Use claude code and corresponding skills to achieve MicroPython code optimization

[leezisheng]

If you’re a MicroPython developer, you’ve likely encountered these frustrating issues:

• Your driver code runs slowly with high loop latency, failing to meet real-time requirements;
• Your device runs out of RAM, leading to frequent memory overflow errors and HardFault crashes—especially on low-memory boards like the RP2040;
• Manually optimizing performance requires diving into complex low-level optimization manuals, wasting hours on trial-and-error without guaranteed results;
• You struggle to reduce memory usage, with techniques like CONST and slots causing unexpected issues when misapplied.

Now, a zero-threshold automated optimization solution is here:

With the Claude Code + MicroPython_Skills toolchain, you can complete end-to-end optimization of your MicroPython driver code in one click—boosting performance and slimming down memory usage. No need to manually parse optimization guides; you’ll get faster, leaner code with built-in before/after testing to validate improvements.

The toolchain includes two core optimization utilities:

• ✅ upy-opt-driver (Performance Optimization):
  • Rewrites code according to the GraftSense Performance Optimization Guide, targeting loop execution, numerical operations, and memory access logic. Using techniques like @viper/@ptr8 decorators, float-point optimization, and replacing list with array, it can increase loop execution speed by 20-58x, drastically reducing latency and solving real-time performance bottlenecks.
• ✅ upy-slim-driver (Memory Optimization):
  • Refactors code per the GraftSense Memory Minimization Guide, significantly reducing RAM usage through constant privatization, slots declarations, eliminating dynamic allocation, and optimizing GC control. This prevents memory overflow crashes on low-memory devices, ensuring stable operation even with limited resources.

This guide will teach you step by step how to use Claude Code's automation tools to optimize the execution speed of MicroPython code hints or reduce memory usage.

Full Operation Guide:
https://f1829ryac0m.feishu.cn/wiki/VZHGwbWuPif145kk59QcnsL7nIg?from=from_copylink

The guide covers the full workflow:

1. Pre-requisites: Claude Code installation and toolchain deployment (with a China-recommended setup option);
2. Step-by-step operation: Benchmarking original code performance/memory → one-click performance optimization → one-click memory optimization → before/after validation;
3. Advanced techniques: Detailed breakdown of core optimization methods, expected effects, and applicable scenarios for both performance and memory tuning;
4. Pitfall avoidance: A curated list of common issues, root causes, and solutions to help you avoid costly mistakes.

Whether you need faster sensor driver response times or want to fix crashes on low-memory boards, this guide lets you achieve professional-level MicroPython code optimization with zero prior expertise, easily overcoming performance and memory bottlenecks.

Skill source code address: https://github.com/FreakStudioCN/MicroPython_Skills

[robert-hh]

Is this a commercial?

[GitHubsSilverBullet]

It fills my BS-Bingo card quite nicely.

[robert-hh]

That:s what it is. The question is, whether the embedded links are safe.

[Josverl]

@leezisheng,

Please do not turn this into another AI post stoked discussion.
One of your previous discussion in this community turned into what I perceived as a stream of AI slop.
If that happens again you risk being banned.

[leezisheng]

Dear @robert-hh and the MicroPython team,

I'm truly sorry for the confusion and trouble my posts have caused in the community. That was never my intention.

To clarify: I did all the practical testing and development of MicroPython drivers/optimizations using Claude, with full verified records (including code, test results, and step-by-step processes) saved in my cloud document. The AI-generated summary in my post was just a quick draft because converting the cloud document to standard Markdown was time-consuming, not empty spam or commercial promotion. The link in my post points to my complete, non-malicious test notes.

I understand the community's stance against low-quality AI content, and I apologize for not following the post guidelines properly before. I'll be more careful to organize my content into clear, substantive posts that fit the community's standards moving forward.

Thank you for your feedback, and I'm sorry again for the misunderstanding.

[leezisheng]

Sorry for the inappropriate post format.
All of my MicroPython skills and summary are sorted out based on my two markdown documents:

• https://github.com/FreakStudioCN/MicroPython_Skills/blob/main/MicroPython_Memory_Footprint_Minimization_Guide.md
• https://github.com/FreakStudioCN/MicroPython_Skills/blob/main/MicroPython_Performance_Optimization_Guide.md

Brief Overview of the Two Guides

• MicroPython_Memory_Footprint_Minimization_Guide.md
  • This guide focuses on solving the limited RAM issue on embedded devices (e.g., RP2040, ESP32). It covers:
  • Core concepts like RAM/Flash, bytecode, and frozen modules to build a clear foundation for beginners.
  • Practical memory optimization methods: pre-allocation, using array/bytearray instead of lists, const for constants, frozen modules/.mpy precompilation, and reducing GC triggers.
  • All methods are verified with runnable REPL test code, with memory usage and GC time comparisons to prove effectiveness.
• MicroPython_Performance_Optimization_Guide.md
  • This guide provides a complete workflow for improving MicroPython performance, from bottleneck identification to hardware-level optimizations:
  • A structured optimization process: locate bottlenecks first, then optimize memory usage, code execution efficiency, and hardware operations step by step.
  • Practical optimizations: const declarations, native/viper decorators, memoryview to avoid copies, and direct register operations.
  • Every optimization is validated with timing tests, showing clear performance improvements before and after changes.

These two documents are summarized by myself, referring to the official MicroPython documentation and a lot of actual hardware tests. I sorted out memory footprint reduction methods and running performance optimization skills.
I'm just a MicroPython enthusiast, I simply organized my usual learning and practical experience, and I didn't understand the community posting rules very well before. There is no advertising or other purpose. I will pay attention to the format later.

[leezisheng]

@Josverl l I'm really sorry for the misunderstanding and the trouble I caused.

I'm just a regular MicroPython enthusiast, and these are just my personal practical experience summaries, not AI-generated slop or spam.

The memory footprint reduction and performance optimization skills are all summarized from official documents and my own repeated tests on real hardware, with every method verified by myself.
The other skill is about packaging a sensor driver into a standard package (with README, MIT license, package.json) that can be directly installed via mip, which I've fully tested in my own driver library.

All of this is documented and tested in my GitHub repo: https://github.com/FreakStudioCN/GraftSense-Drivers-MicroPython

I didn't fully understand the community's stance against low-quality AI content before, and I'm really sorry for making you think this is messy or unhelpful. I never meant to cause issues or turn this into a problematic discussion.

I'll be much more careful with my posts in the future, and I won't use AI-generated summaries or any content that might be misinterpreted. Thank you for your reminder.

[Josverl]

@leezisheng,
Thank you for your response, and I am happy for your enthusiasm and willingness to share your learnings and creations.
In a community such as this there should be a place for all and for a variety in different styles in communication.

One tip to allow your post to be both succinct, and carry more details, is to us Collapsed Sections

Example

You can add more information here for users that want to dive deeper.
or add multiple large screenshots .

Thank you for your consideration for future posts.

[leezisheng]

Thank you so much for your kind words and the helpful tip! I'll definitely use collapsed sections in my future posts to keep the discussion clean while sharing my work.

[andrewleech]

You might also be interested in my claude plugin marketplace I've been building up with micropython skills: https://github.com/andrewleech/claude-mpy-marketplace

It's perpetually work in progress but includes all the skills I use to improve the consistency and accuracy of my micropython related work; though most are focusing more on feature work within micropython itself moreso than applications built in top of micropython however many of the same concepts directly translate.

[leezisheng]

This is awesome! Thanks for sharing claude-mpy-marketplace. I’ll check it out today and spread the word to Chinese MicroPython devs — they’re gonna love this!

[ruffrecords]

Am I missing something - is there an English version of this?

Cheers

Ian

[leezisheng]

Hi Ian! Currently there isn’t an official English version yet, but I’m working on it and will release one as soon as possible. Thanks for your interest!

[ruffrecords]

Hi there! I tried using google translate and it did a reasonable job except it could not cope with the tables. I think an English version will be very popular. Also an English translation of your memory management document would be good.

Cheers

Ian