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python-backend

Enforce PEP-8, type hinting, and Hexagonal Architecture for Python backends.

Updated Jan 30, 2026
Search Tags:#testing#pydantic#fastapi#python#backend#hexagonal architecture#flask
One-click install
npx skills add https://github.com/spallempati/AI-Studio --skill python-backend-spallempati
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-backend
Source: https://github.com/spallempati/AI-Studio/tree/main/skills/python/python-backend
Command: npx skills add https://github.com/spallempati/AI-Studio --skill python-backend-spallempati

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive standards and architectural guidance for developing secure, maintainable, and scalable Python backend services, addressing challenges in code quality, dependency management, and architectural design.

Core Features & Use Cases

  • Coding Standards: Enforces PEP-8 compliance, type hinting, and code structure best practices.
  • Architectural Patterns: Guides implementation of Hexagonal Architecture for clear separation of concerns.
  • Data Validation: Promotes Pydantic models for robust data validation and schema-driven development.
  • Testing: Mandates unit tests for public methods and outlines strategies for pytest.
  • Security: Emphasizes parameterized queries and secure handling of sensitive data.
  • Use Case: Develop a new microservice using FastAPI, ensuring all data models are Pydantic-based, business logic is isolated in the domain layer, and all API inputs/outputs are validated against OpenAPI schemas.

Quick Start

Apply the python-backend skill to refactor the existing Flask application to adhere to hexagonal architecture principles and implement Pydantic models for request validation.

Frequently Asked Questions about python-backend

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

FAQPage Schema
How do I implement hexagonal architecture in a FastAPI application?▼

Hexagonal architecture in FastAPI separates business logic into an isolated domain layer while routing handles API inputs and outputs. This isolates core logic from framework dependencies, ensuring maintainable Python backend services.

What is the best way to enforce data validation in Python backend services?▼

Data validation in Python backend services is enforced using Pydantic models for schema-driven development. This validates API inputs and outputs against defined schemas, securing data flow and maintaining code structure.

Can I refactor an existing Flask application to use hexagonal architecture?▼

Refactoring an existing Flask application to use hexagonal architecture is supported through brownfield integration strategies. This allows you to gradually migrate legacy code, isolating domain logic while maintaining existing API endpoints.

Does Python backend development require type hinting and PEP-8 compliance?▼

Python backend development requires PEP-8 compliance and type hinting to enforce coding standards. These practices ensure code quality, improve readability, and maintain secure dependency management across the project.

How do I write unit tests for public methods in a Python backend?▼

Unit tests for public methods in a Python backend are written using pytest strategies. The approach mandates testing all exposed domain logic to verify behavior and ensure secure, maintainable code.

When should I use parameterized queries in Python backend services?▼

Parameterized queries in Python backend services should be used whenever handling database interactions to prevent injection vulnerabilities. This security practice ensures sensitive data is handled securely across FastAPI or Flask applications.

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