python-patterns

Standardize Python backend structure with FastAPI, async SQLAlchemy, and Pydantic.

1|Updated Dec 14, 2025

Search Tags:#api #pydantic #fastapi #python #backend #patterns #sqlalchemy

Ready to run

npx skills add https://github.com/komluk/scaffolding.template --skill python-patterns-komluk

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-patterns
Source: https://github.com/komluk/scaffolding.template/tree/main/stacks/python/.claude/skills/python-patterns
Command: npx skills add https://github.com/komluk/scaffolding.template --skill python-patterns-komluk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized and efficient approach to building Python backend applications using FastAPI, SQLAlchemy, and Pydantic, reducing boilerplate code and promoting best practices.

Core Features & Use Cases

Structured Project Layout: Enforces a clean separation of concerns with distinct layers for endpoints, services, repositories, models, and schemas.
Async Database Operations: Implements robust patterns for asynchronous database interactions with SQLAlchemy.
Pydantic Schema Design: Guides the creation of reusable and validated data models for requests and responses.
Use Case: When developing a new API endpoint for user management, this Skill ensures that routes, data validation, business logic, and database interactions are implemented in a consistent, maintainable, and scalable manner.

Quick Start

Use the python-patterns skill to generate a basic CRUD structure for a 'Product' resource using FastAPI and SQLAlchemy.

Frequently Asked Questions about python-patterns

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

FAQPage Schema

How do I structure a FastAPI backend project with SQLAlchemy and Pydantic?▼

Structure a FastAPI backend by enforcing a clean separation of concerns with distinct layers for endpoints, services, repositories, models, and schemas. This standardized project layout reduces boilerplate code and promotes maintainable API development.

What's the best way to implement async database operations in FastAPI?▼

The best way to implement async database operations in FastAPI is by using established asynchronous SQLAlchemy patterns. This approach facilitates robust, non-blocking database interactions that maintain scalability and high performance.

How does Pydantic schema design work for FastAPI requests and responses?▼

Pydantic schema design works by guiding the creation of reusable, validated data models for FastAPI requests and responses. This ensures strict data validation and type checking across API endpoints without adding boilerplate.

Can I generate a complete CRUD API structure for a resource using FastAPI?▼

Yes, you can generate a complete CRUD structure for a resource like 'Product' using FastAPI and SQLAlchemy. This ensures routes, data validation, business logic, and database interactions are consistently implemented in a scalable manner.

Do I need a layered architecture to build scalable Python APIs?▼

You need a layered architecture to build scalable Python APIs when you want to standardize development and reduce boilerplate. Separating routes, services, and data models ensures long-term maintainability for complex backend applications.

Why use established patterns for Python backend development instead of custom logic?▼

Use established patterns for Python backend development to address the need for maintainable and scalable API structures. Standardized patterns prevent inconsistent logic and reduce boilerplate code across complex FastAPI applications.