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awesome-devops-mcp-servers: A Curated List of DevOps-Focused MCP Servers
Discover awesome-devops-mcp-servers, a comprehensive GitHub repository featuring a curated list of Model Context Protocol (MCP) servers tailored for DevOps tools and capabilities. This resource enables AI models to securely interact with a wide range of local and remote resources, enhancing automation and intelligence in DevOps workflows. Explore servers for infrastructure as code, container orchestration, cloud providers, security, and more.
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awesome-devops-mcp-servers: A Curated List of DevOps-Focused MCP Servers
Discover awesome-devops-mcp-servers, a comprehensive GitHub repository featuring a curated list of Model Context Protocol (MCP) servers tailored for DevOps tools and capabilities. This resource enables AI models to securely interact with a wide range of local and remote resources, enhancing automation and intelligence in DevOps workflows. Explore servers for infrastructure as code, container orchestration, cloud providers, security, and more.
awesome-a2a: A Curated List of Agent2Agent (A2A) Resources
The awesome-a2a repository is a comprehensive, curated list of Agent2Agent (A2A) protocol servers, clients, tools, and frameworks. It serves as a central hub for developers looking to explore and build interoperable AI agent systems. This resource helps in discovering various A2A-compliant implementations and related utilities.
Micstec Skills: A Comprehensive Collection for AI Coding Agents
Micstec Skills is a centralized GitHub repository offering a vast collection of skills, plugins, and extensions for various AI coding agents. It supports platforms like Claude Code, Gemini CLI, Copilot, and Cursor, providing a unified approach to managing AI agent capabilities. Developers can easily discover, install, and enhance their AI-powered development workflows across multiple environments.
product-manager-skills: AI-Powered PM Assistant for Coding Tools
The product-manager-skills repository transforms AI coding tools like Claude Code, Codex, Cursor, and Windsurf into a rigorous product manager. It enables AI to diagnose SaaS metrics, critique PRDs, plan roadmaps, run discovery, and provide career coaching for product professionals. This skill offers a structured, framework-driven approach to product management workflows, moving beyond generic AI responses.
Product-Manager-Skills: AI-Powered Framework for Product Management Excellence
Product-Manager-Skills is a comprehensive GitHub repository offering a framework of 77 battle-tested product management skills. Designed for AI agents like Claude, Codex, and ChatGPT, it helps product managers and their AI counterparts achieve professional-level work. This resource aims to provide both functional tools and pedagogic insights into effective product management practices.
Awesome Agent Skills: A Curated Directory for AI Coding Agents
Awesome Agent Skills is a comprehensive directory of skills, tools, and plugins designed for AI coding agents. It stands out by covering various agent skill ecosystems, including Agent Skills (SKILL.md), MCP servers, Cursor rules, and more, all in one centralized location. This resource helps developers and AI enthusiasts discover and implement capabilities across different AI agent platforms.
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PyQtGraph: Fast Data Visualization and GUI Tools for Scientific Applications
PyQtGraph is a powerful, pure-Python graphics library tailored for scientific and engineering applications. It provides fast data visualization and GUI tools, leveraging NumPy for numerical processing, Qt's GraphicsView for 2D, and OpenGL for 3D displays. This makes it an excellent choice for high-performance data plotting and interactive interfaces.
RAGChecker: A Fine-grained Framework for Diagnosing RAG Systems
RAGChecker is an advanced automatic evaluation framework developed by Amazon Science, specifically designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It offers a comprehensive suite of metrics and tools for in-depth analysis of RAG performance. This framework empowers developers and researchers to thoroughly evaluate and enhance their RAG systems with precision.
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