Cursor Rules 2026: The Complete Guide to .cursorrules Files That Make AI Write Your Style
Cursor writes correct code in someone else's style - different naming, wrong patterns, no context. Rules files fix this in minutes, and there is a 40k-star collection of examples to steal from.
💡 What You Will Learn
Cursor writes correct code in someone else's style - different naming, wrong patterns, no context. Rules files fix this in minutes, and there is a 40k-star collection of examples to steal from.
📜 Table of Contents
What Rules Files Actually Do
Cursor rules (.cursorrules in older versions, .cursor/rules with glob patterns in current ones) are instructions injected into every AI request for matching files. They are the difference between an assistant that guesses your conventions and one that follows them. The community index is awesome-cursorrules (40,569 stars, fetched 2026-08-13), a library of real rules from real projects.
The Anatomy of a Good Rule
A rule that works has three parts:
- Identity: who you are building for and what the codebase is. For example: this is a Django 5 REST API serving a React SPA.
- Do this: concrete conventions - Python type hints mandatory, use dataclasses for DTOs, prefer async views, tests live in tests/ with pytest style names.
- Never do this: the anti-patterns that keep coming back - no print() debugging, no unused imports, no monkeypatching in tests.
# .cursor/rules/python.mdc
---
description: Python conventions for this repo
globs: **/*.py
---
- Always use type hints on function signatures.
- Use dataclasses for request/response DTOs.
- Async views only; no sync ORM calls in views.
- Tests: pytest, named test_feature_case, live in tests/.
- Never: print() debugging, wildcard imports, mutable default args.
How to Write Rules From Your Own History
Do not invent rules from memory. Open your last 5 pull requests and extract the repeated review comments - those are your real conventions. Convert the top 10 into rules. This takes 30 minutes and immediately cuts review back-and-forth.
Rules vs Skills vs Commands
- Rules: always-on context. Best for conventions and constraints.
- Skills: on-demand capabilities the agent can invoke (read docs, run tests, deploy).
- Commands: slash-command macros like /fix or /review that run multi-step agent actions.
They overlap but serve different jobs. Rules define how code should look; skills define what the agent can do; commands define shortcuts you press.
Steal, Then Trim
The awesome-cursorrules repo is searchable by stack. Pull rules for your framework, then trim to the 10 that match your codebase - a 200-line rule file dilutes every prompt it touches. Keep rules short enough that the model actually follows them.
Verify the Effect
Before/after test: give Cursor the same refactor task with and without rules, then check three things - naming consistency with existing code, use of your helper utilities, and whether it asked about conventions instead of guessing. If nothing changed, your rules are too vague. Rewrite them to be testable.
