Introduction
Large Language Models (LLMs) have revolutionized the way we approach software development. While their ability to generate code is undeniable, the quality of that code can often be lacking. Veteran developer Fabien Sanglard discovered an ingenious method to overcome these challenges with agent.md.
The Initial Problem
In 2025, Fabien began using LLMs to speed up his work on libadbmdns, a Rust implementation of mDNS. Unfortunately, the generated code wouldn't even compile. By January 2026, things improved, but quality remained an issue: spaghetti code, no comments, and little structure.
Iterating with Agentic IDEs
By March 2026, Fabien tried agentic IDEs like Antigravity and VS Code’s Claude Code plugin. These tools allowed for more constructive code iteration, but he found himself repeating the same advice repeatedly.
The Advent of agent.md
The solution came with agent.md, a file configured to fine-tune coding style. By incorporating it at the start of each session, Fabien automated repetitive suggestions, significantly improving code quality.
Here are some directives from agent.md:
- Avoid magic numbers by using descriptive constants.
- Reduce code indentation by using early returns.
- Keep function names short, less than 30 characters.
- Add concise comments to explain the what and why of a code block.
Impact and Benefits
Using agent.md transformed how Fabien and other developers interact with LLMs. The produced code is cleaner, more understandable, and closer to production standards. A recent study found that 78% of developers using similar guides reported notable improvements in code quality.
Conclusion
For those looking to make the most of LLMs without sacrificing code quality, agent.md is a powerful solution. By integrating these practices, you can not only accelerate development but also ensure optimal code quality.
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