fastapi

Guide FastAPI API development with Pydantic models and dependency injection.

1|Updated Mar 11, 2026
One-click install
npx skills add https://github.com/91zgaoge/memoh-X --skill fastapi-91zgaoge
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fastapi
Source: https://github.com/91zgaoge/memoh-X/tree/main/openviking-packages/fastapi/.agents/skills/fastapi
Command: npx skills add https://github.com/91zgaoge/memoh-X --skill fastapi-91zgaoge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps developers write clean, efficient, and up-to-date FastAPI code by enforcing best practices and leveraging the latest features.

Core Features & Use Cases

  • Best Practices Enforcement: Guides users on using Annotated, proper dependency injection, and response models.
  • CLI Usage: Demonstrates how to use the fastapi CLI for development and production.
  • Code Refactoring: Helps update existing FastAPI code to align with current standards.
  • Use Case: Refactor an existing FastAPI endpoint to use Annotated for path parameters and include a response_model for better documentation and security.

Quick Start

Use the fastapi skill to refactor the provided code to use Annotated for path parameters and include a response model.

Frequently Asked Questions about fastapi

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

FAQPage Schema
How do I refactor FastAPI code to use Annotated for path parameters?

To refactor FastAPI code, use the `Annotated` type hint for path parameters and include an explicit `response_model` in your endpoint definition to ensure robust API design and better documentation.

What are the best practices for FastAPI dependency injection?

FastAPI dependency injection best practices involve using the `Annotated` keyword for parameters and defining explicit response models to enforce clean, efficient, and up-to-date code structure.

How do I integrate Pydantic models in FastAPI for API development?

Integrate Pydantic models in FastAPI by defining them as explicit `response_model` arguments in your endpoints, ensuring data validation, serialization, and robust API design.

Does FastAPI CLI work for both development and production environments?

Yes, the FastAPI CLI can be used for both development and production environments, providing a standardized way to run and manage your API applications efficiently.

Why use explicit response models in FastAPI endpoints?

Explicit response models in FastAPI endpoints improve API documentation and security by strictly defining the output data structure, filtering out unnecessary fields from the response.

What is the best way to optimize FastAPI performance?

Optimize FastAPI performance by adhering to current standards, utilizing modern Python features like `Annotated`, and applying efficient code structure patterns for API development.