What is 'AI slop' in Python?

AI slop refers to generic, unoptimized code patterns often produced by LLMs, such as using generic names like 'df' or 'data', omitting type hints, and ignoring PEP 8 standards.

Is it compatible with Black and Ruff?

Yes, it specifically encourages the use of industry-standard tools like Black, Ruff, and Mypy to format and validate the code it helps produce.

Does it support Jupyter notebooks?

Yes, the skill applies to both .py scripts and .ipynb notebooks, ensuring that even experimental data science code follows professional standards.

How does this skill improve pandas code?

It enforces method chaining, the use of .copy() to avoid SettingWithCopy warnings, and descriptive variable naming instead of generic placeholders like 'df'.

Can it refactor existing legacy code?

While primarily used for new or AI-generated code, you can use it to review and refactor inherited code to meet modern type-safe and PEP 8 standards.

Python Anti-Slop

Name: Python Anti-Slop
Author: emraher

byemraher

0•

数据科学与机器学习

Enforces production-grade Python standards by eliminating generic AI coding patterns through PEP 8 compliance, rigorous type hinting, and pandas best practices.

Python Anti-Slop is a specialized Claude Code skill designed to transform generic, low-quality AI outputs into robust, maintainable production code. It addresses common issues like ambiguous variable naming, missing type annotations, and inefficient pandas operations. By enforcing strict adherence to PEP 8, NumPy-style docstrings, and explicit error handling, this skill ensures that data science pipelines and general Python scripts are readable, type-safe, and ready for professional code reviews or deployment.

主要功能

01Optimizes pandas operations with method chaining and explicit copies

02Mandates comprehensive type hints for all function signatures

03Implements robust error handling with specific exception types

040 GitHub stars

05Enforces PEP 8 compliance and organized import hierarchies

06Standardizes NumPy/Google style docstrings for better documentation

使用场景

01Refining and cleaning AI-generated Python code for team collaboration

02Standardizing code quality across data science pipelines and ML workflows

03Converting quick notebook experiments into production-ready scripts

主要功能

01Optimizes pandas operations with method chaining and explicit copies

02Mandates comprehensive type hints for all function signatures

03Implements robust error handling with specific exception types

040 GitHub stars

05Enforces PEP 8 compliance and organized import hierarchies

06Standardizes NumPy/Google style docstrings for better documentation

使用场景

01Refining and cleaning AI-generated Python code for team collaboration

02Standardizing code quality across data science pipelines and ML workflows

03Converting quick notebook experiments into production-ready scripts