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Graphql Api Design

Community
seb1n
graphql-api-design

Design GraphQL APIs with well-structured schemas, efficient resolvers, pagination, and performance patterns like DataLoader and federation. Use when the user requests graphql api design or provides relevant inputs for this workflow.

Overview

Publisherseb1n
Repositoryawesome-ai-agent-skills
Skill namegraphql-api-design
Stars
188
Forks
35
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by seb1n on GitHub. Read the source before you install it.

Installation

Install the Graphql Api Design AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/api-and-integration/graphql-api-design .claude/skills/graphql-api-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Graphql Api Design in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Graphql Api Design on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Graphql Api Design is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GraphQL API Design

This skill enables an AI agent to design complete GraphQL APIs from specifications, schemas, or natural language descriptions. The agent produces type definitions, queries, mutations, subscriptions, input types, enums, and resolver implementations. It applies performance patterns including DataLoader for N+1 prevention, cursor-based pagination via the Relay connection spec, query depth limiting, and schema federation for microservice architectures.

Workflow

  1. Model the domain as types: Analyze the application domain and define GraphQL object types, input types, enums, interfaces, and unions. Each type should represent a real entity with fields that match the data consumers actually need. Use non-nullable (!) annotations deliberately—fields that can genuinely be absent should be nullable. Prefer specific scalar types (e.g., DateTime, URL) over raw String for self-documenting schemas.

  2. Design queries and mutations: Define Query fields for read operations and Mutation fields for write operations. Queries should be noun-based (user, posts) while mutations should be verb-based (createPost, updateUser). Each mutation should accept a single input type argument and return a payload type that includes the modified object plus any user-facing errors. This pattern keeps mutations consistent and extensible.

  3. Implement pagination with connections: For any list field that could return many items, use the Relay connection specification with edges, node, cursor, and pageInfo. This provides cursor-based pagination that is stable under insertions and deletions, unlike offset-based pagination. Define reusable connection types per entity rather than returning raw arrays.

  4. Write resolvers with DataLoader: Implement resolvers that use DataLoader to batch and cache database lookups within a single request. Without DataLoader, a query that fetches 50 posts and their authors would make 50 separate author queries (the N+1 problem). DataLoader collapses these into a single batched query. Create a new DataLoader instance per request to avoid leaking data between users.

  5. Add subscriptions for real-time data: Define Subscription fields for events clients need to react to in real-time (e.g., new messages, status changes). Use a pub/sub backend (Redis, Kafka, or in-memory for development) to publish events. Keep subscription payloads lean—clients can use the subscription trigger to refetch full data if needed.

  6. Secure and optimize the schema: Add query depth limiting (max 10-15 levels) and query complexity analysis to prevent abusive queries. Implement field-level authorization in resolvers. Use persisted queries in production to reduce bandwidth and prevent arbitrary query execution. Consider schema federation if the API spans multiple services.

Supported Technologies

  • Servers: Apollo Server, GraphQL Yoga, Mercurius (Fastify), Strawberry (Python), graphql-java
  • Schema tools: SDL-first (typeDefs), code-first (TypeGraphQL, Nexus, Pothos)
  • Performance: DataLoader, @defer/@stream directives, persisted queries, automatic persisted queries (APQ)
  • Federation: Apollo Federation, GraphQL Mesh, Schema Stitching
  • Testing: GraphQL Playground, Apollo Studio, graphql-test (jest), Insomnia

Usage

Provide the agent with a description of the data entities, their relationships, and the operations needed. The agent will produce a complete SDL schema, resolver implementations, and DataLoader setup. Specify whether you want SDL-first or code-first output, and which server framework to target.

Examples

Example 1: Blog Platform Schema with Resolvers

graphql
# schema.graphql — Complete blog platform schema

scalar DateTime

enum PostStatus {
  DRAFT
  PUBLISHED
  ARCHIVED
}

type User {
  id: ID!
  username: String!
  email: String!
  bio: String
  avatarUrl: String
  posts(first: Int, after: String): PostConnection!
  createdAt: DateTime!
}

type Post {
  id: ID!
  title: String!
  slug: String!
  content: String!
  excerpt: String
  status: PostStatus!
  author: User!
  tags: [Tag!]!
  comments(first: Int, after: String): CommentConnection!
  publishedAt: DateTime
  createdAt: DateTime!
  updatedAt: DateTime!
}

type Comment {
  id: ID!
  body: String!
  author: User!
  post: Post!
  createdAt: DateTime!
}

type Tag {
  id: ID!
  name: String!
  slug: String!
  posts(first: Int, after: String): PostConnection!
}

# Relay connection types for cursor-based pagination
type PostConnection {
  edges: [PostEdge!]!
  pageInfo: PageInfo!
  totalCount: Int!
}

type PostEdge {
  cursor: String!
  node: Post!
}

type CommentConnection {
  edges: [CommentEdge!]!
  pageInfo: PageInfo!
  totalCount: Int!
}

type CommentEdge {
  cursor: String!
  node: Comment!
}

type PageInfo {
  hasNextPage: Boolean!
  hasPreviousPage: Boolean!
  startCursor: String
  endCursor: String
}

# Queries
type Query {
  post(id: ID, slug: String): Post
  posts(
    first: Int = 10
    after: String
    status: PostStatus
    tagSlug: String
  ): PostConnection!
  user(id: ID!): User
  me: User
  tags: [Tag!]!
}

# Mutations with input types and payload types
input CreatePostInput {
  title: String!
  content: String!
  tagIds: [ID!]
  status: PostStatus = DRAFT
}

type CreatePostPayload {
  post: Post
  errors: [MutationError!]!
}

input UpdatePostInput {
  title: String
  content: String
  status: PostStatus
  tagIds: [ID!]
}

type UpdatePostPayload {
  post: Post
  errors: [MutationError!]!
}

type MutationError {
  field: String
  message: String!
}

type Mutation {
  createPost(input: CreatePostInput!): CreatePostPayload!
  updatePost(id: ID!, input: UpdatePostInput!): UpdatePostPayload!
  deletePost(id: ID!): Boolean!
  addComment(postId: ID!, body: String!): Comment!
}

# Subscriptions
type Subscription {
  commentAdded(postId: ID!): Comment!
  postPublished: Post!
}
javascript
// resolvers.js — Resolvers with DataLoader for N+1 prevention
const DataLoader = require("dataloader");

// Create loaders per request (called from context factory)
function createLoaders(db) {
  return {
    userLoader: new DataLoader(async (userIds) => {
      const users = await db.users.findByIds(userIds);
      const userMap = new Map(users.map((u) => [u.id, u]));
      return userIds.map((id) => userMap.get(id) || null);
    }),
    postLoader: new DataLoader(async (postIds) => {
      const posts = await db.posts.findByIds(postIds);
      const postMap = new Map(posts.map((p) => [p.id, p]));
      return postIds.map((id) => postMap.get(id) || null);
    }),
  };
}

const resolvers = {
  Query: {
    post: (_, { id, slug }, { db }) => {
      if (id) return db.posts.findById(id);
      if (slug) return db.posts.findBySlug(slug);
      return null;
    },
    posts: async (_, { first = 10, after, status, tagSlug }, { db }) => {
      const cursor = after ? decodeCursor(after) : null;
      const { rows, totalCount } = await db.posts.findPaginated({
        limit: first + 1,
        cursor,
        status,
        tagSlug,
      });
      const hasNextPage = rows.length > first;
      const edges = rows.slice(0, first).map((post) => ({
        cursor: encodeCursor(post.id),
        node: post,
      }));
      return {
        edges,
        totalCount,
        pageInfo: {
          hasNextPage,
          hasPreviousPage: !!after,
          startCursor: edges[0]?.cursor || null,
          endCursor: edges[edges.length - 1]?.cursor || null,
        },
      };
    },
    me: (_, __, { currentUser }) => currentUser,
  },
  Post: {
    author: (post, _, { loaders }) => loaders.userLoader.load(post.authorId),
    tags: (post, _, { db }) => db.tags.findByPostId(post.id),
  },
  Comment: {
    author: (comment, _, { loaders }) => loaders.userLoader.load(comment.authorId),
  },
  Mutation: {
    createPost: async (_, { input }, { currentUser, db }) => {
      if (!currentUser) return { post: null, errors: [{ message: "Not authenticated" }] };
      if (!input.title.trim()) {
        return { post: null, errors: [{ field: "title", message: "Title cannot be empty" }] };
      }
      const post = await db.posts.create({ ...input, authorId: currentUser.id });
      return { post, errors: [] };
    },
  },
};

function encodeCursor(id) { return Buffer.from(`cursor:${id}`).toString("base64"); }
function decodeCursor(cursor) { return Buffer.from(cursor, "base64").toString().replace("cursor:", ""); }

Example 2: Cursor-Based Pagination Implementation

javascript
// pagination.js — Reusable cursor-based pagination for any entity

/**
 * Generic paginated query builder for SQL databases.
 * Works with any table that has an auto-incrementing or sortable ID.
 */
async function paginatedQuery(db, { table, first = 10, after, where = {} }) {
  const limit = Math.min(first, 100); // Cap at 100 per page
  const conditions = [];
  const params = [];

  // Apply cursor (decode to original ID)
  if (after) {
    const cursorId = Buffer.from(after, "base64").toString().split(":")[1];
    conditions.push(`id < $${params.length + 1}`);
    params.push(cursorId);
  }

  // Apply additional filters
  for (const [key, value] of Object.entries(where)) {
    if (value !== undefined) {
      conditions.push(`${key} = $${params.length + 1}`);
      params.push(value);
    }
  }

  const whereClause = conditions.length > 0 ? `WHERE ${conditions.join(" AND ")}` : "";

  // Fetch one extra row to determine hasNextPage
  const query = `SELECT * FROM ${table} ${whereClause} ORDER BY id DESC LIMIT ${limit + 1}`;
  const rows = await db.query(query, params);

  // Count total matching rows
  const countQuery = `SELECT COUNT(*) as total FROM ${table} ${whereClause}`;
  const [{ total: totalCount }] = await db.query(countQuery, params);

  const hasNextPage = rows.length > limit;
  const nodes = rows.slice(0, limit);

  const edges = nodes.map((node) => ({
    cursor: Buffer.from(`cursor:${node.id}`).toString("base64"),
    node,
  }));

  return {
    edges,
    totalCount,
    pageInfo: {
      hasNextPage,
      hasPreviousPage: !!after,
      startCursor: edges[0]?.cursor || null,
      endCursor: edges[edges.length - 1]?.cursor || null,
    },
  };
}

// Usage in resolver
const resolvers = {
  Query: {
    posts: (_, args, { db }) =>
      paginatedQuery(db, {
        table: "posts",
        first: args.first,
        after: args.after,
        where: { status: args.status },
      }),
  },
};

Best Practices

  • Keep mutations consistent by always using a single input argument and returning a payload type with both the result and a list of user-facing errors. This makes client code predictable.
  • Solve N+1 with DataLoader on every relationship resolver. Create DataLoader instances per-request (in the context factory) to avoid leaking cached data between users or requests.
  • Limit query depth and complexity to prevent denial-of-service attacks. Set max depth to 10-15 and assign complexity costs to fields (especially connections and nested relationships).
  • Use nullable return types for single-entity queries (post(id: ID!): Post returns null if not found) and non-nullable arrays for list queries (tags: [Tag!]! always returns an array, possibly empty).
  • Version via schema evolution, not URL versioning. Add new fields freely (non-breaking), deprecate old fields with @deprecated(reason: "Use newField instead"), and remove them after clients have migrated.
  • Use input types for all mutation arguments rather than passing individual scalar arguments. This makes it easy to add optional fields later without breaking existing clients.

Edge Cases

  • Circular references: Types like User -> Posts -> Author -> Posts create circular schemas. This is valid in GraphQL but requires depth limiting to prevent infinite queries. DataLoader prevents infinite resolution loops.
  • Null propagation: If a non-nullable field resolver throws an error, the null propagates upward to the nearest nullable parent. Design nullable boundaries carefully to prevent one field error from nullifying an entire response.
  • Empty connections: Return { edges: [], pageInfo: { hasNextPage: false, hasPreviousPage: false }, totalCount: 0 } for empty result sets, not null.
  • Cursor stability: Cursors should be opaque and stable across insertions. Using row IDs as cursor values (base64 encoded) is stable; using offsets is not and breaks when items are inserted or deleted.
  • File uploads: GraphQL doesn't natively support file uploads. Use the multipart request spec (graphql-upload) or handle uploads via a separate REST endpoint and pass the resulting URL to a mutation.
  • Subscription connection drops: Clients can lose WebSocket connections. Design subscriptions so clients can recover state by re-querying on reconnect rather than relying solely on the subscription stream.

Frequently asked questions

What does the Graphql Api Design AI skill do?

Design GraphQL APIs with well-structured schemas, efficient resolvers, pagination, and performance patterns like DataLoader and federation. Use when the user requests graphql api design or provides relevant inputs for this workflow.

Why use Graphql Api Design on TypingMind?

Because you install it once and use it with any model. Graphql Api Design is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Graphql Api Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/seb1n/awesome-ai-agent-skills/tree/main/api-and-integration/graphql-api-design. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Graphql Api Design?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Graphql Api Design?

As many as you like. As long as a model supports skills, you can use Graphql Api Design with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Graphql Api Design AI skill free?

Yes. It is published on GitHub by seb1n under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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