Common coding standards and guidelines for AI coding agents, derived from the Effective Programming Practices for Economists course.
This repository provides a unified set of instructions for AI coding agents working across multiple projects. The goal is to ensure consistent code quality, style, and best practices regardless of which project an AI agent is assisting with.
Reference CLAUDE.md in your project's AI agent configuration to apply these standards.
The file contains guidelines organized by topic:
- Background: OS, file systems, floating point, graph theory
- Tools: Shell and terminal usage
- Git: Version control best practices
- Python Installation/Execution: pixi, pytest, pytask
- Python Basics: Data types, functions, pathlib
- Debugging: Agans' rules, debugger usage
- Software Engineering: Naming, testing, error handling, pure functions
- Pandas: Functional data cleaning, dtypes, merging
- Scientific Computing: NumPy, vectorization, Numba
- Numerical Optimization: optimagic, algorithm selection
- Projects: pytask, directory structure, reproducibility
- Texts: Markdown, README writing
- Plotting: plotly.express, graph_objects
- Machine Learning/Econometrics: statsmodels, scikit-learn
These guidelines are extracted from course materials at: https://github.com/OpenSourceEconomics/epp_topics