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skill-lang-python

Apply Python 3.11+ best practices including type hints, dataclasses, and async/await.

4|Updated Mar 31, 2026
Search Tags:#pydantic#python#async#code-quality#error-handling#dataclasses#typing
One-click install
npx skills add https://github.com/ryan-nguyen-01/agent-platform --skill skill-lang-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-lang-python
Source: https://github.com/ryan-nguyen-01/agent-platform/tree/main/.claude/skills/skill-lang-python
Command: npx skills add https://github.com/ryan-nguyen-01/agent-platform --skill skill-lang-python

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps Python developers write modern, robust, and maintainable code by adhering to best practices for typing, dataclasses, async/await, and clean coding patterns in Python 3.11+.

Core Features & Use Cases

  • Type hints and TypeAlias usage for explicit, self-describing APIs.
  • Dataclasses and Pydantic models for reliable data modeling and validation.
  • Async/await patterns with clear error handling for scalable, concurrent code.
  • Consistent naming, imports, and code structure to improve readability and maintainability.
  • Use Case: Build a small service with typed models, asynchronous I/O, and validated inputs across layers.

Quick Start

Create a minimal Python 3.11+ module that demonstrates typed functions, a dataclass model, and an async function.

Frequently Asked Questions about skill-lang-python

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

FAQPage Schema
How do I write type-safe Python data models with validation?▼

Type-safe Python data models are built using type hints and TypeAlias for explicit APIs, combined with dataclasses for structure or Pydantic models for reliable validation and maintainable code.

What is the best way to handle errors in async Python workflows?▼

The best way to handle errors in async Python is applying async/await patterns with clear, explicit error handling mechanisms, ensuring scalable and concurrent code execution across asynchronous I/O operations.

Does this Python best practices approach work with older versions?▼

This Python best practices approach targets Python 3.11 and above. It uses modern features like TypeAlias and advanced typing capabilities, making it suitable for projects seeking maintainable, type-safe code.

How do I structure Python code for better maintainability across internal APIs?▼

Structure Python code for maintainability by applying consistent naming, organizing imports, and using type hints across internal APIs. This clean code pattern improves overall readability and project structure.

Pydantic vs dataclasses for Python data modeling: which should I use?▼

Dataclasses provide reliable, standard data modeling structures, while Pydantic adds explicit validation. Use dataclasses for simple models and Pydantic when your internal APIs require strictly validated inputs.

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