api-design

Design and implement RESTful APIs with FastAPI routing and Pydantic models.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/101mare/skill-library --skill api-design-101mare
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: api-design
Source: https://github.com/101mare/skill-library/tree/main/skills/patterns/api-design
Command: npx skills add https://github.com/101mare/skill-library --skill api-design-101mare

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured approach to designing and implementing RESTful APIs using FastAPI, addressing common challenges in routing, data modeling, and error handling.

Core Features & Use Cases

  • FastAPI Project Structure: Outlines a maintainable directory layout for API projects.
  • Request/Response Models: Employs Pydantic for robust input validation and clear output serialization.
  • Error Handling: Implements a strategy for mapping domain exceptions to appropriate HTTP responses.
  • Dependency Injection: Demonstrates effective use of FastAPI's dependency system for managing resources.
  • Use Case: When starting a new microservice that needs to expose data via a REST API, use this Skill to set up the foundational structure, including endpoints for common CRUD operations and proper error handling.

Quick Start

Use the api-design skill to create a new FastAPI project with standard routing and response models.

Frequently Asked Questions about api-design

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

FAQPage Schema
How do I structure a FastAPI project for maintainable REST APIs?

A maintainable FastAPI project structure requires separating routing logic, Pydantic response models, and dependency injection. This approach ensures clear API contracts, robust input validation, and a maintainable codebase for your REST API endpoints.

What's the best way to handle input validation in FastAPI?

Input validation in FastAPI is best handled using Pydantic models for request data. Pydantic provides robust validation and clear output serialization, ensuring your API endpoints receive properly structured data before processing.

How do I map domain exceptions to HTTP responses in FastAPI?

Mapping domain exceptions to HTTP responses in FastAPI involves implementing an error handling strategy. This structure translates internal application errors into appropriate HTTP status codes, ensuring clients receive clear REST API feedback.

Can I use dependency injection for resource management in FastAPI?

Yes, FastAPI supports dependency injection for managing resources effectively. Using FastAPI's dependency system allows you to structure your REST API components cleanly, handling database connections and shared logic without tight coupling.

When do I need Pydantic response models for my REST API?

You need Pydantic response models whenever you want clear output serialization and robust input validation in your REST API. They define strict API contracts that structure the JSON data returned by your FastAPI endpoints.

Does this approach work for building new microservices with CRUD operations?

Yes, this FastAPI approach works for building new microservices that expose data via a REST API. It sets up the foundational structure including endpoints for common CRUD operations and proper error handling.