Docker AI Agent Skills

An MCP (Model Context Protocol) server that provides token-efficient access to OpenAPI (v3.0) and Swagger (v2.0) specifications via MCP Resource Templates. The primary goal of this project is to allow MCP clients (like Cline or Claude Desktop) to explore the structure and details of large OpenAPI specifications without needing to load the entire file into an LLM's context window. It achieves this

A comprehensive security operations platform that integrates multiple security tools into a unified interface. This platform provides a centralized way to run various security scanning and testing tools. - Unified Interface: Single entry point for multiple security tools - Docker Support: Easy deployment using Docker - JSON Output: Consistent JSON output format across all tools - Error Handling: R

A portable, single-binary system auditing tool for Linux. Like Lynis but faster and smarter. No configuration needed. No dependencies. Just run. - 🔒 Security: Firewall, SSH hardening, SSL/TLS, fail2ban, SUID binaries, open ports - 🚀 Services: Systemd services, web servers, databases, Docker - 💻 Resources: CPU, RAM, disk usage, top processes - 💾 Storage: SMART health, inode usage, filesystem er

A high-performance, pure Rust Model Context Protocol (MCP) server that provides persistent, semantic, and graph-based memory for AI agents. Works perfectly with: Claude Desktop Claude Code (CLI) Cline / Roo Code Any other MCP-compliant client. Unlike other memory solutions that require a complex stack (Python + Vector DB + Graph DB), this project is a single, self-contained executable. ✅ No Extern

English | 中文 Zero-dependency MCP memory server for AI agents — persistent, searchable, local-first, single binary. - No infra to babysit. Single Go binary. No Docker, no Node.js runtime, no cloud account, no API keys. brew install in 30 seconds. - Memory stays with the project. Stored in .aimemo/ next to your code — commit it to git or add it to .gitignore. Switch branches; memory follows the dire

� 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

Open source feedback for teams that ship. The open-source alternative to Canny, UserVoice, and Productboard. Collect feedback. Prioritize what matters. Close the loop. Website · Docs · Get Started Cloud coming soon at quackback.io. Join the waitlist. Self-hosted anywhere with Docker or one click on Railway. Most feedback tools are expensive, closed-source, and lock you in. Quackback

Manage YouTube activities using the yutu CLI. Use this skill when you need to list channel activities such as uploads, likes, and favorites.

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

After setup, you can usually just say: The goal is that you only install this MCP server, and it handles the rest (searching servers, selecting servers, configuring servers, etc.). Choose one of the following: 1. Remote (simplest & fastest ⚡💨) 2. Local (prebuilt) — Docker, uvx, or npx 3. Local (from source) — run this repo directly Use the hosted endpoint (recommended for the simplest setup). Doc

Give Claude access to all 200+ AWS services through the AWS CLI. This MCP server lets Claude run AWS CLI commands on your behalf. Instead of wrapping each AWS API individually, it wraps the CLI itself—giving Claude complete AWS access through just two tools: Claude learns commands on-demand using --help, then executes them. Your IAM policy controls what it can actually do. - Streamable HTTP transp

Get Claude Code in your local project talking to Telegram in 3 minutes! TSGram MCP connects Claude Code sessions to Telegram using TS/Node/Docker/cli-utils, enabling AI-powered code assistance directly in your Telegram chats. Ask questions about your codebase, get AI insights, and even edit files - all from Telegram on your phone! - Local Docker container runs the Telegram webhook server - TSGram

基于 MCP 协议的代码执行器，最后是通过 Docker 来进行代码执行的，所以本机上需要有 Docker 的环境. - [x] 支持 MCP 协议 - [x] 支持代码执行，获得代码执行的结果 - [ ] 获取代码执行的时间、内存等信息

title: System for Cross-domain Identity Management (SCIM) MCP Server colorFrom: pink colorTo: blue sdk: docker appport: 7860 pinned: false shortdescription: SCIM 2.0 relay for AI agents to manage identities - building-mcp-track-enterprise scim-mcp is a SCIM (System for Cross-domain Identity Management) relay for AI agents. This MCP server enables LLMs to manage enterprise user identities and group

A Model Context Protocol (MCP) server that integrates with Tilt to provide programmatic access to Tilt resources and logs through LLM applications. Imagine prompting like this: The key insight is you no longer need to tell your LLM how to build and deploy your code. Instead, you can simply ask it to what to build and deploy. Tilt is a powerful tool for working with Docker/Kubernetes workloads. Wit

An MCP server for managing Docker with natural language! - 🚀 Compose containers with natural language - 🔍 Introspect & debug running containers - 📀 Manage persistent data with Docker volumes - Server administrators: connect to remote Docker engines for e.g. managing a public-facing website. - Tinkerers: run containers locally and experiment with open-source apps supporting Docker. - AI enthusia

Ever wished you could just ask Portainer what's going on? Now you can! Portainer MCP connects your AI assistant directly to your Portainer environments. Manage Portainer resources such as users and environments, or dive deeper by executing any Docker or Kubernetes command directly through the AI. Portainer MCP is a work in progress implementation of the Model Context Protocol (MCP) for Portainer e

A Model Context Protocol (MCP) server that lets AI assistants use Go’s LSP (gopls) for navigation, diagnostics, testing, coverage, and more. This MCP server helps AI assistants to: - Use LSP to analyze Go workspaces - Navigate to definitions, references, and workspace symbols - Format, rename, and inspect code actions without leaving MCP - Run Go tests, coverage, go mod tidy, govulncheck, and modu

文颜（Wenyan） 是一款多平台 Markdown 排版与发布工具，支持将 Markdown 一键转换并发布至： - 以及其它内容平台（持续扩展中） 文颜的目标是：让写作者专注内容，而不是排版和平台适配。 文颜目前提供多种形态，覆盖不同使用场景： - macOS App Store 版 - MAC 桌面应用 - 跨平台桌面版 - Windows/Linux - CLI 版本 - 命令行 / CI 自动化发布 - 👉 MCP 版本 - 本项目 - 核心库 - 嵌入 Node / Web 项目 本仓库是 文颜的 MCP Server 版本，基于模型上下文协议（Model Context Protocol），旨在让 AI 助手（如 Claude Desktop）具备自动排版和发布公众号文章的能力。 - 与 AI 深度集成：让 AI 帮你管理公众号的排版和发布 - 支持 SSE 模式

Backend: Node.js with Express.js

- 🚀 99% fewer API calls for batch operations (150-300 → 1) - ⚡ 88% token reduction with compact response formats - 🎯 Batch Dedupe Mode - Check 50-100 titles in one operation - 🔄 Smart Caching - 70-85% API call reduction - 🛡️ Safety Features - Multi-season confirmation, validation - 📦 4 Powerful Tools - Consolidated from 8 for clarity - 🤖 Automated Security Scanning - Dependabot for dependenc

A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models. - Native Async API - Uses falclient.runasync() for optimal performance - Queue Support - Long-running tasks (video/music) use queue API with progress updates - Non-blocking - All operations are truly asynchronous - STDIO - Traditional Model Con

Shodh-Memory Persistent memory for AI agents. Single binary. Local-first. Runs offline. We built this because AI agents forget everything between sessions. They make the same mistakes, ask the same questions, lose context constantly. Shodh-Memory fixes that. It's a cognitive memory system—Hebbian learning, activation decay, semantic consolidation—packed into a single ~17MB binary that runs offline