Why is the 150-500 line range recommended for files?

This range is considered the 'sweet spot' for both human comprehension and AI code editors. It ensures that Claude and other AI tools can process the entire file context without truncation or loss of focus.

When is the best time to split a Python module?

You should split a file if it exceeds 500 lines, contains multiple unrelated concerns, causes scroll fatigue, or if the associated tests have become difficult to organize.

Does this skill provide a specific folder structure?

Yes, it recommends a standard feature-based structure including core logic, models, handlers, and utilities to isolate side effects from pure functions.

Can I use this for non-Python projects?

While the core principles of modularity are universal, this skill provides specific implementation patterns, naming conventions (snake_case), and references tailored for Python development.

How does this skill help with AI-assisted development?

By keeping modules focused and right-sized, AI tools can more accurately analyze dependencies and logic without hitting token limits, leading to higher-quality code generation and debugging.

Modular Python Organization

Name: Modular Python Organization
Author: scooter-lacroix

byscooter-lacroix

開発者ツール

Optimizes Python codebases by enforcing modular organization and maintainable file sizes for better AI-assisted development.

概要

The Modular Code skill for Claude Code provides a structured framework for refactoring large Python files into maintainable, domain-driven modules. By adhering to an optimal 'sweet spot' of 150-500 lines per file, it ensures that code remains easily digestible for both human developers and AI context windows, preventing tool truncation and cognitive overload. It provides actionable guidance on identifying natural split points, implementing clean package hierarchies, and executing incremental refactoring strategies to transform monolithic scripts into professional-grade architectures.

主な機能

0 GitHub stars
Standardized Python package structure templates (models, handlers, utils)
Step-by-step refactoring workflows for large legacy modules
Heuristics for identifying natural split points by domain or abstraction
Standardized file size guidelines for optimal AI and human readability
Best practices for module naming and minimal __init__.py usage

ユースケース

Standardizing module organization and naming conventions across development teams
Refactoring monolithic Python scripts into maintainable package structures
Optimizing codebase architecture for better context retention in AI coding assistants

概要

主な機能

0 GitHub stars
Standardized Python package structure templates (models, handlers, utils)
Step-by-step refactoring workflows for large legacy modules
Heuristics for identifying natural split points by domain or abstraction
Standardized file size guidelines for optimal AI and human readability
Best practices for module naming and minimal __init__.py usage

ユースケース

Standardizing module organization and naming conventions across development teams
Refactoring monolithic Python scripts into maintainable package structures
Optimizing codebase architecture for better context retention in AI coding assistants