How does this skill handle code quality?

It provides built-in commands for linting with Ruff, type checking with Pyright, and running automated smoke tests with Pytest to maintain production-grade ML code.

What is the purpose of the python-conventions skill?

It enforces a standardized layout and workflow for Python experiments, ensuring consistency in directory structure, configuration handling, and stage-based execution.

How are experiments executed using this skill?

Experiments are invoked using 'uv run' to execute specific stage modules (e.g., stage_entrypoint.py) coupled with JSON-based configuration files.

Can I use this for LaTeX integration?

Yes, the skill includes specific conventions for generating plots and assets that are stored in dedicated paths for easy inclusion in LaTeX documents.

Python Experiment Conventions

Name: Python Experiment Conventions
Author: JoernStoehler

byJoernStoehler

データサイエンスとML

Standardizes the development of Python machine learning experiments using specific layout, execution, and asset management patterns.

概要

This skill provides a structured framework for managing Python experiments within the python_viterbo package. It automates adherence to project-specific conventions, including experiment directory layouts, stage-based entrypoints, and configuration management. By integrating linting via Ruff, type checking with Pyright, and testing with Pytest, it ensures high code quality and consistency across data science workflows. Additionally, it streamlines the generation of LaTeX-compatible plots and assets, making it an essential tool for researchers and developers moving from experimentation to publication-ready output.

主な機能

Dedicated asset management for LaTeX-ready plot generation
Integrated smoke and targeted testing workflows with Pytest
Streamlined experiment execution via uv and JSON configurations
Standardized experiment directory layout and stage entrypoint patterns
0 GitHub stars
Automated linting and formatting using Ruff and Pyright

ユースケース

Generating and managing data artifacts and LaTeX-compatible plots for research documentation
Enforcing code quality and ML best practices across scientific Python codebases
Organizing new machine learning experiments within the Viterbo repository structure

概要

主な機能

Dedicated asset management for LaTeX-ready plot generation
Integrated smoke and targeted testing workflows with Pytest
Streamlined experiment execution via uv and JSON configurations
Standardized experiment directory layout and stage entrypoint patterns
0 GitHub stars
Automated linting and formatting using Ruff and Pyright

ユースケース

Generating and managing data artifacts and LaTeX-compatible plots for research documentation
Enforcing code quality and ML best practices across scientific Python codebases
Organizing new machine learning experiments within the Viterbo repository structure