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A set of coding style guidelines for Vibe Coding or SWE-Agents that maximize efficiency and improve human readability.
| Date | Stars |
|---|---|
| 2026-07-31 | 491 |
| 2026-08-04 | 493 |
| 2026-08-06 | 493 |
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# The First AI Coding Style Guide
by Xinran Wang(lidangzzz)
[中文版](README_CN.md)
## Quick Start
Open the [`AI_Coding_Style_Guide_prompts.toml`](AI_Coding_Style_Guide_prompts.toml) file in your favorite text editor, and you will find a collection of prompts that can be used to guide large language models in compressing code. You can use these prompts to instruct large language models to follow the coding style guides and compress code according to the principles outlined in this document.
If you want to use these prompts in your own projects, you can copy the contents of the `AI_Coding_Style_Guide_prompts.toml` file and paste them into your own prompt management system. You can also modify the prompts to suit your specific needs.
If you want to load these prompts into your own prompt management system, you can use the following code snippet:
```python
import toml
from pathlib import Path
def load_prompts(file_path: str) -> dict:
"""Load prompts from a TOML file."""
return toml.load(Path(file_path))
prompts = load_prompts("AI_Coding_Style_Guide_prompts.toml")
```
## Introduction
When I use vibe coding or SWE Agent, I often encounter the issue of insufficient context window size. If we feed multiple source code files at once and the total length exceeds the context window, we are forced to selectively include only some of the files. This can cause the large language model to lose part of the necessary context.
Another issue is that most current vibe coding or SWE Agent tools charge based on token usage, because the API providers of large language models also charge these tools according to the number of tokens consumed. In this case, the longer the source code, the more tokens are required — not only leading to a linear increase in cost but also a linear increase in the time cost of token output.
To enable large language models to process more source code files, modules, and functionalities within a fixed context window — and to save more tokens — we have designed this collection of coding style guides. This collection includes both general coding style guides for common programming languages and specific guides tailored to particular languages and scenarios.
Our goal is to maximize code compression across various programming languages while maintaining a reasonable level of human readability, so as to conserve as much of the context window as possible while achieving a balance between compactness and readability.
## Principles
We created this set of coding style guides because we believe in three fundamental trends, which reflect our first-principles thinking in both software engineering and working with large language models:
1. The capabilities of large language models will continue to improve, gradually surpassing ordinary human programmers. Large language models are already capable of understanding these extremely compressed forms of code, and their comprehension will only get stronger.
2. The context window of large language models will always feel insufficient. Whether it's 32k, 64k, 256k, 1M tokens, or beyond, prompt engineers will always want to pack in more code and context. Whereas we could previously only fit two or three files, we may eventually aim to include dozens or even hundreds of files.
3. When vibe coding or SWE Agent tools take on the primary programming tasks, the importance of code readability gradually decreases. Humans lack the time or willingness to review all the code in detail; instead, they care more about whether the functionality is complete. To maximize the efficiency of vibe coding or SWE Agents, it becomes reasonable to sacrifice code readability to some extent — reducing unnecessary redundancy like overly long variable names, extra spaces, line breaks, tabs, etc.
4. The reliability of code should increasingly rely on fine-grained unit tests rather than the outcomes of human code review. Comprehensive unit tests can help ensure code quality across the codebase. HoweverExcerpt of 25,815 characters
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