GitHub AI Agent Skills

cognee‑mcp - Run cognee’s memory engine as a Model Context Protocol server Join Discord Join r/AIMemory Build memory for Agents and query from any client that speaks MCP – in your terminal or IDE. - Multiple transports – choose Streamable HTTP --transport http (recommended for web deployments), SSE --transport sse (real‑time streaming), or stdio (classic pipe, default) - API Mode – connect to an a

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,

Your MCPs, supercharged. Find any tool instantly, execute with code mode, run on schedule, discover skills, load Photons, ready for any client. Smart loading saves tokens and energy. Instead of your AI juggling 50+ tools scattered across different MCPs, NCP gives it a single, unified interface with code mode execution, scheduling, skills discovery, and custom Photons. Your AI sees just 2-3 simple

MCP server for transferring conversation context between AI chats or different projects within the same AI. - Audit Logging (v0.7.0+): Optional structured JSONL logging for diagnostics (--audit flag) - Verbatim Conversation Saving (v0.6.1+): AI saves complete conversations without summarization or abbreviation - Merge Handoffs (v0.6.0+): Combine multiple related handoffs into one unified context -

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. 1MCP (One MCP) is designed to simplify the way you work with AI assistants. Instead of configuring multiple MCP servers for different clients (Claude Desktop, Cherry Studio, Cursor, Roo Code, Claude, etc.), 1MCP provides a single, unified server. - 🔄 Unified Interface: Aggregates multiple MCP ser

A Model Control Protocol (MCP) server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, VSCode with Copilot, etc. - MCP Compatible: Works out-of-the-box with any MCP client tha

Production-ready Rust SDK for the Model Context Protocol (MCP) with zero-boilerplate development and progressive enhancement. Build MCP servers in seconds with automatic schema generation, type-safe handlers, and multiple transport protocols. - Rust 1.89.0+ (Edition 2024) - Check with rustc --version - Tokio async runtime Add to your Cargo.toml: Or with cargo: TurboMCP uses feature flags for progr

Draft a new release. Bumps version, generates CHANGELOG.md from git history, and creates a PR.

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

Helps configure mcp-rubber-duck MCP server in AI coding tools. Triggers when users ask about adding rubber duck to Claude Desktop, Cursor, VS Code, Windsurf, Continue, or any MCP-compatible tool.

Publish the package to npmjs by bumping version, building, and creating a GitHub release

MCP Unity is an implementation of the Model Context Protocol for Unity Editor, allowing AI assistants to interact with your Unity projects. This package provides a bridge between Unity and a Node.js server that implements the MCP protocol, enabling AI agents like Cursor, Windsurf, Claude Code, Codex CLI, GitHub Copilot, and Google Antigravity to execute operations within the Unity Editor. MCP Unit

Guide for creating Claude Code skills following Anthropic's official best practices. Use when user wants to create a new skill, build a skill, write SKILL.md, update an existing skill, or needs skill creation guidelines. Provides structure, frontmatter fields, naming conventions, and new features like dynamic context injection and subagent execution.

A Text-to-Speech MCP server plugin for Claude Code that converts text to speech using OpenAI's TTS API. Get audio feedback from Claude as you work! - Deterministic Auto-Speak: Every Claude response is automatically spoken (via Stop hook) - 6 High-Quality Voices: alloy, echo, fable, onyx, nova, shimmer - Worker Pool Architecture: Non-blocking queue with concurrent processing - Mutex-Protected Playb

Screenpipe brand style guide. Reference this when designing UI components, writing copy, or making visual decisions.

Use when the user wants to create a ComfyUI custom node, convert Python code to a node, make a node from a script, or needs help with ComfyUI node development, INPUT_TYPES, RETURN_TYPES, or node class structure.

Automatically hunt for high-impact OSS contribution opportunities in trending repositories.

Secure environment variable management ensuring secrets are never exposed in Claude sessions, terminals, logs, or git commits

Hard-edged writing style contract for timeless, forceful English prose without AI tics

Practical patterns for using fp-ts with React - hooks, state, forms, data fetching. Use when building React apps with functional programming patterns. Works with React 18/19, Next.js 14/15.

This Model Context Protocol server implementation of Asana allows you to talk to Asana API from MCP Client such as Anthropic's Claude Desktop Application, and many more. More details on MCP here: - https://www.anthropic.com/news/model-context-protocol - https://modelcontextprotocol.io/introduction - https://github.com/modelcontextprotocol - ASANAACCESSTOKEN: (Required) Your Asana access token - RE

Point to browser DOM elements for agentic coding tools via MCP! MCP Pointer is a local tool combining an MCP Server with a Chrome Extension: 1. 🖥️ MCP Server (Node.js package) - Bridges between the browser and AI tools via the Model Context Protocol 2. 🌐 Chrome Extension - Captures DOM element selections in the browser using Option+Click The extension lets you visually select DOM elements in the

Text-to-speech and speech-to-text using fal.ai audio models

One-off HLS encoding (local video -> HLS renditions + playlist) using Transloadit via the `transloadit` CLI. Prefer builtin templates (`builtin/encode-hls-video@latest`) and download outputs locally via `-o`.