This looks great! Duplication and dead code are especially tricky to catch because they are not visible in diffs.

Since you mentioned the implementation details, a couple questions come to mind:

1. Are there any research papers you found helpful or influential when building this? For example, I need to read up on using tree edit distance for code duplication.

2. How hard do you think this would be to generalize to support other programming languages?

I see you are using tree-sitter which supports many languages, but I imagine a challenge might be CFGs and dependencies.

I’ll add a Qlty plugin for this (https://github.com/qltysh/qlty) so it can be run with other code quality tools and reported back to GitHub as pass/fail commit statuses and comments. That way, the AI coding agents can take action based on the issues that pyscn finds directly in a cloud dev env.

This is an interesting idea but you might be better off marketing it as a tool for software engineers, maybe to help with old code bases. Or even for someone stuck cleaning up vibe coded nonsense.

Vibe coders don't care about quality and wouldn't understand why any of these things are a problem in the first place.

I absolutely love this. Tests and code coverage metrics are still important, but so easy to leave behind as you are running toward the vibe. This is a nice addition to the toolbox.

Current AI is most proficient in java-script and python because of the vast training data. But in the long-run, i feel like languages with good static analysis, static type checks, clear language rules, memory leak detection, fuzzing, test oriented code, and any number of other similar tooling is gonna be the true game-changer. Directed learning using this tooling could improve the models beyond their training set, or simply allow humans to constrain AI output within certain bounds.

I'm surprised you went with go for this, you're going to encounter so much pain with large codebases.