Comment by booty

I have had pretty decent success instructing OpenAI/Anthropic models to do this. By default, they don't care too much about human readability (which is understandable) but they're amenable to instruction.

- Basic style/linter rules (so you don't get 200-line functions, etc) paired w/ instructing your agent how to use them goes a long way (e.g., a sane rubocop.yml in your repo, plus a note in AGENTS.md telling the agents how to run rubocop, or whatever makes sense for your language) - I have a code review skill that specifically calls out human-readability concerns such as making sure that identifier names represent their contents and intended use

The other key IME is that you need to make readability/refactoring passes outside of a particular pull request. When working on a specific branch the LLMs will be somewhat myopic, and do stuff that looks pretty good in isolation, but when you zoom out and look at the bigger picture it's kind of a mess - for example, often they'll duplicate some code that existed elsewhere.

The LLMs are also pretty good at generating Mermaid charts and tracing execution flows, which is a great way to understand code. It doesn't help them to produce more readable code but it helps me to understand code I'm unfamiliar with.