Common vibe coding artifacts:
• Code duplication (from copy-pasted snippets)
• Dead code from quick iterations
• Over-engineered solutions for simple problems
• Inconsistent patterns across modules
pyscn performs structural analysis:
• APTED tree edit distance + LSH
• Control-Flow Graph (CFG) analysis
• Coupling Between Objects (CBO)
• Cyclomatic Complexity
Try it without installation:
uvx pyscn analyze . # Using uv (fastest)
pipx run pyscn analyze . # Using pipx
(Or install: pip install pyscn)
Vibe coders don't care about quality and wouldn't understand why any of these things are a problem in the first place.
There was a time when hand soldered boards were not only seen as superior to automated soldering, but machine soldered boards were looked down on. People went gaga over a good hand soldered board and the craft.
People that are using AI to assist them to code today, the "vibe coders", I think would also appreciate tooling that assists in maintaining code quality across their project.
I think a comparison that fits better is probably PCB/circuit design software. Back in the day engineering firms had rooms full of people drafting and doing calculations by hand. Today a single engineer can do more in an hour then 50 engineers in a day could back then.
The critical difference is, you still have to know what you are doing. The tool helps, but you still have to have foundational understanding to take advantage of it.
If someone wants to use AI to learn and improve, that's fine. If they want to use it to improve their workflow or speed them up that's fine too. But those aren't "vibe coders".
People who just want the AI to shit something out they can use with absolutely no concern for how or why it works aren't going to be a group who care to use a tool like this. It goes against the whole idea.
But "vibe coding" is this vague term that is used on the entire spectrum, from people that do "build me a billion dollar SAAS now" kind of vibe coders, to the "build this basic boilerplate component" type of vibe coders. The former never really get too far.
The later have staying power because they're actually able to make progress, and actually build something tangible.
So now I'm assuming you're not against AI generated code, right?
If that's the case then it's clear that this kind of tool can be useful.
I think AI is useful for research and digging through documentation. Also useful for generating small chunks of code at a time, documentation, or repetitive tasks witb highly structured inputs and outputs. Anything beyond that, in my opinion, is a waste of time. Especially these crazy ass agent workflows where you write ten pages of spec and hope the thing doesn't go off the rails.
Doesn't matter how nice a house you build if you build it on top of sand.