aii_python

Enforce Python coding standards for experiment and evaluation scripts.

1|Updated Feb 27, 2026
One-click install
npx skills add https://github.com/AMGrobelnik/ai-inventor-old3 --skill aii-python
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aii_python
Source: https://github.com/AMGrobelnik/ai-inventor-old3/tree/main/.claude/skills/aii_python
Command: npx skills add https://github.com/AMGrobelnik/ai-inventor-old3 --skill aii-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enforces consistent Python coding standards for experiment and evaluation scripts, ensuring maintainability, readability, and robust error handling.

Core Features & Use Cases

  • Environment Setup: Guides users on setting up Python environments using uv and managing dependencies.
  • Logging: Implements structured logging with loguru for detailed tracking and debugging.
  • Error Handling: Mandates robust error handling with try/except blocks and the @logger.catch decorator.
  • Code Structure: Promotes best practices like pathlib, type hints, and keyword arguments.
  • Use Case: Ensure all data science experiments follow a uniform structure, making it easier to share, debug, and reproduce results across a team.

Quick Start

Follow the provided script pattern to create a new experiment script.

Frequently Asked Questions about aii_python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I standardize Python experiment scripts for machine learning projects?

Standardize Python experiment scripts by enforcing consistent coding standards for environment setup, logging, error handling, and code structure. This ensures reproducibility and maintainability across data science and machine learning projects.

What is the best way to implement structured logging in Python evaluation scripts?

Structured logging in Python evaluation scripts is implemented using loguru for detailed tracking and debugging. This approach enforces consistent logging standards, making it easier to monitor experiments and reproduce results across teams.

How do I set up robust error handling for Python data science experiments?

Robust error handling for Python data science experiments is achieved using try/except blocks and the @logger.catch decorator. This approach mandates explicit exceptions to ensure scripts fail predictably and maintain readability.

Does this Python coding standard require specific environment setup tools?

Yes, the Python coding standard requires setting up environments using uv for dependency management. This ensures consistent environment configuration across experiment scripts, satisfying reproducibility requirements for data science projects.

What modern Python practices are enforced for experiment and evaluation scripts?

Modern Python practices enforced for experiment scripts include using pathlib for file operations, type hints for clarity, and keyword arguments. These standards promote readable, maintainable code structures for data science and machine learning projects.