Cursor インストールガイド
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
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
Claude Code · OpenAI Codex · OpenClaw · Hermes Agent[^hermes] · Mistral Vibe[^vibe] · Cursor · Aider · Windsurf · Kilo Code · OpenCode · Augment · Antigravity
good-story 是一个给科研写作用的 agent skill。它帮你把手里的结果、图表和草稿，整理成一个证据站得住、读者听得懂、审稿人也挑不散的论文故事。
Let AI agents use the same Chrome browser you use every day.
Paper Search CLI is an agent-facing Skill + CLI package built on a standalone Node.js command line tool for academic literature work.
Agent skills for GPU kernel development — writing, debugging, profiling, and optimizing CUDA and TileLang GPU kernels.
Professional post-match team reports from — a clean Python package + an agent skill, built on the Post-Match-Report-2.0 blueprint.
The definitive list of agent skills and template libraries for generating beautiful, single-file HTML presentations — for Claude Code, Codex, Cursor, OpenClaw, and Hermes.
Split "planning by chatting with an agent" from "visualizing + executing" into two independent modules:
基于 Andrej Karpathy 提出的 LLM Wiki 模式 构建的 Agent Skill，通过四阶段流水线将碎片化信息转化为结构化、可检索、持续增长的个人知识库。
On agent | Claude Code, Codex, OpenCode, Antigravity, Copilot, Cursor, Grok-Build, Pi, Hermes, OpenClaw, etc.
A complete development workflow for AI coding tools
audit-prompt-caching is a portable Codex/agent skill for finding why LLM cache reuse fails across the request path: prompt/prefix caches, provider cache telemetry, cache-aware routing, agent tool...