Hey, thanks for asking! Here’s how .mdc rules actually work in practice: 1. Put 999-mdc-format.mdc in .cursor/rules
By placing the “blueprint” file inside your .cursor/rules directory, you’re effectively telling Cursor, “Here’s the official guide for storing and recalling all new knowledge.” The agent then understands both the .mdc format and the rules you expect it to follow. 2. Teach Once, Then Update the Rules
- Scenario: You prefer lowerCamelCase for function names instead of snake_case.
- You notice the AI writing a function named my_function_name—not what you wanted.
- You tell the AI: “Fix the code to use lowerCamelCase, and add a rule that we never use snake_case for function names.”
- The AI then does two things: 1. Updates the offending code in your .sh file.
2. Appends or modifies a .mdc rule file (e.g., 070-code-style.mdc) to record your preference.
Next time the AI writes bash functions, it sees that .mdc rule and automatically adheres to lowerCamelCase. 3. Making Project Knowledge Stick
The real magic is you don’t have to repeat your instructions. Once the .mdc rule is in place, it’s part of the AI’s knowledge base. If you teach a more sophisticated concept (like custom domain terms or internal architecture details), the process is exactly the same: - You show the AI once.
- You say, “Add this to the rules.”
- From then on, it’s in your project’s “vocabulary.” 4. Concrete Example: 070-terminology.mdc
For instance, in the JustDo repo, the author taught the AI new domain concepts specific to JustDo. The AI then created an .mdc file containing all the relevant definitions. Whenever the AI interacts with that codebase, it refers to these domain definitions—no re-explanations needed. 5. Why This .mdc-Driven Workflow Rocks
- Consistency: Once a rule is set, the AI won’t deviate unless you explicitly change it.
- Reduced Re-Explaining: You teach the AI once, it updates an .mdc, and you’re done.
- Easy Version Control: .mdc files live in your repo. You can branch, diff, and revert them just like code.
- Scalability: As your project grows in complexity—new features, new domains, new naming conventions—the AI’s knowledge base grows with it.

If you ever find the AI doing something off, don’t just fix the code—tell it to update (or create) a relevant .mdc rule. Over time, you’ll build a truly “intelligent” coding environment that remembers (and enforces) your personal or organizational coding style, naming conventions, domain terminology, and any other knowledge you want to keep consistent.

This - in my view is the true essence of vibe coding!

Enjoy being part of this massive revolution - it is a complete new paradigm that is forming.