How to Write Effective Cursor Rules (.mdc)

Cursor rules (.mdc files) let you shape how the AI editor behaves in your project. A good rule set makes outputs consistent, safe, and aligned with your team’s standards. This guide explains the structure of effective rules, provides practical examples, and shares debugging tips when the AI output drifts.

What Cursor Rules Are

Cursor rules are configuration files that tell the editor how to behave. They can define:

• Naming conventions.
• Code style expectations.
• Project-specific constraints.
• Preferred workflows or review steps.

Think of them as a lightweight policy layer. The clearer the rules, the more consistent the output.

Core Principles for Effective Rules

1. Be specific, not vague

Rules like “write clean code” are too fuzzy. Instead, say:

• “Use snake_case for variable names.”
• “Prefer pure functions for data transformation.”
• “Always validate inputs for public APIs.”

Specificity reduces ambiguity.

2. Keep scope narrow

A rule file should focus on one project or subsystem. If it tries to cover everything, it becomes hard to maintain.

3. Prioritize safety

Add rules that prevent risky behavior:

• Disallow editing outside the project directory.
• Require tests after changes.
• Enforce safe handling of secrets.

Safety rules are often the most valuable.

Recommended Structure

A simple .mdc file might include:

1. Project overview

   • A short description of the project and its goals.

2. Coding standards

   • Naming conventions and style rules.

3. Workflow rules

   • Required tests, review checks, or build steps.

4. Safety constraints

   • Limits on file access, external calls, or destructive actions.

5. Examples

   • A short input/output example to demonstrate the expected format.

This structure keeps rules readable and enforceable.

Example Rule Snippet

Here is a simplified example:

# Project: Payments Service
- Use snake_case for variables
- Validate all external inputs
- Never log secrets or tokens
- Run unit tests after any change

Even short rules like this can drastically improve output consistency.

Debugging Rule Failures

If the AI output does not follow the rules, debug systematically:

1. Check rule clarity: vague rules are often ignored.
2. Reduce scope: simplify the rules to core requirements.
3. Add examples: demonstrate the desired output format.
4. Test iteratively: update one rule at a time and re-run.

Debugging is faster when you treat rules like code: change one thing, test, and validate.

Common Mistakes to Avoid

• Overloading the rule file: too many rules become noise.
• Conflicting instructions: the AI cannot satisfy contradictions.
• No validation step: always specify how outputs should be checked.
• Ignoring maintenance: rules need updates as the project evolves.

Rules are a living part of your workflow.

Tips for Strong Rule Sets

• Start with 5–10 rules and expand gradually.
• Document why each rule exists.
• Keep rules consistent across related projects.
• Review and update rules quarterly.

Small, clear rule sets are more powerful than large, messy ones.

Conclusion

Cursor rules are a lightweight way to guide AI behavior and keep outputs consistent. Focus on specificity, safety, and clarity. When your rules are well-written, the AI editor becomes more reliable, predictable, and aligned with your team’s standards.

Start with a small rule set, test it in real workflows, and refine it as your project grows.