How does it ensure code quality?

The skill includes a built-in verification workflow that runs ruff for linting and formatting, along with pytest for unit and integration testing.

Why use a layered architecture?

Layering code into API, service, and repository layers keeps handlers thin and ensures that business logic is centralized and easily testable.

What technology stack does this skill use?

By default, it utilizes Python 3.10+, FastAPI, Pydantic v2, SQLAlchemy, Alembic, and the uv package manager for dependency management.

Does it handle database security?

It provides guidelines for transaction boundaries and explicit validation to ensure data safety, while also checking for secret leakage in logs.

Can I use this for existing projects?

Yes, while it has a default stack, the architectural principles of separation of concerns and layered design can be applied to any existing Python backend.

Backend Engineering Playbook

Name: Backend Engineering Playbook
Author: Tendo33

byTendo33

0•

Desarrollo de API

Implements production-grade backend APIs and services using modern Python standards and layered architectural boundaries.

The Backend Engineering Playbook provides a structured framework for building predictable, testable, and secure backend systems with Claude. It enforces a domain-driven, layered architecture (API, Service, Repository) to ensure clean separation of concerns and maintainability. Optimized for the modern Python ecosystem, it integrates industry-standard tools like FastAPI, Pydantic v2, and SQLAlchemy to deliver robust data validation and safe database transactions while maintaining high code quality through automated linting and testing workflows.

Características Principales

01Standardizes Python 3.10+ stack with FastAPI and Pydantic v2

02Automates code quality checks using uv, ruff, and pytest

03Enforces layered architecture (API -> Service -> Repository -> Domain)

040 GitHub stars

05Manages database migrations and transactions with SQLAlchemy and Alembic

06Prevents secret leakage and ensures stable error response handling

Casos de Uso

01Refactoring monolithic backend code into testable service layers

02Building scalable REST APIs with robust validation and error handling

03Implementing database-driven features with secure migration workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tendo33/python-template backend-engineering-playbook

For use in Claude.ai and ChatGPT

Características Principales

01Standardizes Python 3.10+ stack with FastAPI and Pydantic v2

02Automates code quality checks using uv, ruff, and pytest

03Enforces layered architecture (API -> Service -> Repository -> Domain)

040 GitHub stars

05Manages database migrations and transactions with SQLAlchemy and Alembic

06Prevents secret leakage and ensures stable error response handling

Casos de Uso

01Refactoring monolithic backend code into testable service layers

02Building scalable REST APIs with robust validation and error handling

03Implementing database-driven features with secure migration workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tendo33/python-template backend-engineering-playbook

For use in Claude.ai and ChatGPT