Claude Desktop インストールガイド
ローカル MCP 設定に対応するデスクトップクライアント。
MCP Servers
A secure Windows SSH workspace and MCP server for Codex, Claude Code, Cursor and other AI agents.
Use ChatGPT Developer Mode as a local coding agent for your repo through MCP.
a lib to build MCP clients and MCP servers in Clojure(script)
泪心UE4Dump逆向分析MCP工具
个人知识库 MCP 服务器 — 对本地文档建立索引，并提供一组飞书 API 工具，通过 MCP 协议供 AI 客户端访问。
Use Devnors Data from Python with one API Key.
Fictional concept interface—not an actual Codex UI or a measured context, token, speed, or latency benchmark.
You built an agent. Now you need to tune the prompt. Swap the model. Restrict access for specific users. Figure out what it's actually costing you.
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.
1. GET ACTIONS: Exposes APIs to fetch the most relevant content from InterviewReady including blogs, resources and course materials. 2.
Build WordPress sites with your AI
Agent Skills
🌐 Make websites accessible for AI agents. Automate tasks online with ease.
the runtime your coding agents live on
Small, composable skills for coding agents.
Think package.json, requirements.txt, or Cargo.toml — but for AI agent configuration.
The official Lark/Feishu CLI tool, maintained by the larksuite team — built for humans and AI Agents.
Personal dotfiles with modern shell tooling, optimized for Laravel/PHP development. Features fast startup times, smart directory navigation, and modern CLI tools.
Building AI agents, atomically
Режим идиоматического русского мата для AI-агентов. Короче, душевнее, эффективнее. 18+
Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models.
Autonomous research system for measurable, computer-executable research.
LLM-compiled knowledge bases for any AI agent. Parallel multi-agent research, collector catalogs, automated session capture, feedback curation, thesis-driven investigation, source ingestion, wiki...
You don't write AGENTS.md. You train it with gradient descent.