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Data Client Endpoint Setup

OrganizationPopular
reactive
data-client-endpoint-setup

Set up @data-client/endpoint for custom async operations. Wraps existing async functions with Endpoint for use with Data Client hooks. Use after data-client-setup detects non-REST/GraphQL async patterns.

Overview

Publisherreactive
Repositorydata-client
Skill namedata-client-endpoint-setup
Stars
2K
Forks
98
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Data Client Endpoint Setup 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.
    https://github.com/reactive/data-client/tree/master/.agents/skills/data-client-endpoint-setup
  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/reactive/data-client.git /tmp/data-client
mkdir -p .claude/skills
cp -r /tmp/data-client/.agents/skills/data-client-endpoint-setup .claude/skills/data-client-endpoint-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Client Endpoint Setup 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 Data Client Endpoint Setup 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 Data Client Endpoint Setup 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.

Custom Endpoint Setup

This guide configures @data-client/endpoint for wrapping existing async functions. Use it once the Data Client provider is set up and the project has async operations that aren't REST or GraphQL.

Installation

Install the endpoint package alongside the core package:

bash
# npm
npm install @data-client/endpoint

# yarn
yarn add @data-client/endpoint

# pnpm
pnpm add @data-client/endpoint

When to Use

Use @data-client/endpoint when:

  • Working with third-party SDK clients (Firebase, Supabase, AWS SDK, etc.)
  • Using WebSocket connections for data fetching
  • Accessing local async storage (IndexedDB, AsyncStorage)
  • Any async function that doesn't fit REST or GraphQL patterns

Wrapping Async Functions

See Endpoint for full API documentation.

Detection

Scan for existing async functions that fetch data:

  • Functions returning Promise<T>
  • SDK client methods
  • WebSocket message handlers
  • IndexedDB operations

Basic Wrapping Pattern

Before (existing code):

ts
// src/api/users.ts
export async function getUser(id: string): Promise<User> {
  const response = await sdk.users.get(id);
  return response.data;
}

export async function listUsers(filters: UserFilters): Promise<User[]> {
  const response = await sdk.users.list(filters);
  return response.data;
}

After (with Endpoint wrapper):

ts
// src/api/users.ts
import { Endpoint } from '@data-client/endpoint';
import { User } from '../schemas/User';

// Original functions (keep for reference or direct use)
async function fetchUser(id: string): Promise<User> {
  const response = await sdk.users.get(id);
  return response.data;
}

async function fetchUsers(filters: UserFilters): Promise<User[]> {
  const response = await sdk.users.list(filters);
  return response.data;
}

// Wrapped as Endpoints for use with Data Client hooks
export const getUser = new Endpoint(fetchUser, {
  schema: User,
  name: 'getUser',
});

export const listUsers = new Endpoint(fetchUsers, {
  schema: [User],
  name: 'listUsers',
});

Endpoint Options

Configure based on the function's behavior:

ts
export const getUser = new Endpoint(fetchUser, {
  // Required for normalization
  schema: User,
  
  // Unique name (important if function names get mangled in production)
  name: 'getUser',
  
  // Mark as side-effect if it modifies data
  sideEffect: true, // for mutations
  
  // Cache configuration
  dataExpiryLength: 60000, // 1 minute
  errorExpiryLength: 5000, // 5 seconds
  
  // Enable polling
  pollFrequency: 30000, // poll every 30 seconds
  
  // Optimistic updates
  getOptimisticResponse(snap, id) {
    return snap.get(User, { id });
  },
});

Custom Key Function

If the default key function doesn't work for your use case:

ts
export const searchUsers = new Endpoint(fetchSearchUsers, {
  schema: [User],
  name: 'searchUsers',
  key({ query, page }) {
    // Custom key for complex parameters
    return `searchUsers:${query}:${page}`;
  },
});

Common Patterns

Firebase/Firestore

ts
import { Endpoint } from '@data-client/endpoint';
import { doc, getDoc, collection, getDocs } from 'firebase/firestore';
import { db } from './firebase';
import { User } from '../schemas/User';

async function fetchUser(id: string): Promise<User> {
  const docRef = doc(db, 'users', id);
  const docSnap = await getDoc(docRef);
  return { id: docSnap.id, ...docSnap.data() } as User;
}

async function fetchUsers(): Promise<User[]> {
  const querySnapshot = await getDocs(collection(db, 'users'));
  return querySnapshot.docs.map(doc => ({
    id: doc.id,
    ...doc.data(),
  })) as User[];
}

export const getUser = new Endpoint(fetchUser, {
  schema: User,
  name: 'getUser',
});

export const listUsers = new Endpoint(fetchUsers, {
  schema: [User],
  name: 'listUsers',
});

Supabase

ts
import { Endpoint } from '@data-client/endpoint';
import { supabase } from './supabase';
import { User } from '../schemas/User';

async function fetchUser(id: string): Promise<User> {
  const { data, error } = await supabase
    .from('users')
    .select('*')
    .eq('id', id)
    .single();
  if (error) throw error;
  return data;
}

async function fetchUsers(filters?: { role?: string }): Promise<User[]> {
  let query = supabase.from('users').select('*');
  if (filters?.role) {
    query = query.eq('role', filters.role);
  }
  const { data, error } = await query;
  if (error) throw error;
  return data;
}

export const getUser = new Endpoint(fetchUser, {
  schema: User,
  name: 'getUser',
});

export const listUsers = new Endpoint(fetchUsers, {
  schema: [User],
  name: 'listUsers',
});

IndexedDB

ts
import { Endpoint } from '@data-client/endpoint';
import { User } from '../schemas/User';

async function fetchUserFromCache(id: string): Promise<User | undefined> {
  const db = await openDB('myapp', 1);
  return db.get('users', id);
}

async function fetchUsersFromCache(): Promise<User[]> {
  const db = await openDB('myapp', 1);
  return db.getAll('users');
}

export const getCachedUser = new Endpoint(fetchUserFromCache, {
  schema: User,
  name: 'getCachedUser',
  dataExpiryLength: Infinity, // Never expires
});

export const listCachedUsers = new Endpoint(fetchUsersFromCache, {
  schema: [User],
  name: 'listCachedUsers',
  dataExpiryLength: Infinity,
});

WebSocket Fetch

ts
import { Endpoint } from '@data-client/endpoint';
import { socket } from './socket';
import { Message } from '../schemas/Message';

async function fetchMessages(roomId: string): Promise<Message[]> {
  return new Promise((resolve, reject) => {
    socket.emit('getMessages', { roomId }, (response: any) => {
      if (response.error) reject(response.error);
      else resolve(response.data);
    });
  });
}

export const getMessages = new Endpoint(fetchMessages, {
  schema: [Message],
  name: 'getMessages',
});

Mutations with Side Effects

ts
export const createUser = new Endpoint(
  async (userData: Omit<User, 'id'>): Promise<User> => {
    const { data, error } = await supabase
      .from('users')
      .insert(userData)
      .select()
      .single();
    if (error) throw error;
    return data;
  },
  {
    schema: User,
    name: 'createUser',
    sideEffect: true,
  },
);

export const deleteUser = new Endpoint(
  async (id: string): Promise<{ id: string }> => {
    const { error } = await supabase.from('users').delete().eq('id', id);
    if (error) throw error;
    return { id };
  },
  {
    name: 'deleteUser',
    sideEffect: true,
  },
);

Using extend() for Variations

ts
const baseUserEndpoint = new Endpoint(fetchUser, {
  schema: User,
  name: 'getUser',
});

// With different cache settings
export const getUserFresh = baseUserEndpoint.extend({
  dataExpiryLength: 0, // Always refetch
});

// With polling
export const getUserLive = baseUserEndpoint.extend({
  pollFrequency: 5000, // Poll every 5 seconds
});

Important: Function Name Mangling

In production builds, function names may be mangled. Always provide explicit name option:

ts
// Bad - name may become 'a' or similar in production
const getUser = new Endpoint(fetchUser);

// Good - explicit name survives minification
const getUser = new Endpoint(fetchUser, { name: 'getUser' });

Usage in with hooks and controller

tsx
useSuspense(getUser, id);
ctrl.fetch(createUser, userData);

Both hooks and controller methods take endpoint as first argument, with the endpoint's function arguments following.

Next Steps

  1. Apply skill "data-client-schema" to define Entity classes
  2. Apply skill "data-client-react" or "data-client-vue" for usage

References

Vue projects: read <name>.vue.md instead of <name>.md when it exists.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Data Client Endpoint Setup AI skill do?

Set up @data-client/endpoint for custom async operations. Wraps existing async functions with Endpoint for use with Data Client hooks. Use after data-client-setup detects non-REST/GraphQL async patterns.

Why use Data Client Endpoint Setup on TypingMind?

Because you install it once and use it with any model. Data Client Endpoint Setup 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 Data Client Endpoint Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reactive/data-client/tree/master/.agents/skills/data-client-endpoint-setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Data Client Endpoint Setup?

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 Data Client Endpoint Setup?

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

Is the Data Client Endpoint Setup AI skill free?

Yes. It is published on GitHub by reactive under the Apache-2.0 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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