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

Data Science & ML

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

About

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.

Key Features

Optimizes pandas operations with method chaining and explicit copies
Mandates comprehensive type hints for all function signatures
Implements robust error handling with specific exception types
0 GitHub stars
Enforces PEP 8 compliance and organized import hierarchies
Standardizes NumPy/Google style docstrings for better documentation

Use Cases

Refining and cleaning AI-generated Python code for team collaboration
Standardizing code quality across data science pipelines and ML workflows
Converting quick notebook experiments into production-ready scripts

About

Key Features

Optimizes pandas operations with method chaining and explicit copies
Mandates comprehensive type hints for all function signatures
Implements robust error handling with specific exception types
0 GitHub stars
Enforces PEP 8 compliance and organized import hierarchies
Standardizes NumPy/Google style docstrings for better documentation

Use Cases

Refining and cleaning AI-generated Python code for team collaboration
Standardizing code quality across data science pipelines and ML workflows
Converting quick notebook experiments into production-ready scripts