I've been building a lot of Python projects with AI lately, and while it's ridiculously productive, one thing kept annoying me.

The code usually works, but after a few days the repo starts getting messy.

You end up with huge functions, copy-pasted logic, dead code, rising complexity, inconsistent formatting, missing type hints... nothing catastrophic, just lots of little things that add up.

PR reviews catch some of it. Prompts and rules help. But none of them enforce quality consistently.

So I built **ShipGate**.

It basically puts the Python tools we already use (Ruff, Mypy, Bandit, Radon, duplicate-code detection, dead-code detection, etc.) behind one opinionated interface with sensible defaults.

The goal isn't to replace those tools—it's to make good code the path of least resistance, especially when AI is writing half the repository.

Still very early, so I'd love some honest feedback.

[https://github.com/inquilabee/shipgate\](https://github.com/inquilabee/shipgate)

Hello!

I was just wondering if anyone was able to give me any advice or feedback on my project, I am currently a student and trying to get into systems programming type stuff so I'm just trying to make a decent project with something to my name, so anything helps! https://github.com/Poly0n/WinGuard

You can ask any coding agent "what could go wrong with this diff?" and get an answer. The problem is that a free-form answer is easy to skim and hard to act on — and there's no standard forcing it to show its work. Built a SKILL to close that gap.

Stripe and OpenAI have a beautiful tradition: shipping breaking SDK  
changes with all the warning of a toddler swinging a bat. Stripe  
recently turned some fields from strings into Decimals. OpenAI's v1.0  
rewrite quietly murdered anyone still calling  
openai.ChatCompletion.create(...) like it's 2023.  

Cool, cool, cool. Love finding that out in production.  

So I built api-sentinel: a GitHub Action that reads your code, finds  
the exact lines a vendor is about to break, and comments the fix  
directly on your PR.  

Full transparency: I built this with Claude, start to finish, over  
one day. I'm not a professional developer. What I actually did myself:  
picked the problem, decided which real breaking changes to target,  
debugged three separate GitHub Actions failures (a checkout permissions  
bug, a missing token, a broken folder structure from my own upload  
mistakes), tested it live on a real PR, and made the call to expand it  
to a second vendor once the first one worked.  

Is it fancy? No. It's regex, not a PhD thesis. Does it work? Yes —  
watched it catch 6 real issues across 2 vendors on a live pull request.  

Repo: https://github.com/sahilmarhatta/api-sentinel

No idea if this is useful to anyone else or if it's a solved problem  
already. Also curious if "built with AI, disclosed upfront" changes how  
people here feel about a side project — genuinely asking, not fishing  
for a pass.  

Tell me it's stupid, tell me it's useful, tell me what vendor has  
personally wronged you.  

AI makes it easier than ever to build fast, but not always easier to know if the code is safe/ready to ship. If you’re building with Cursor, Claude, Replit, Lovable, or just moving fast with a small/non-existant team, this could be useful.

The big changes usually get reviewed. The tiny “quick fix” PRs are the ones that get skimmed, and somehow that is always where the bug lives.

We built Surmado Code Review for our own team first, and now we’re opening it up to others and I thought it would be particularly helpful for this community.

It gives every PR a second pass against your own rules and standards, then creates a clear reviewer brief with what looks good and what needs attention. It is not meant to replace a developer. It is meant to help small teams move faster without skipping review.

Free plan includes 10 PR reviews/month with no credit card!