Does it support background tasks?

Yes, it includes implementation patterns and workers for TaskIQ to handle asynchronous background processing.

How do I verify my code with this skill?

You can use 'just' commands like 'just typecheck' for mypy, 'just fmt' for ruff formatting, and 'just test' for the pytest suite.

What architecture does this skill follow?

It follows a hexagonal architecture, separating concerns into API routers, business logic services, and data CRUD layers for better maintainability.

What database tools are used?

The skill is optimized for SQLModel, which combines SQLAlchemy and Pydantic for robust data modeling and validation in Python.

FastAPI Backend & Hexagonal Architecture

Name: FastAPI Backend & Hexagonal Architecture
Author: djimontyp

bydjimontyp

API開発

Streamlines the development of robust FastAPI backends using SQLModel and async service patterns for complex data processing.

概要

This skill provides a structured framework for building high-performance Python backends within the Pulse Radar ecosystem. It enforces a hexagonal architecture that separates concerns between API routers, business logic services, and database CRUD operations. With pre-defined patterns for async operations, WebSocket integration, and background task management via TaskIQ, it helps developers maintain consistency across a large codebase featuring dozens of endpoints and sophisticated data models for AI-driven insights and message classification.

主な機能

Hexagonal architecture enforcement (Router-Service-CRUD pattern)
Async FastAPI implementation with SQLModel integration
Strict verification workflows via mypy, ruff, and pytest
Pre-defined schemas for message processing and LLM provider management
0 GitHub stars
Integrated background task patterns using TaskIQ

ユースケース

Building scalable REST APIs with strictly typed service layers
Implementing complex data models for AI/ML processing pipelines
Developing real-time applications using WebSocket endpoints and background workers

概要

主な機能

Hexagonal architecture enforcement (Router-Service-CRUD pattern)
Async FastAPI implementation with SQLModel integration
Strict verification workflows via mypy, ruff, and pytest
Pre-defined schemas for message processing and LLM provider management
0 GitHub stars
Integrated background task patterns using TaskIQ

ユースケース

Building scalable REST APIs with strictly typed service layers
Implementing complex data models for AI/ML processing pipelines
Developing real-time applications using WebSocket endpoints and background workers