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python-backend-expert

Provide Django, FastAPI, and Flask backend API patterns for ORMs, validation, and testing.

6|Updated Feb 25, 2026
Search Tags:#pydantic#fastapi#python#backend#async#sqlalchemy#django
One-click install
npx skills add https://github.com/archibate/archibate-skills --skill python-backend-expert-archibate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-backend-expert
Source: https://github.com/archibate/archibate-skills/tree/main/old-skills/minor-skills/python-backend-expert
Command: npx skills add https://github.com/archibate/archibate-skills --skill python-backend-expert-archibate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides concrete, framework-specific patterns and best practices to reduce bugs, N+1 queries, race conditions, and slow async patterns when building Python backend APIs across Django, FastAPI, and Flask.

Core Features & Use Cases

  • Framework Patterns: Clear examples for FastAPI dependency injection, background tasks, lifespan events, and Pydantic validation.
  • ORM & Querying: SQLAlchemy 2.0 declarative models, async queries, eager loading strategies, and Django select_related/prefetch_related to prevent N+1.
  • Reliability & Testing: Guidance on atomic transactions, async timeouts, task groups, pytest fixtures, and mocking for robust production deployments.

Quick Start

Ask the python-backend-expert to review my API routes and propose code-level fixes for Pydantic models, async DB usage, and query optimizations.

Frequently Asked Questions about python-backend-expert

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

FAQPage Schema
How do I fix N+1 queries in FastAPI using SQLAlchemy 2.0 async sessions?▼

To fix N+1 queries in FastAPI with SQLAlchemy 2.0 async sessions, apply eager loading strategies like selectinload or joinedload within your declarative models to optimize database access and prevent performance bottlenecks.

What is the best way to handle Pydantic validation in FastAPI?▼

The best way to handle Pydantic validation in FastAPI is to implement custom Pydantic validators and dependency injection patterns, ensuring robust API request data validation and reducing input-related bugs across your backend.

How do I prevent race conditions in Django ORM transactions?▼

Prevent race conditions in Django ORM transactions by implementing atomic transactions and utilizing select_related or prefetch_related strategies to ensure reliable, concurrent database operations without query conflicts.

How do I set up pytest fixtures for async Flask API testing?▼

Set up pytest fixtures for async Flask API testing by configuring mocking and async timeout handling within your test suites, ensuring robust production readiness and reliable validation of your backend API endpoints.

Does this approach work with both Django and FastAPI background tasks?▼

Yes, this approach works with both Django and FastAPI background tasks by providing framework-specific patterns for lifespan events, task groups, and dependency injection to manage asynchronous operations reliably.

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