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開発者向け MCP サーバーとよく使われる AI コードエディタ。
MCP Servers
Your senior Microsoft 365, Intune, and Entra admin, installed in the AI client you already use. Agent Skills plus MCP servers for Claude Code, Cursor, Codex, Gemini, and GitHub Copilot, with a human...
A Stdio MCP server which proxies an SSE MCP server.
douyin-mcp 让 AI 同时读懂你的抖音创作数据和视频内容 本地运行 · 音轨文案按需提取 · 数据可追溯 · 面向个人创作者的 MCP Server
Agent Interface Compiler — One command. Every agent speaks your product. Transform OpenAPI specs into MCP Servers, Skills, CLAUDE.md, AGENTS.md, and more.
泪心UE4Dump逆向分析MCP工具
This repository contains a complete tutorial on how to create an MCP (Modular Computational Platform) client in Python using FastAPI.
The decision layer for personal AI. Local-first runtime where agents automate across GitHub, Slack, Gmail & 45+ services — but every risky action can stop for your yes, workers (agents) earn autonomy...
Easy install scripts and user guide for Zotero MCP — connects your Zotero library to Claude and other AI assistants
Intercept AI requests, track usage, inject MCP tools centrally
An MCP server that connects to a Swagger specification and helps an AI to build all the required models to generate a MCP server for that service.
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🧩 MCP Gateway - A lightweight gateway service that instantly transforms existing MCP Servers and APIs into MCP servers with zero code changes.
Agent Skills
The Best AI Agent Framework for Agent Collaboration.
The open sharing protocol for the agentic era. A Linux Foundation AI & Data Project
Agent Skills
- : Turn your idea into a video using your coding agent. - : Edit and animate using drag and drop. - : Connect to data, and manage complexity with code.
Reverse-engineer any design system into a Claude-ready skill. Pure static analysis. No AI. No API keys.
Craft AI-driven interface effortlessly🤖
Official Pulumi Agent Skills for writing, migrating, and operating infrastructure with AI coding agents
- [2026/05/01] 🔥 (📃Paper) has been released.
A Claude Code of the skills we share at AI Builder Club for building : agents that get triggered on their own, pick up work, ship it, verify it, and log what they learned, so the work compounds...
"To achieve great things, two things are needed: a plan and not quite enough time." - attributed to Leonard Bernstein
Commonplace studies how agentic systems can change after deployment through inspectable knowledge artifacts.
LiteRT-LM is Google's production-ready, high-performance, open-source inference framework for deploying Large Language Models on edge devices.