fastapi-expert

Generate production-grade FastAPI async REST API guidance with Pydantic v2 and JWT/OAuth2.

1|1|Updated Feb 2, 2026
One-click install
npx skills add https://github.com/corticalstack/claude-code-flow --skill fastapi-expert-corticalstack
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fastapi-expert
Source: https://github.com/corticalstack/claude-code-flow/tree/main/.claude/skills/fastapi-expert
Command: npx skills add https://github.com/corticalstack/claude-code-flow --skill fastapi-expert-corticalstack

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you avoid common pitfalls and build reliable, secure, production-grade async REST APIs with FastAPI by enforcing modern FastAPI, Pydantic v2, and SQLAlchemy async best practices.

Core Features & Use Cases

  • Production-grade FastAPI architecture: Enforces an async-first design using lifespan instead of deprecated startup hooks.
  • Correct Pydantic v2 schema patterns: Uses ConfigDict, field/model validators, and model_dump/model_dump_json idioms to keep schemas consistent.
  • Async SQLAlchemy 2.0 patterns: Provides guidance for async engine/session lifecycle, mapped_column/relationships, and safe CRUD handling without lazy-loading surprises.
  • API reliability & interoperability: Standardizes RFC 9457 Problem Details error responses, cursor pagination, and URI versioning from day one.
  • Security-ready authentication: Recommends PyJWT (not python-jose), OAuth2 patterns, secure cookie refresh strategies, and role-based access control.

Quick Start

Use the fastapi-expert Skill when designing a new versioned FastAPI API and ask it to produce the router, schemas, auth dependency patterns, and RFC 9457 error handling skeleton.

Frequently Asked Questions about fastapi-expert

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

FAQPage Schema
How do I build a production-grade async FastAPI API with Pydantic v2?

To build a production-grade async FastAPI API, use an async-first design with lifespan management instead of deprecated startup hooks, Pydantic v2 ConfigDict schemas, and model_dump_json idioms. This ensures consistent type validation and reliable async REST API performance.

What's the best way to handle FastAPI error responses with RFC 9457?

Handling FastAPI error responses with RFC 9457 involves standardizing Problem Details responses across your API. This enforces consistent error handling semantics, providing interoperable error structures for clients consuming your async REST endpoints.

How do I implement cursor pagination in FastAPI with async SQLAlchemy?

Implementing cursor pagination in FastAPI requires using async SQLAlchemy 2.0 patterns to manage database sessions safely without lazy-loading surprises. Cursor pagination handles large collections efficiently by avoiding offset limitations, ensuring stable results during data modifications.

Does FastAPI support JWT OAuth2 authentication with PyJWT?

FastAPI supports JWT OAuth2 authentication using PyJWT for secure token handling. This approach implements OAuth2 patterns, secure cookie refresh strategies, and role-based access control to protect your async REST API endpoints effectively.

How do I test async FastAPI endpoints with httpx and anyio?

Testing async FastAPI endpoints uses httpx AsyncClient with anyio asyncio_mode auto. This testing pattern validates your async REST API behavior, including Idempotency-Key and Retry-After semantics, ensuring reliable production performance under concurrent requests.

Can I use APIRouter prefixes for URI versioning in FastAPI?

APIRouter prefixes enable URI versioning in FastAPI by routing requests to specific endpoint versions. This enforces consistent API reliability and interoperability from day one, allowing you to manage breaking changes across different API versions systematically.