Does this skill support both SQL and NoSQL databases?

Yes, it includes comprehensive patterns and configurations for PostgreSQL, MySQL, SQLite, and MongoDB (via Beanie and Motor).

Does it include production best practices?

Yes, it covers multi-stage Docker builds, non-root user security, health checks, environment management with Pydantic-settings, and Gunicorn/Uvicorn worker optimization for high-load environments.

What types of projects can I build with this skill?

You can build everything from simple REST APIs and CRUD applications to complex microservices, full-stack backends, and specialized ML/AI model serving APIs using the included scaffolding scripts.

How does the project scaffolding work?

Use the included scaffold_project.py script to generate a complete project structure with your choice of database, testing framework, and Docker setup in seconds.

FastAPI Production Toolkit

Name: FastAPI Production Toolkit
Author: alijilani-dev

byalijilani-dev

Desarrollo de API

Accelerates FastAPI development by providing automated project scaffolding, database integration patterns, and production-ready deployment workflows.

Acerca de

This comprehensive Claude Code skill streamlines the entire lifecycle of FastAPI application development, from initial project scaffolding to production deployment. It offers a structured approach to building REST APIs, microservices, and ML-ready backends through pre-configured templates, robust database integration guides (supporting both SQL and NoSQL), and standardized testing patterns. By automating boilerplate generation and providing expert guidance on Dockerization, background task management, and file handling, it ensures developers follow industry best practices for building high-performance, scalable Python applications.

Características Principales

Standardized testing suite setup using Pytest with async and fixture support
Advanced feature templates for background tasks, file uploads, and cloud storage
Automated project scaffolding for simple APIs, microservices, and ML-ready backends
0 GitHub stars
Pre-configured database integration for PostgreSQL, MySQL, SQLite, and MongoDB
Production-optimized Docker configurations including multi-stage build patterns

Casos de Uso

Preparing a local FastAPI application for production-grade deployment via Docker and Gunicorn
Rapidly bootstrapping a new microservice architecture with standardized folder structures
Implementing complex database models and migrations in an existing FastAPI project

Acerca de

Características Principales

Standardized testing suite setup using Pytest with async and fixture support
Advanced feature templates for background tasks, file uploads, and cloud storage
Automated project scaffolding for simple APIs, microservices, and ML-ready backends
0 GitHub stars
Pre-configured database integration for PostgreSQL, MySQL, SQLite, and MongoDB
Production-optimized Docker configurations including multi-stage build patterns

Casos de Uso

Preparing a local FastAPI application for production-grade deployment via Docker and Gunicorn
Rapidly bootstrapping a new microservice architecture with standardized folder structures
Implementing complex database models and migrations in an existing FastAPI project