coding-standards

Enforce universal coding standards for Python development across teams.

2|Updated Jan 21, 2026

Search Tags:#documentation #python #code-quality #design-principles #coding-standards #naming-conventions

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npx skills add https://github.com/anton-dovnar/cursor --skill coding-standards-anton-dovnar

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: coding-standards
Source: https://github.com/anton-dovnar/cursor/tree/main/skills/coding-standards
Command: npx skills add https://github.com/anton-dovnar/cursor --skill coding-standards-anton-dovnar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Universal coding standards and best practices to improve readability, maintainability, and consistency across Python projects.

Core Features & Use Cases

Readability First: Code is read more than written; clear variable and function names; self-documenting code preferred over comments; consistent formatting.
KISS (Keep It Simple, Stupid): Emphasizes simple solutions; avoids over-engineering; no premature optimization; easy to understand over clever code.
DRY (Don't Repeat Yourself): Extract common logic into functions; reusable components; share utilities across modules; avoid copy-paste.
YAGNI (You Aren't Gonna Need It): Avoid building features before they're needed; avoid speculative generality; add complexity only when required; start simple and refactor.
SOLID Principles: Design guidelines for maintainable, scalable, and testable code; clear abstractions; loose coupling; long-term evolution.

Core Features & Use Cases

Python Standards: Variable Naming; Function Naming; Immutability Pattern; Error Handling; Async/Await Best Practices; Type Safety.
Class and Function Design: Class Structure; Decorators and Utilities; State Management; Conditional Logic.
API Design Standards: REST API conventions; Response Format; Input Validation.
File Organization & Naming: Project structure and snake_case naming.
Comments & Documentation: When to Comment; Docstrings for Public APIs; Examples.
Performance Best Practices: Memoization; Lazy Loading; Database Queries.
Testing Standards: Test Structure; Test Naming.
Code Smell Detection: Long Functions; Deep Nesting; Magic Numbers.
Documentation & Example Patterns: Guides and examples to illustrate guidelines.

Quick Start

Audit your Python modules and refactor them to align with the documented standards.

Frequently Asked Questions about coding-standards

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

FAQPage Schema

What are the best Python coding standards for improving code readability and maintainability?▼

Python coding standards for readability prioritize clear variable and function names, consistent formatting, and self-documenting code over excessive comments. Applying SOLID, KISS, and DRY principles ensures maintainable, scalable, and testable codebases.

How do I apply SOLID and DRY design principles to a Python codebase?▼

To apply SOLID and DRY principles in Python, extract common logic into reusable functions, enforce loose coupling with clear abstractions, and avoid speculative generality. This prevents copy-paste duplication and ensures long-term scalable design.

What naming conventions should I use for Python functions, classes, and file structure?▼

Python naming conventions mandate snake_case for file names and variables, with specific structural rules for class and function design. Following these project structure and naming standards improves consistency and readability across development teams.

How do I structure REST API response formats and input validation in Python?▼

REST API design standards in Python require consistent response formats and strict input validation. Following these conventions alongside async/await best practices ensures reliable API endpoints and predictable state management.

Does this Python coding standards guide cover async patterns and error handling?▼

Yes, the Python coding standards explicitly address async/await best practices, error handling, and type safety. These guidelines ensure robust state management and reliable conditional logic across complex asynchronous codebases.

How do I detect code smells like deep nesting and magic numbers in Python?▼

Code smell detection in Python targets long functions, deep nesting, and magic numbers to enforce KISS and YAGNI principles. Refactoring these elements using decorators and utilities yields simple, easy-to-understand solutions without premature optimization.