Does this skill help with database connections?

Yes, it includes advanced patterns for SQLAlchemy async sessions, engine disposal, and Redis connection management within the application lifespan.

What version of FastAPI does this skill support?

It is designed for modern FastAPI versions following 2026 standards, specifically leveraging Pydantic v2 and the asynccontextmanager for application lifespan events.

Are there health check patterns included?

Yes, it includes a standardized health router to verify database and cache readiness, suitable for Kubernetes liveness and readiness probes.

Can I use this for custom middleware?

Absolutely; it provides production-ready templates for Request ID tracking, execution timing, structured logging, and CORS configuration.

How does it handle application configuration?

It utilizes Pydantic Settings for type-safe environment variable management, including advanced field validation and secret handling.

FastAPI Advanced Patterns

Name: FastAPI Advanced Patterns
Author: yonatangross

byyonatangross

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Desarrollo de API

Implements production-ready FastAPI 2026 patterns including modern lifespan management, dependency injection, and high-performance middleware.

Acerca de

The FastAPI Advanced skill empowers developers to build scalable, high-performance Python backends using current best practices. It provides structured patterns for managing application lifespans via async context managers, sophisticated dependency injection for database and service layers, and robust Starlette-based middleware for logging, timing, and security. Designed for modern standards, it helps developers avoid common anti-patterns like global state or blocking the event loop while ensuring type safety with Pydantic v2 settings and comprehensive health monitoring.

Características Principales

Modern Async Lifespan Context Managers
Custom Middleware for Logging and Observability
Tiered Dependency Injection Patterns
29 GitHub stars
Type-safe Pydantic Settings Management
Standardized Health and Readiness Checks

Casos de Uso

Architecting high-concurrency Python microservices with resource-aware lifespans.
Configuring secure, type-safe application settings and async database connections.
Implementing structured logging and request-response interceptors.

Acerca de

Características Principales

Modern Async Lifespan Context Managers
Custom Middleware for Logging and Observability
Tiered Dependency Injection Patterns
29 GitHub stars
Type-safe Pydantic Settings Management
Standardized Health and Readiness Checks

Casos de Uso

Architecting high-concurrency Python microservices with resource-aware lifespans.
Configuring secure, type-safe application settings and async database connections.
Implementing structured logging and request-response interceptors.