. - Fully support all features and syntax of ECharts, include data, style, theme and so on. - Support exporting to png, svg, and option formats, with validation for ECharts to facilitate the model's multi-round output of correct syntax and graphics.

SmartDB is a universal database gateway that implements the Model Context Protocol (MCP) server interface. This gateway allows MCP-compatible clients to connect and explore different databases. Compared to similar products, SmartDB not only provides basic database connection and exploration capabilities but also adds advanced features such as OAuth 2.0 authentication , health checks, SQL optimizat

GXtract is a Model Context Protocol (MCP) server designed to integrate with VS Code and other compatible editors. It provides a suite of tools for interacting with the GroundX platform, enabling you to leverage its powerful document understanding capabilities directly within your development environment. - Architecture - Prerequisites - Installing UV - Quick Start: VS Code Integration - Available

MCP (Model Context Protocol) server for the gget bioinformatics library. This server implements the Model Context Protocol (MCP) for gget, providing a standardized interface for accessing powerful bioinformatics tools and databases. MCP enables AI assistants and agents to perform complex genomics queries through structured interfaces. The gget bioinformatics toolkit provides efficient querying of

AI-powered image editing with style transfer and object removal

OpenZIM MCP Server Transform static ZIM archives into dynamic knowledge engines for AI models OpenZIM MCP transforms static ZIM archives into dynamic knowledge engines for Large Language Models. Unlike basic file readers, this tool provides intelligent, structured access that LLMs need to effectively navigate and understand vast knowledge repositories. Why LLMs Love OpenZIM MCP: - Smart Navigation

An MCP server that empowers AI agents to inspect any wallet’s balance and onchain activity across major EVM chains and Solana chain. - Multi-Chain Support: Queries Solana, Ethereum, Polygon, Binance Smart Chain (BSC), Base, Arbitrum and more. - Flexible Output: Balances in ASCII tables, activities and transactions in structured text. - Python: Version 3.10 or higher. - Dune SIM API Key: Obtain fro

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.