fastapi-patterns

Design scalable FastAPI applications with routing, Pydantic models, and dependency injection.

46|8|Updated Mar 25, 2026
One-click install
npx skills add https://github.com/manikosto/claude-code-python-stack --skill fastapi-patterns-manikosto
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fastapi-patterns
Source: https://github.com/manikosto/claude-code-python-stack/tree/main/skills/fastapi-patterns
Command: npx skills add https://github.com/manikosto/claude-code-python-stack --skill fastapi-patterns-manikosto

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designing and implementing scalable FastAPI apps with proven architecture patterns and best practices.

Core Features & Use Cases

  • Routing patterns, API versioning, and modular routing
  • Pydantic models for data validation and serialization
  • Dependency injection, middleware, and error handling
  • WebSocket and background task support for real-time and async tasks
  • Testing patterns and project structure for maintainability

Quick Start

Start by scaffolding a FastAPI project and wiring the app to use the provided patterns.

Frequently Asked Questions about fastapi-patterns

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

FAQPage Schema
How do I structure a scalable FastAPI application with modular routing and API versioning?

Use modular routing and API versioning patterns to isolate features within your FastAPI application. This approach organizes endpoints into maintainable modules, ensuring robust API growth without routing conflicts.

How does dependency injection work in FastAPI for middleware and error handling?

Dependency injection in FastAPI isolates shared logic for middleware and error handling by injecting callable dependencies into route definitions. This mechanism centralizes validation and exception management across your application.

Can I use FastAPI for WebSocket APIs and asynchronous background tasks?

Yes, FastAPI supports WebSocket APIs and asynchronous background tasks for real-time communication. Proven architecture patterns integrate these features to manage concurrent connections and offload long-running async operations effectively.

What is the best way to handle data validation and serialization in FastAPI?

The best way to handle data validation and serialization in FastAPI is using Pydantic models. Define typed schemas to automatically validate incoming request data and serialize outgoing responses across your REST APIs.

What testing patterns should I use for a production-grade FastAPI project?

Use structured testing patterns that validate routing orchestration and dependency injection within your FastAPI project. Implementing these patterns ensures maintainability and verifies that API endpoints behave correctly under various conditions.