Unpopular Opinion: The "Work" is now writing .mdc files, not the actual prompts.
I’ve realized that I’m getting much better results by treating .mdc files like a PRD or architecture doc. Instead of spending time prompting (and re-prompting) to get the logic right, I’ve shifted my effort into creating detailed rules first. Once the MDC is solid, Cursor knows exactly what to build and the chat becomes secondary. Anyone else finding that "Rule-first" development is way more efficient than "Chat-first"?
Agent Library is a centralized collection of reusable AI agent skills, workflows, prompts, and specialized knowledge modules. Built for Claude Code, Codex, Cursor, and other AI coding agents with an efficient on-demand loading architecture.
An open-source collection of production-ready AI skills, prompts, templates, and workflows for Claude, ChatGPT, Gemini, Cursor, and other AI tools.
A collection of Skills (system prompts) for autonomous AI Agents and LLMs (Google Antigravity, Claude Code, GitHub Copilot, Codex, Cursor, AutoGPT, ChatGPT, Gemini).
许惟的 AI 工具合集 · A growing collection of practical AI tools & skills. First up: diagram-generator (one sentence → clean diagrams).
A curated collection of high-quality AI prompts for ChatGPT, Claude, Gemini, Cursor, and other AI assistants
A curated collection of ready-to-run automation prompts for real-world workflows.
Workflows from the Neura Market marketplace related to this Cursor resource