gerboni-backend

Develop FastAPI e-commerce backends with Python, SQLAlchemy, and Pydantic.

Updated Feb 3, 2026

Search Tags:#fastapi #python #backend #api development #sqlalchemy #ecommerce

Ready to run

npx skills add https://github.com/linardsb/GERBONI --skill gerboni-backend

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gerboni-backend
Source: https://github.com/linardsb/GERBONI/tree/main/.claude/skills/gerboni-backend
Command: npx skills add https://github.com/linardsb/GERBONI --skill gerboni-backend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework and set of standards for developing a production-ready FastAPI backend for the Gerboni e-commerce platform, ensuring consistency, security, and maintainability.

Core Features & Use Cases

Structured Development: Enforces a three-layer architecture (API, Service, Data) and a service layer pattern for clean business logic.
Robustness & Security: Implements domain exceptions, strict authentication dependencies, rate limiting, and security middleware.
Use Case: When adding a new product management API endpoint, use this Skill to define the Pydantic schemas, SQLAlchemy models, service logic, and API routes following established patterns and ensuring proper transaction management and error handling.

Quick Start

Use the gerboni-backend skill to create a new API endpoint for managing user profiles.

Frequently Asked Questions about gerboni-backend

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

FAQPage Schema

How do I structure a FastAPI e-commerce backend for maintainability?▼

A FastAPI e-commerce backend should follow a strict three-layer architecture: API, Service, and Data. This separation enforces a service layer pattern for clean business logic, ensuring maintainable and scalable code.

How do I create a new API endpoint in FastAPI with SQLAlchemy and Pydantic?▼

Define Pydantic schemas for validation, SQLAlchemy models for database mapping, service logic for business rules, and API routes. This pattern ensures proper transaction management and error handling across the e-commerce backend.

What's the best way to handle authentication and security in a FastAPI backend?▼

Handle FastAPI security by implementing strict authentication dependencies, rate limiting, and security middleware. Additionally, use domain exceptions to manage errors securely and consistently across the e-commerce application.

Can I integrate AI agent tools into a FastAPI service layer?▼

Yes, you can integrate AI agent tools into a FastAPI service layer. The architecture supports combining service layer logic implementation with AI agent tool integration alongside standard SQLAlchemy database operations.

How do I test FastAPI e-commerce API endpoints and service logic?▼

Test FastAPI e-commerce endpoints and services using comprehensive testing strategies. Validate the API routes, service layer logic, and Pydantic schema design to ensure robust transaction management and error handling across all layers.

When should I use a service layer pattern in Python backend API development?▼

Use a service layer pattern in Python API development when separating business logic from routing and data access. It ensures clean transaction management and domain exception handling for complex applications like e-commerce platforms.