python-fastapi-developer

Create FastAPI project skeletons with domain-driven modular structure.

Updated Dec 22, 2025
One-click install
npx skills add https://github.com/senior-sigan/llm-skills --skill python-fastapi-developer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-fastapi-developer
Source: https://github.com/senior-sigan/llm-skills/tree/main/skills/python-fastapi-developer
Command: npx skills add https://github.com/senior-sigan/llm-skills --skill python-fastapi-developer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Solves the problem of building robust, maintainable FastAPI backends in Python.

Core Features & Use Cases

  • Modular project structure with domain-driven layout inspired by Netflix Dispatch
  • Best-practice guidance for async I/O, Pydantic validation, and SQLAlchemy integration
  • Production-ready patterns for authentication, testing, and deployment

Quick Start

Create a new FastAPI project skeleton following the recommended domain-driven structure and practices outlined in this guide.

Frequently Asked Questions about python-fastapi-developer

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

FAQPage Schema
How do I structure a FastAPI backend for maintainability and scale?▼

A maintainable FastAPI backend benefits from a domain-driven modular structure inspired by Netflix Dispatch, separating concerns into domains. This layout ensures scalable architecture for complex API projects handling async I/O and data validation.

What is the best way to integrate Pydantic validation with SQLAlchemy in FastAPI?▼

The best way to integrate Pydantic validation with SQLAlchemy in FastAPI involves using best-practice guidance for async I/O operations. This combination ensures production-ready data validation and robust persistence workflows within your API project.

Does this FastAPI guide cover authentication and testing workflows?▼

Yes, this FastAPI guide covers authentication and testing workflows. It provides production-ready patterns for securing your API endpoints and implementing a strong linting and testing toolbox to ensure production-grade quality.

Can I use this approach for an existing Python backend or is it only for new projects?▼

This approach applies primarily to creating a new FastAPI project skeleton following the recommended domain-driven structure. While it focuses on new setups, the production-ready patterns for async I/O and Pydantic validation can inform existing backend refactoring.

How does async I/O improve FastAPI backend performance?▼

Async I/O improves FastAPI backend performance by allowing concurrent non-blocking database and network operations. Applying best-practice guidance for async I/O ensures your API projects handle high-throughput requests efficiently using modern Python typing.