
Fastapi Development
This covers the FastAPI fundamentals you need to ship production async Python APIs. You get guides for Pydantic validation, SQLAlchemy async queries, JWT auth, dependency injection, and the full application setup with CORS and middleware. The reference docs show actual implementations for service layers and route handlers, not just conceptual stuff. The best practices section is solid on what to avoid, like returning raw database models or trusting unvalidated input. If you're building a modern Python backend and want type safety with automatic OpenAPI docs, this gets you past the boilerplate quickly. Assumes you already know why you want FastAPI over Flask or Django.
- skills.sh installs
- 624
- GitHub stars
- 379
- last updated
- Aug 21, 2026
Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill fastapi-development --agent claude-codeInstalls into .claude/skills of the current project.
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FastAPI Development
Table of Contents
Overview
Create fast, modern Python APIs using FastAPI with async/await support, automatic API documentation, type validation using Pydantic, dependency injection, JWT authentication, and SQLAlchemy ORM integration.
When to Use
- Building high-performance Python REST APIs
- Creating async API endpoints
- Implementing automatic OpenAPI/Swagger documentation
- Leveraging Python type hints for validation
- Building microservices with async support
- Integrating Pydantic for data validation
Quick Start
Minimal working example:
# main.py
from fastapi import FastAPI, HTTPException, status
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
import logging
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
Sponsors
Details
- Repository
- github.com/aj-geddes/useful-ai-prompts
- Categories
- Backend & APIs, Security, DevOps & CI/CD, Documentation, AI & Agent Building, Debugging, Python
- First seen
- Jun 3, 2026
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