python-engineering

Codify Python development best practices for writing, reviewing, and tooling.

Updated Apr 27, 2026

Search Tags:#testing #linting #python #code-review #typing #cli-tools #production-quality

Ready to run

npx skills add https://github.com/Immanuel-Aristotle/agents --skill python-engineering-immanuel-aristotle

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-engineering
Source: https://github.com/Immanuel-Aristotle/agents/tree/main/skills/python-engineering
Command: npx skills add https://github.com/Immanuel-Aristotle/agents --skill python-engineering-immanuel-aristotle

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Comprehensive guidelines to help Python developers write and review production-grade code, improving reliability, readability, and consistency across projects.

Core Features & Use Cases

Clear project structure guidance, including recommended src layout and testing conventions.
Best practices for error handling, typing, formatting, testing, and tooling integration (uv, ruff, mypy, pytest).
Use cases include building CLI tools, AI agents (LangGraph), and general Python development workflows.

Quick Start

Review code against these guidelines to ensure production-grade quality in Python projects.

Frequently Asked Questions about python-engineering

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

FAQPage Schema

How do I set up a production-grade Python project structure?▼

A production-grade Python project requires a recommended src layout, strict naming conventions, and a configured pyproject.toml. This ensures consistent structure, maintainability, and reliable packaging across CLI tools and general development workflows.

What are the best practices for Python error handling and typing?▼

Python error handling and typing best practices involve strict type checking using mypy and standardized formatting rules. Applying these practices ensures consistent, maintainable code and improves overall reliability across general Python development workflows.

How do I configure ruff, mypy, and pytest for Python code reviews?▼

Configuring ruff, mypy, and pytest requires specifying tooling integration within your pyproject.toml. This setup codifies best practices for formatting, typing, and testing to ensure production-grade quality during code reviews.

Can I use these Python tooling guidelines for building LangGraph AI agents?▼

Yes, these production-grade Python guidelines apply directly to building AI agents with LangGraph. They provide the necessary structure, error handling, and testing conventions required for reliable agent development workflows.

What is the recommended way to handle Python packaging configurations?▼

The recommended way to handle Python packaging configurations is by defining settings within pyproject.toml. This standardizes project structure, tooling integration like uv, and ensures consistent maintainable code across environments.