python-project-structure

Define Python module boundaries and public APIs with __all__.

Updated Apr 25, 2026

Search Tags:#testing #python #packaging #project-structure #public-api #module-architecture

Ready to run

npx skills add https://github.com/quangkmhd/Vietstock_Agent --skill python-project-structure-quangkmhd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-project-structure
Source: https://github.com/quangkmhd/Vietstock_Agent/tree/main/.agents/skills/python-project-structure
Command: npx skills add https://github.com/quangkmhd/Vietstock_Agent --skill python-project-structure-quangkmhd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams organize Python codebases by enforcing a well-defined project structure, explicit module boundaries, and clear public APIs. It guides decisions on where to place code, tests, and documentation to improve maintainability and readability.

Core Features & Use Cases

Explicit public APIs with all to define module boundaries.
Flat vs nested directory guidance to balance navigability.
Guidelines for test placement and packaging.

Quick Start

Create a starter layout following the example to establish consistent naming and packaging conventions.

Frequently Asked Questions about python-project-structure

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

FAQPage Schema

How do I structure a Python project to improve maintainability?▼

To structure a Python project for maintainability, apply a clean layout with explicit module boundaries, predictable imports, and consistent test placement. This approach scales from small scripts to libraries by defining clear directories and public APIs.

What is the best way to define public APIs in Python modules?▼

The best way to define public APIs in Python modules is by using the __all__ variable. This establishes explicit module boundaries, clarifying which specific functions or classes are exposed to external imports and hiding internal implementation details.

Should I use a flat or nested directory structure for my Python project?▼

Choosing between a flat or nested directory structure depends on balancing navigability with module boundaries. Flat layouts suit small scripts, while nested directories help larger libraries scale by organizing predictable imports and separating tests.

How do I organize tests in a Python project consistently?▼

To organize tests consistently in a Python project, follow established guidelines for test placement that align with your module architecture. Consistent test layouts improve onboarding and maintain predictable imports across the codebase.

How do I scale a Python script into a full library package?▼

To scale a Python script into a library, transition to a defined project structure with concise packaging decisions. Define explicit modules, establish public interfaces using __all__, and implement consistent test layouts to ensure maintainability.

Why do I need explicit module boundaries in my Python codebase?▼

Explicit module boundaries in a Python codebase are needed to improve readability and onboarding. By defining clear public interfaces and predictable imports, you prevent circular dependencies and ensure long-term maintainability as the project grows.