Does it support microservices architectures?

Yes, the skill includes specialized patterns for GraphQL Federation, helping you design and manage a unified graph across distributed service environments.

How does this skill help with GraphQL performance?

It provides specific implementation patterns for DataLoader to batch and cache database requests, alongside strategies for query cost analysis to prevent expensive operations from slowing down the server.

How does it handle GraphQL security?

The skill enforces best practices like limiting query depth to prevent DoS attacks, disabling introspection in production, and implementing authorization logic within resolvers rather than just the schema.

Is this skill compatible with frontend libraries?

Absolutely. It covers client-side integration best practices for popular libraries like Apollo Client and urql, including normalized caching and type policies.

GraphQL Architecture & Optimization

Name: GraphQL Architecture & Optimization
Author: sickn33

bysickn33

•

6,034

API開発

Architects secure, high-performance GraphQL APIs with a focus on type safety, N+1 query prevention, and robust schema design.

概要

This skill equips Claude with the expertise of a senior backend engineer to build and maintain production-grade GraphQL APIs. It provides deep guidance on schema design, resolver optimization using DataLoader to prevent N+1 query issues, and implementing GraphQL Federation for microservices. Beyond development, it emphasizes critical security and performance patterns such as query depth limiting, cost analysis, and field-level authorization to ensure your API remains resilient and efficient under load.

主な機能

Client-side integration strategies for Apollo and urql
Query depth and complexity limiting for DoS protection
GraphQL Federation patterns for scalable microservices
Type-safe schema design and introspection best practices
6,034 GitHub stars
N+1 query prevention using DataLoader batching and caching

ユースケース

Refactoring legacy resolvers to solve performance bottlenecks and N+1 issues
Building scalable, type-safe backend APIs for data-intensive applications
Implementing robust security layers including field-level authorization and cost analysis

概要

主な機能

Client-side integration strategies for Apollo and urql
Query depth and complexity limiting for DoS protection
GraphQL Federation patterns for scalable microservices
Type-safe schema design and introspection best practices
6,034 GitHub stars
N+1 query prevention using DataLoader batching and caching

ユースケース

Refactoring legacy resolvers to solve performance bottlenecks and N+1 issues
Building scalable, type-safe backend APIs for data-intensive applications
Implementing robust security layers including field-level authorization and cost analysis