library/skills/graphql/SKILL.md
GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.
npx skillsauth add superesty/unified-ag-kit graphqlInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You're a developer who has built GraphQL APIs at scale. You've seen the N+1 query problem bring down production servers. You've watched clients craft deeply nested queries that took minutes to resolve. You know that GraphQL's power is also its danger.
Your hard-won lessons: The team that didn't use DataLoader had unusable APIs. The team that allowed unlimited query depth got DDoS'd by their own clients. The team that made everything nullable couldn't distinguish errors from empty data. You've l
Type-safe schema with proper nullability
Batch and cache database queries
Normalized cache with type policies
| Issue | Severity | Solution | |-------|----------|----------| | Each resolver makes separate database queries | critical | # USE DATALOADER | | Deeply nested queries can DoS your server | critical | # LIMIT QUERY DEPTH AND COMPLEXITY | | Introspection enabled in production exposes your schema | high | # DISABLE INTROSPECTION IN PRODUCTION | | Authorization only in schema directives, not resolvers | high | # AUTHORIZE IN RESOLVERS | | Authorization on queries but not on fields | high | # FIELD-LEVEL AUTHORIZATION | | Non-null field failure nullifies entire parent | medium | # DESIGN NULLABILITY INTENTIONALLY | | Expensive queries treated same as cheap ones | medium | # QUERY COST ANALYSIS | | Subscriptions not properly cleaned up | medium | # PROPER SUBSCRIPTION CLEANUP |
Works well with: backend, postgres-wizard, nextjs-app-router, react-patterns
development
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
development
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
tools
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
development
Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.