Can I use this to protect my API from malicious queries?

Yes, the skill includes depth limiting and complexity analysis techniques to identify and reject overly nested or resource-heavy queries before they impact your server.

Does it support response caching?

It covers various caching layers, including field-level caching, Redis-based response caching, and CDN optimization through Automatic Persisted Queries (APQ).

How does this skill help with N+1 query problems?

It provides implementation patterns for DataLoader, which batches multiple individual resource requests into a single database query, significantly reducing database overhead.

Is this skill specific to a certain GraphQL framework?

While it features examples for Apollo Server, the principles of complexity analysis, batching, and depth limiting are applicable across most modern GraphQL implementations.

GraphQL Performance Optimizer

Name: GraphQL Performance Optimizer
Author: TheBushidoCollective

byTheBushidoCollective

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API Development

Optimizes GraphQL API efficiency and security using complexity analysis, batching, and advanced caching strategies.

About

This skill empowers Claude to architect and refine high-performance GraphQL APIs by implementing industry-standard optimization patterns. It provides actionable guidance on preventing N+1 problems via DataLoader, protecting servers from malicious queries through depth limiting and complexity analysis, and reducing latency with multi-level caching and Automatic Persisted Queries (APQ). Ideal for developers building scalable backends who need to balance flexible data fetching with server-side stability and resource management.

Key Features

Multi-level response caching strategies using Redis and CDNs
Recursive depth limiting to prevent DoS attacks
Query complexity and cost analysis implementation
39 GitHub stars
DataLoader integration for batching and N+1 prevention
Automatic Persisted Queries (APQ) for reduced payload size

Use Cases

Securing public GraphQL endpoints against expensive or malicious operations
Optimizing database performance by batching individual resource requests
Scaling a GraphQL API to handle complex, deeply nested frontend queries

About

Key Features

Multi-level response caching strategies using Redis and CDNs
Recursive depth limiting to prevent DoS attacks
Query complexity and cost analysis implementation
39 GitHub stars
DataLoader integration for batching and N+1 prevention
Automatic Persisted Queries (APQ) for reduced payload size

Use Cases

Securing public GraphQL endpoints against expensive or malicious operations
Optimizing database performance by batching individual resource requests
Scaling a GraphQL API to handle complex, deeply nested frontend queries