Claude Desktop 安装指南
支持本地 MCP 配置的桌面客户端。
MCP Servers

APISIX Model Context Protocol (MCP) server is used to bridge large language models (LLMs) with the APISIX Admin API.

MCPSDK.dev(ToolSDK.ai)'s Awesome MCP Servers and Packages Registry and Database with Structured JSON configurations. Supports OAuth2.1, DCR...

A Model Context Protocol (MCP) server that provides line-oriented text file editing capabilities through a standardized API.

A Model Context Protocol (MCP) server that provides seamless integration with the GoHighLevel API v2.

Model Context Protocol server that enables Claude Desktop to interact with and view tmux session content.

A Model Context Protocol (MCP) server implementation that provides a web search capability over stdio transport. This server integrates with a WebSearch Crawler API to retrieve search results.

A curated guide to convention files AI agents read, write, and act on: AGENTS.md, CLAUDE.md, SKILL.md, llms.txt, MCP configs, rules, and examples.

Simple MCP server for managing AI prompts and agent configurations with direct claude CLI orchestration.

Production ready MCP server with real-time search, extract, map & crawl.

A secure REST API and Model Context Protocol (MCP) server for your vault.

Model Context Protocol server for PwnDoc pentest documentation

This repository contains a Model Context Protocol server implementation for Reddit that allows AI assistants to access and interact with Reddit content through PRAW (Python Reddit API Wrapper).
Agent Skills

https://github.com/user-attachments/assets/023c0a9c-194a-45a9-bb88-ac8f16f5ffd5

Screenshot testing is key to validate your app's appearance and functionality. It efficiently detects visual issues and tests the app as users would use it, making it easier to spot problems.

Built by DeepSeek, for DeepSeek. An AI coding agent that flows through your codebase like a tide. The name: DeepSeek + tide (terminal IDE).

山音超级编剧大师 - 全格式影视编剧 Skill 由 @山音 设计的自然语言驱动编剧 Agent Skill 覆盖 1-3 分钟概念超短片→90 分钟电影长片→多集剧集的全流程剧本创作,一站式搞定人物、大纲、场景、剧本全链路

for quasi-experimental designs in Python.