Cursor 설치 가이드
개발자용 MCP 서버와 함께 자주 쓰이는 AI 코드 에디터.
MCP Servers
Add vision to text-only models in Opencode (, ) — , no manual file saving.
Open-source, local-first memory and context for AI agents. One memory store, every agent.
Agent Browser eliminates the need to configure each MCP server in every client. Connect your clients once to Agent Browser, and it will manage all your Server-Sent Events (SSE) MCP servers for you.
Self-hosted, agent-native web app and MCP server for your Obsidian-style Markdown vault. Browse, search, and edit notes from a fast UI or from AI agents.
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。
LogLead performs log loading, log enhancement, log feature engineering, log analysis, log anomaly detection also via MCP-server.
Agent Skills
用最少的提示词，识别并去除中文 AI 写作痕迹。
Agent Skill for generating AGENTS.md files following the agents.md convention | Claude Code compatible
A universal multi-cloud data MCP Server supporting over 40 types of data source connections, providing secure, unified data access in a single platform.
High-value AI skills repository for Codex, Claude Code, OpenClaw, agents, prompts, and automation workflows. 高价值的 AI 技能库，涵盖 Codex、Claude Code、OpenClaw、代理、提示和自动化工作流程。
A curated collection of custom agents, skills, rules, and prompts for GitHub Copilot, Google Antigravity, OpenCode, ChatGPT Codex, and Oh My Pi.
A Copilot CLI skill that analyzes your repository and generates the configuration files AI agents need to contribute correctly.
The real-time engine for AI agents on the JVM. Tokens flow from the LLM runtime to the client through a broadcaster you can filter, gate, and observe — over WebSocket, SSE, long-polling, or gRPC, and...
Practices, protocols, and skills for AI-driven software development
This is a list of JavaScript repositories with good first issues for newcomers to open source. Contributions are welcome!
Turn a codebase, a requirements document, or both into a living skills system for AI coding agents.
专为 优化的 Codex skill，大幅降低 token 消耗并提升前缀缓存命中率。
AI Unified Process (AIUP) is a requirements-first workflow for taking software from a product vision to reviewed specifications, implementation, and traceable tests.