Is Your AI-Generated Code Base Actually Well Designed? Here’s How to Audit It
Introduction
If you’re building a Node.js Express API and letting AI write most of your code, there’s a question you should be asking constantly: is this codebase architecture actually well designed, or is it just working? Those are two very different things. Code can run perfectly fine in the short term while quietly accumulating structural debt that makes every future feature harder to ship.
In this post, we’ll walk through how to use Matt Pocock’s improved codebase architecture skill with Claude Code to audit a real Node.js Express project, surface actionable architectural improvements, and understand why those suggestions matter — backed by ideas from some of the most respected software design books in the industry.
The Problem With “It Just Works”
Picture a typical setup: a Node.js Express API for a blog platform, complete with authentication and post routes, mostly written with AI assistance. The code runs. The tests pass. But does that mean the codebase architecture is sound?
Not necessarily. AI-generated code tends to be functionally correct but structurally inconsistent — repository layers that don’t need to exist, business rules scattered across multiple files, and abstractions that add complexity without adding value. The only way to catch this is to actually audit the structure, not just the output.
What Is the Improved Codebase Architecture Skill?
Matt Pocock maintains a GitHub repository of skills for AI coding agents, and one of them is specifically built for this kind of audit. The skill scans a codebase looking for “deepening opportunities” — places where modules could be simplified or consolidated — informed by:
- Domain language used in the project
- Context and markdown documentation
- Architectural Decision Records (ADRs)
Installing the Skill
Getting it running is simple:
- Copy the npx install command from Matt Pocock’s repository
- Run it in your terminal or code editor
- Select “improved codebase architecture” from the list of available skills
- Install it for Claude Code (or whichever coding agent you use)
Once installed, you’ll find a SKILL.md file inside the .claude/skills/ folder, along with supporting files the skill uses to evaluate your project.
Running the Audit
With the skill installed, running it is as easy as telling Claude Code to “use the skill improve codebase architecture.” From there, Claude Code explores the project structure on its own and returns a set of suggestions — in this case, five candidates, each ranked by confidence level, plus a visual web page summarizing the findings.
Candidate 1: Collapse the Repository Layer (Strong)
The first and strongest suggestion was to merge a separate post repository and post service into a single service that calls Prisma directly, since the repository layer was already just a thin wrapper around Prisma.
This recommendation traces back to John Ousterhout’s concept of deep modules from A Philosophy of Software Design — the idea that a module should hide significant complexity behind a simple interface, rather than existing as a thin pass-through layer.
That said, there’s a counterargument worth knowing. Keeping a repository layer thin can pay off if you ever need to swap data storage systems. For relational databases, switching providers usually just means updating drivers or tweaking queries — Prisma absorbs most of that. But if you’re moving from a relational database to something like AWS DynamoDB or S3-based storage, the interfaces differ enough that a dedicated repository layer becomes genuinely useful as an abstraction boundary.
The takeaway: this suggestion is solid, but whether you act on it depends on how likely you are to change your storage backend down the line.
Candidate 2: Deepen the Slug Module (Strong)
The second suggestion targeted a more subtle issue: a “slug invariant” — the rule that a post’s slug should only be set on first publish — that had leaked across three separate modules instead of living in one place.
This is the kind of issue that’s easy to introduce with AI-assisted development and genuinely hard to catch through casual code review. Manual review had already flagged this same issue, and the fact that the skill independently surfaced it too is a strong signal that it’s catching real problems, not just style nitpicks.
Candidate 3: One Guard, Not Two (Worth Exploring)
A less urgent suggestion to centralize Prisma error classification logic that currently exists in two places. Not critical, but a reasonable cleanup candidate.
Candidate 4: Drop the PostController Class (Speculative)
The skill flagged the post controller class as a possible simplification, but this one’s marked speculative — meaning it’s a “maybe,” not a “should.” In this case, keeping the controller class made more sense for the project’s needs.
How Do You Know Which Suggestions to Trust?
This is really the core lesson here. A tool can surface candidates, but evaluating which ones are actually worth acting on requires real software design knowledge. The skill’s author points to several foundational books as references:
- The Pragmatic Programmer
- Domain-Driven Design
- Extreme Programming Explained
- A Philosophy of Software Design
Reading these — and reading more code in general, whether AI-written or human-written — builds the judgment needed to separate strong architectural improvements from speculative noise.
Implementing the Refactor
Once you’ve decided which suggestion to act on, implementation is straightforward: tell Claude Code which numbered candidate to tackle, and it handles the refactor. After any refactor, the same rules apply as always — review the resulting code carefully, confirm the test suite still passes, and manually QA the change before considering it done.
Key Takeaways
- Working code and well-architected code are not the same thing. AI can produce code that runs correctly while still accumulating structural debt.
- The improved codebase architecture skill audits a Node.js/Express (or similar) codebase and surfaces ranked, actionable suggestions based on real software design principles.
- Deep modules matter. Consolidating thin, pass-through layers (like an unnecessary repository layer) can simplify a codebase — but weigh this against future flexibility needs.
- Scattered invariants are a red flag. Business rules that leak across multiple modules (like the slug logic in this example) are prime refactoring targets.
- Tooling doesn’t replace judgment. You still need to understand software design principles — through books, code review, and practice — to know which suggestions are worth implementing.
- Always review after refactoring: check the diff, run your tests, and manually QA the change.
Conclusion
AI coding agents are remarkably good at writing code that works. What they can’t do on their own is guarantee that code is well designed. Tools like Matt Pocock’s improved codebase architecture skill close part of that gap by auditing your project against established software design principles and surfacing concrete, ranked suggestions for improvement.
But the tool is only half the equation. The other half is you — staying in the driver’s seat, reading the books these principles come from, reviewing what AI generates, and developing the judgment to know which suggestions genuinely make your codebase better.
Watch the Full Video
Want to see this entire audit process in action, step by step, inside a real Node.js Express project? Watch the full video walkthrough to see the skill installed, run, and applied in real time.
