Cursor AI - How To Extend Cursor with Code Health Monitoring

Our Founder and CTO, Adam Tornhill , shares a great example of how AI coding assistants can be taken to the next level and how we can ensure that AI-generated code does not degrade maintainability but instead serves as a guardrail and add-on to the current IDE. Let's dig in!

AI tools like Cursor accelerate coding by predicting and autocompleting code. While this boosts task completion times, it also risks generating unmaintainable code. In this article, we explore how integrating automated code reviews with Cursor helps ensure long-term code quality.

AI-assisted Coding: The Promise and Pitfalls

The effects of AI editors like Cursor can be spectacular, particularly when the AI manages to predict what I’m about to code next. If I’m happy with its suggestions, I just auto-complete. Time saved.

So what’s the problem? Well, AI assistants make it easy to generate a lot of code that shouldn’t have been written in the first place. Specifically, AI optimizes for the short-term goal of task completion at the expense of the future well-being of our codebase. It’s not enough that the code works – it also needs to be understandable and maintainable, both for our future selves as well as for our teammates.

But how does that guideline reflect in our tools and practices? Let’s look at a practical example which extends Cursor AI with automated code reviews via live monitoring.

Improving Code Health with Real-time Feedback in Cursor AI

One option is to use CodeScene’s VSCode extension, which integrates seamlessly with Cursor too. You download the extension, open the Extensions pane in Cursor, and drag the downloaded file into the Extension pane. That’s it.

Once activated, CodeScene’s IDE extension will monitor the code quality of any modified file in real-time.? Let’s see it in action:

The preceding image captures a snapshot from a live coding session. I’m writing a rule inside an if-statement, and Cursor helpfully suggests an autocomplete. However, to the right, you see that the Code Health Monitor kicks in and informs me that the code is unnecessarily complex.

Looking at the feedback, I agree: the proposed code would lead to the Complex Conditional code smell. Sure, right now I know what that complex boolean expression means. But will I remember it two months from now? Will my colleagues understand the purpose of that if-statement? Or have I just planted a landmine for the future maintenance programmer?

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Act on declining code health in Cursor AI

Complex conditional logic doesn’t age well, so we are well-advised to act on the feedback:

• I could try to come up with a cleaner way myself.?
• I might utilize Cursor’s chat functionality and ask an LLM.?
• Or, I could use the ACE extension, which is designed to safely refactor code health issues.

The last option adds an interesting twist: ACE is the AI-powered refactoring tool. As such, we’re using AI to fix the issues introduced by another AI. This demonstrates how different AI tools can complement each other to address coding challenges. Here’s what it looks like:

From here, I’d inspect the proposed refactoring. When I accept it, ACE applies the refactoring, and my code smell warning goes away. The code is now back to a healthy state, and I have avoided future problem.

Watch the demo

How is code quality measured, and why should I trust it?

So what’s “code quality”? Well, I’m glad you asked: historically, code quality has been a subjective and opinionated topic. Thanks to recent research, we now have access to better metrics: the Code Health measure.?

• Code Health is validated via peer-reviewed research, and correlates with increased development speed and defect reduction.
• Code Health is designed to identify code that is hard to understand for human readers (as opposed to machines, who happily execute any spaghetti code).
• Code Health aggregates and detects 25 code smells known to impact our ability to understand and – consequently – modify code. (The Complex Conditional that you saw above is one example. Another is Low Cohesion – code with too many responsibilities – which is a common reason for nasty bugs like unexpected feature interactions; you modify some code, and a seemingly unrelated part of the product breaks).

The main difference compared to linting tools is that Code Health focuses on what matters to the human code reader rather than line-by-line commenting. (Linters are of course still useful to capture omissions and coding mistakes.) Compare it to a spell checker: a spell checker points out mistakes and grammatical errors. However, it cannot tell you if the whole paragraph makes sense. That’s the role Code Health takes in coding.

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Get your AI Guardrails?

Automated code reviews adds essential guardrails to AI-assisted coding, ensuring code remains maintainable and understandable. To start, integrate CodeScene's VSCode extension?with Cursor AI.?

You might also be interested in taking a look at the ACE tool, which is available via the same extension, but as a commercial opt-in. (It’s expensive to run an LLM).

And remember: With AI-assisted coding, writing code is easy — keeping it clean is the real differentiator.