So I built a GitHub App that audits your Python codebase. It reviews PRs and runs weekly scans on the entire repo, catching complex functions, dead code, and duplicates. When it finds issues, it posts comments and Issues with concrete suggestions.

It's powered by pyscn (https://github.com/ludo-technologies/pyscn), a static analyzer I open-sourced a few months ago.

Happy to answer questions.

It reviews PRs and runs weekly audits on the entire repo. When it finds problems, it posts them as comments or Issues with concrete suggestions.

I open-sourced pyscn ([https://github.com/ludo-technologies/pyscn](https://github.com/ludo-technologies/pyscn)) a few months ago. I liked what it could do, but wanted to make it more accessible and more powerful. Combining it with an LLM felt like the right next step. pyscn-bot is the result.

It catches things like complex functions, dead code, and duplicated logic. It can also analyze your entire codebase architecture, something most review bots can't do since they only look at diffs.

Happy to answer questions.

I'd love to look into incorporating checks for these into pyscn. This is exactly the kind of stuff I want it to catch.

Thanks a lot for the bug report and for providing the details. I have a hunch—it's possible that you need to explicitly specify the path depending on your directory structure. For example, if your Python files are under a src directory, could you try running it like [your_tool_name] analyze src/?

If that still doesn't solve the problem, it would be a huge help if you could open a quick issue on GitHub for this.

Thanks again for your feedback!

It was linked in the TLDR newsletter on monday.

(myglobalenv) steve@bird:~/PycharmProjects/netflow$ ls aggregator.py data netflow settings.py assets database.py notifications.py sniper.py config.py Dockerfile opencode.json start.sh context_manager.py integration.py __pycache__ tcpports.py context.py largflow.py README.md dashboard.py main.py requirements.txt (myglobalenv) steve@bird:~/PycharmProjects/netflow$ pyscn check . Running quality check... Complexity analysis failed: [INVALID_INPUT] no Python files found in the specified paths Dead code analysis failed: [INVALID_INPUT] no Python files found in the specified paths Clone detection failed: no Python files found in the specified paths Error: analysis failed with errors Usage: pyscn check [files...] [flags]

Flags: --allow-dead-code Allow dead code (don't fail) -c, --config string Configuration file path -h, --help help for check --max-complexity int Maximum allowed complexity (default 10) -q, --quiet Suppress output unless issues found --skip-clones Skip clone detection

Global Flags: -v, --verbose Enable verbose output

-v doesnt give me anything neither.

Great idea using it as grounding for AI-assisted refactoring! Let us know how that workflow goes.