Is this skill specific to a particular programming language?

While the provided examples are in Go, the architectural patterns for context, batching, and error handling are applicable to any GraphQL implementation.

Does this skill support field-level security?

Yes, it includes guidance for implementing authorization checks at the individual field level to ensure users only access data they are permitted to see.

Can it help with error masking?

Absolutely. It provides patterns for wrapping internal errors to prevent leaking database or infrastructure details to API consumers.

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

It provides specific implementation patterns for Dataloaders, allowing Claude to batch and cache database requests across resolver chains efficiently.

GraphQL Resolver Patterns

Name: GraphQL Resolver Patterns
Author: jovermier

byjovermier

API Development

Implements efficient, secure, and performant GraphQL resolvers using industry-standard patterns like dataloaders and context propagation.

About

This skill equips Claude with expert guidance for building production-ready GraphQL resolvers, specifically focusing on solving the N+1 query problem through batching. It facilitates the implementation of secure API architectures by enforcing field-level authorization, proper error wrapping to prevent sensitive data leaks, and consistent context propagation across resolver chains. Whether you are architecting a new GraphQL service or optimizing an existing Go-based backend, this skill ensures your implementation follows best practices for performance, scalability, and robust input validation.

Key Features

Secure field-level authorization and authentication middleware
0 GitHub stars
N+1 query prevention using Dataloader batching patterns
Schema-layer input validation strategies
Standardized error handling to prevent internal detail exposure
Context propagation patterns for nested resolver chains

Use Cases

Standardizing backend error responses and request-scoped logging
Optimizing database performance in complex, highly-nested GraphQL schemas
Implementing fine-grained access control for sensitive API data

About

Key Features

Secure field-level authorization and authentication middleware
0 GitHub stars
N+1 query prevention using Dataloader batching patterns
Schema-layer input validation strategies
Standardized error handling to prevent internal detail exposure
Context propagation patterns for nested resolver chains

Use Cases

Standardizing backend error responses and request-scoped logging
Optimizing database performance in complex, highly-nested GraphQL schemas
Implementing fine-grained access control for sensitive API data