Tired of Explaining Code You Didn't Even Write? Let LLMs Do It.
We've all been there. A teammate walks over (or pings you on Slack) with a massive chunk of unfamiliar code and asks: "Hey, can you explain what this does? Also, why does this specific edge case work like this?" Here’s the catch: You didn't write that code. You've never seen it before. Now you’re stuck spending half your afternoon digging through undocumented functions, tracking down call graphs,…
Most of us have experienced the frustration of a colleague asking for an explanation of code we didn't author. Imagine receiving a complex snippet and being told, "Can you explain what this does? Also, why does this particular edge case function correctly?" The problem is that we haven't written or seen this code before. Now we're forced to spend part of our day digging through undocumented functions, following call graphs, and attempting to reverse-engineer someone else's legacy logic in order to provide detailed answers.
Who wouldn't want to avoid such tedious manual code spelunking? Especially when language models like large language models (LLMs) are readily available. Why not let an LLM handle the heavy lifting? This is where pystdoc comes in - a tool designed to completely automate the process of generating structured documentation and explanations for unfamiliar code.
To use pystdoc, simply install it via pip, configure your LLM settings, and run it against the target directory where the unfamiliar code resides. With just one command, pystdoc will analyze the code, understand symbol relationships and call graphs, and leverage LLMs to produce clear, comprehensive explanations. No need to decipher every line yourself.
For those interested in advanced configurations, supported languages and parsers, caching options, and engine settings, the project's documentation provides all the details: PyPI, GitHub. Give pystdoc a try the next time someone asks you to untangle a mystery codebase. Feedback and pull requests are always welcome.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
