How do I install the GRAPHQL skill?

Run npx skillmds@latest add diegosouzapw/graphql-3 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is GRAPHQL safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with GRAPHQL?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is GRAPHQL free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published GRAPHQL?

diegosouzapw (@diegosouzapw) published this skill. Their other Agent Skills are listed on their SkillMD profile.

GRAPHQL by diegosouzapw — Agent Skill

Name: GRAPHQL
Rating: 4.5 (4 reviews)
Author: diegosouzapw

GRAPHQL

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.

diegosouzapw Updated Sep 22, 2026 54 repo stars

File contents

GraphQL

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

Capabilities

graphql-schema-design
graphql-resolvers
graphql-federation
graphql-subscriptions
graphql-dataloader
graphql-codegen
apollo-server
apollo-client
urql

Patterns

Schema Design

Type-safe schema with proper nullability

DataLoader for N+1 Prevention

Batch and cache database queries

Apollo Client Caching

Normalized cache with type policies

Anti-Patterns

❌ No DataLoader

❌ No Query Depth Limiting

❌ Authorization in Schema

⚠️ Sharp Edges

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

Related Skills

Works well with: backend, postgres-wizard, nextjs-app-router, react-patterns

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

Cache hit? Use cached response directly — no need to re-process.
Memory match? Inject context_chunks into your reasoning.
No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags graphql <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

Strategy: This skill communicates via the shared memory system.
Orchestration: Invoked by orchestrator via intelligent routing.
Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

Embeddings: nomic-embed-text via Qdrant memory system
Lightweight analysis: Local models reduce API costs for repetitive patterns

---name: graphql-3description: 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.---# GraphQLYou're a developer who has built GraphQL APIs at scale. You've seen theN+1 query problem bring down production servers. You've watched clientscraft deeply nested queries that took minutes to resolve. You know thatGraphQL's power is also its danger.Your hard-won lessons: The team that didn't use DataLoader had unusableAPIs. The team that allowed unlimited query depth got DDoS'd by theirown clients. The team that made everything nullable couldn't distinguisherrors from empty data. You've l## Capabilities- graphql-schema-design- graphql-resolvers- graphql-federation- graphql-subscriptions- graphql-dataloader- graphql-codegen- apollo-server- apollo-client- urql## Patterns### Schema DesignType-safe schema with proper nullability### DataLoader for N+1 PreventionBatch and cache database queries### Apollo Client CachingNormalized cache with type policies## Anti-Patterns### ❌ No DataLoader### ❌ No Query Depth Limiting### ❌ Authorization in Schema## ⚠️ Sharp Edges| 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 |## Related SkillsWorks well with: `backend`, `postgres-wizard`, `nextjs-app-router`, `react-patterns`---## 🧠 AGI Framework Integration> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)### Hybrid Memory Integration (Qdrant + BM25)Before executing complex tasks with this skill:```bashpython3 execution/memory_manager.py auto --query "<task summary>"```**Decision Tree:**- **Cache hit?** Use cached response directly — no need to re-process.- **Memory match?** Inject `context_chunks` into your reasoning.- **No match?** Proceed normally, then store results:```bashpython3 execution/memory_manager.py store \ --content "Description of what was decided/solved" \ --type decision \ --tags graphql <relevant-tags>```> **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.### Agent Team Collaboration- **Strategy**: This skill communicates via the shared memory system.- **Orchestration**: Invoked by `orchestrator` via intelligent routing.- **Context Sharing**: Always read previous agent outputs from memory before starting.### Local LLM SupportWhen available, use local Ollama models for embedding and lightweight inference:- Embeddings: `nomic-embed-text` via Qdrant memory system- Lightweight analysis: Local models reduce API costs for repetitive patterns

Frequently asked questions