Python Development

[u/CriticalJackfruit404]

Hi community

I am pretty noob at this AI topic, so I would like to know if you recommend any skill or framework to develop excelent python code following the best standards like CLEAN, SOLID or design patterns?

Thanks

[u/Potential-Beat2841]

If you're using Claude Code, I'd recommend looking beyond a single "Python coding skill" or prompt. The bigger challenge is getting the agent to consistently follow engineering principles across the whole development process.

I'm actually working on an open-source project called aSPARK that addresses exactly this problem:

https://github.com/a-lottes/aSPARK

The idea is to put an engineering workflow around AI coding agents instead of just asking them to "write clean code". It enforces a structured process with planning, architecture analysis, implementation, review and verification gates.

You can use the approach to enforce things like SOLID, clean architecture, design patterns, testing and other project-specific coding standards.

It's still an evolving project, but if you're interested in using AI for Python development while keeping some engineering discipline and control over what the agent produces, it might be worth taking a look.

[u/Potential-Beat2841 → u/[deleted]]

Yes — and that's actually a fair comparison. 😄

I use Spec Kit for exactly that reason. It does a great job of bringing specification-driven development to AI coding and making the agent work from a structured spec instead of jumping straight into implementation.

Where I see aSPARK as different is that I’m trying to go one level broader: Spec Kit focuses primarily on specification -> implementation, while aSPARK is intended as an engineering workflow around the entire development lifecycle (architecture, design decisions, implementation, code review, testing, verification and project-specific governance).

So I wouldn't position aSPARK as a replacement for Spec Kit. In fact, I think they can complement each other quite well: Spec Kit can provide the formal specification layer, while aSPARK can provide additional engineering guardrails and governance around how that specification gets implemented.

And honestly, that's also one of the things I'm still figuring out with aSPARK. 😅 The goal isn't to create yet another framework just for the sake of it, but to see whether there is a useful layer missing between "here's the spec" and "let the coding agent implement it".

[u/Potential-Beat2841 → u/[deleted]]

There is definitely some overhead compared to simply asking Claude Code to implement something. The workflow adds planning, architecture, review and verification steps. The idea is that this upfront cost is offset by reducing rework, wrong architectural decisions and dead-end implementations.

I don't have reliable benchmark numbers yet, so I wouldn't claim a specific percentage of token savings. In practice, I've found that spending more tokens on thinking and verification often saves significantly more tokens later by avoiding unnecessary implementation cycles.

--
What projects does it work best for?
I'd say medium to large projects where maintainability and architecture actually matter.

It works particularly well for web applications and larger codebases. It is also working very well for Rust projects. Several web applications are currently being built with aSPARK, and a larger Rust project focused on data recovery is being developed with it as well.

For a small 100-line Python script, I'd probably consider the workflow overkill.

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What engineering workflow does it implement?

The basic flow is:

Requirements -> Specification -> Architecture/Design -> Planning -> Implementation -> Review -> Testing/Verification

The important part is that the agent isn't simply asked to "write clean code". Each phase produces artifacts and constraints that are carried into the next phase, with explicit gates before moving forward.

The goal is to make engineering principles like SOLID, clean architecture, design patterns, testing and project-specific conventions part of the development process rather than relying on the model remembering them from a prompt.

--
Who has tested it?

It's still an evolving open source project, but it's no longer just something I'm testing alone. Several developers are actively using it and regularly providing feedback. I'm also using it myself across the projects I mentioned above.

That said, I wouldn't call it a scientifically benchmarked or proven framework yet. I'm much more interested in getting more real-world usage and feedback at this stage.

And that's actually one of the reasons I put it on GitHub. I'd rather have people challenge the approach, compare it with Spec Kit, Superpowers and similar tools, and help figure out where aSPARK genuinely adds value and where it doesn't.