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Expo Data Fetching

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expo
expo-data-fetching

Framework (OSS). Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, loading/empty/error screen states, and Expo Router data loaders (`useLoaderData`).

Overview

Publisherexpo
Repositoryskills
Skill nameexpo-data-fetching
Stars
2.5K
Forks
146
Bundled files
3
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.

  • 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 expo on GitHub. Read the source before you install it.

Installation

Install the Expo Data Fetching 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/expo/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/expo/skills/expo-data-fetching .claude/skills/expo-data-fetching
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Expo Data Fetching 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 Expo Data Fetching 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 Expo Data Fetching 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.

Expo Networking

You MUST use this skill for ANY networking work including API requests, data fetching, caching, or network debugging.

References

Consult these resources as needed:

references/
  expo-router-loaders.md        Route-level data loading with Expo Router loaders (web, SDK 55+)
  offline-and-cancellation.md   NetInfo network status, offline-first React Query, AbortController

When to Use

Use this skill when:

  • Implementing API requests
  • Setting up data fetching (React Query, SWR)
  • Using Expo Router data loaders (useLoaderData, web SDK 55+)
  • Debugging network failures
  • Implementing caching strategies
  • Handling offline scenarios
  • Authentication/token management
  • Configuring API URLs and environment variables

Preferences

  • Avoid axios, prefer expo/fetch

Every Screen Has Four States

Design loading, error, empty, and content for screens that load data. These can overlap: a refresh error should coexist with cached content.

  • Loading ≠ empty. Empty means resolved with zero items, not missing data. Handle initial loading, failure, and hydration before checking list length. In TanStack Query v5, isLoading means the first fetch is running; a disabled or offline-paused query can have no data without being loading. Show the prerequisite or offline state in that case.
  • Empty is a designed state, not a blank list. Use ListEmptyComponent on FlatList/FlashList: explain why it is empty and offer the relevant next action. "No items yet" can offer Create; "No results" should offer changing or clearing the search/filter.
  • Refetches keep stale content. Render cached data even if a refresh fails, with a nonblocking error and retry. Use isLoading for first-fetch spinners and isFetching for background activity; prefer a skeleton for a slow initial load with a known layout, and RefreshControl for user-initiated refresh.
  • Gate on hydration. When initial UI or a redirect depends on persisted state (auth token, onboarding flag), the root layout renders nothing - or the splash - until that state has loaded. Deciding on unhydrated state flashes the wrong screen on every cold start and misroutes deep links that arrive before hydration.

Saves preserve work. While a mutation is pending, disable repeat submission. On failure, retain the draft, show an inline error, and let the user retry; clear or dismiss only after success. If updating optimistically, restore the previous value or mark the edit as unsynced on failure. Verify with a failed save followed by retry.

Common Issues & Solutions

1. Basic Fetch Usage

Simple GET request:

tsx
const fetchUser = async (userId: string) => {
  const response = await fetch(`https://api.example.com/users/${userId}`);

  if (!response.ok) {
    throw new Error(`HTTP error! status: ${response.status}`);
  }

  return response.json();
};

POST request with body:

tsx
const createUser = async (userData: UserData) => {
  const response = await fetch("https://api.example.com/users", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${token}`,
    },
    body: JSON.stringify(userData),
  });

  if (!response.ok) {
    const error = await response.json();
    throw new Error(error.message);
  }

  return response.json();
};

2. React Query (TanStack Query)

Setup:

tsx
// app/_layout.tsx
import { QueryClient, QueryClientProvider } from "@tanstack/react-query";

const queryClient = new QueryClient({
  defaultOptions: {
    queries: {
      staleTime: 1000 * 60 * 5, // 5 minutes
      retry: 2,
    },
  },
});

export default function RootLayout() {
  return (
    <QueryClientProvider client={queryClient}>
      <Stack />
    </QueryClientProvider>
  );
}

Fetching data:

tsx
import { useQuery } from "@tanstack/react-query";

function UserProfile({ userId }: { userId: string }) {
  const { data, fetchStatus, error, refetch } = useQuery({
    queryKey: ["user", userId],
    queryFn: () => fetchUser(userId),
  });

  if (data === undefined) {
    if (error) return <ErrorState message={error.message} onRetry={() => refetch()} />;
    if (fetchStatus === "paused") return <OfflineState />;
    return <Loading />;
  }

  return (
    <>
      {error && <InlineError message="Could not refresh. Showing saved data." onRetry={() => refetch()} />}
      {data === null ? <EmptyState message="User not found" /> : <Profile user={data} />}
    </>
  );
}

Mutations:

tsx
import { useMutation, useQueryClient } from "@tanstack/react-query";

function CreateUserForm() {
  const queryClient = useQueryClient();

  const mutation = useMutation({
    mutationFn: createUser,
    onSuccess: () => {
      // Invalidate and refetch
      queryClient.invalidateQueries({ queryKey: ["users"] });
    },
  });

  const handleSubmit = (data: UserData) => {
    if (mutation.isPending) return;
    mutation.mutate(data);
  };

  // Form keeps its draft on error and disables Submit while isLoading.
  return <Form onSubmit={handleSubmit} isLoading={mutation.isPending} error={mutation.error?.message} />;
}

3. Error Handling

Comprehensive error handling:

tsx
class ApiError extends Error {
  constructor(message: string, public status: number, public code?: string) {
    super(message);
    this.name = "ApiError";
  }
}

const fetchWithErrorHandling = async (url: string, options?: RequestInit) => {
  try {
    const response = await fetch(url, options);

    if (!response.ok) {
      const error = await response.json().catch(() => ({}));
      throw new ApiError(
        error.message || "Request failed",
        response.status,
        error.code
      );
    }

    return response.json();
  } catch (error) {
    if (error instanceof ApiError) {
      throw error;
    }
    // Network error (no internet, timeout, etc.)
    throw new ApiError("Network error", 0, "NETWORK_ERROR");
  }
};

Retry logic:

tsx
const fetchWithRetry = async (
  url: string,
  options?: RequestInit,
  retries = 3
) => {
  for (let i = 0; i < retries; i++) {
    try {
      return await fetchWithErrorHandling(url, options);
    } catch (error) {
      if (i === retries - 1) throw error;
      // Exponential backoff
      await new Promise((r) => setTimeout(r, Math.pow(2, i) * 1000));
    }
  }
};

4. Authentication

Token management:

tsx
import * as SecureStore from "expo-secure-store";

const TOKEN_KEY = "auth_token";

export const auth = {
  getToken: () => SecureStore.getItemAsync(TOKEN_KEY),
  setToken: (token: string) => SecureStore.setItemAsync(TOKEN_KEY, token),
  removeToken: () => SecureStore.deleteItemAsync(TOKEN_KEY),
};

// Authenticated fetch wrapper
const authFetch = async (url: string, options: RequestInit = {}) => {
  const token = await auth.getToken();

  return fetch(url, {
    ...options,
    headers: {
      ...options.headers,
      Authorization: token ? `Bearer ${token}` : "",
    },
  });
};

Token refresh:

tsx
let isRefreshing = false;
let refreshPromise: Promise<string> | null = null;

const getValidToken = async (): Promise<string> => {
  const token = await auth.getToken();

  if (!token || isTokenExpired(token)) {
    if (!isRefreshing) {
      isRefreshing = true;
      refreshPromise = refreshToken().finally(() => {
        isRefreshing = false;
        refreshPromise = null;
      });
    }
    return refreshPromise!;
  }

  return token;
};

5. Offline Support

Network-status detection with NetInfo and offline-first React Query setup: see ./references/offline-and-cancellation.md.


6. Environment Variables

Using environment variables for API configuration:

Expo supports environment variables with the EXPO_PUBLIC_ prefix. These are inlined at build time and available in your JavaScript code.

tsx
// .env
EXPO_PUBLIC_API_URL=https://api.example.com
EXPO_PUBLIC_API_VERSION=v1

// Usage in code
const API_URL = process.env.EXPO_PUBLIC_API_URL;

const fetchUsers = async () => {
  const response = await fetch(`${API_URL}/users`);
  return response.json();
};

Environment-specific configuration:

tsx
// .env.development
EXPO_PUBLIC_API_URL=http://localhost:3000

// .env.production
EXPO_PUBLIC_API_URL=https://api.production.com

Creating an API client with environment config:

tsx
// api/client.ts
const BASE_URL = process.env.EXPO_PUBLIC_API_URL;

if (!BASE_URL) {
  throw new Error("EXPO_PUBLIC_API_URL is not defined");
}

export const apiClient = {
  get: async <T,>(path: string): Promise<T> => {
    const response = await fetch(`${BASE_URL}${path}`);
    if (!response.ok) throw new Error(`HTTP ${response.status}`);
    return response.json();
  },

  post: async <T,>(path: string, body: unknown): Promise<T> => {
    const response = await fetch(`${BASE_URL}${path}`, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify(body),
    });
    if (!response.ok) throw new Error(`HTTP ${response.status}`);
    return response.json();
  },
};

Important notes:

  • Only variables prefixed with EXPO_PUBLIC_ are exposed to the client bundle
  • Never put secrets (API keys with write access, database passwords) in EXPO_PUBLIC_ variables—they're visible in the built app
  • Environment variables are inlined at build time, not runtime
  • Restart the dev server after changing .env files
  • For server-side secrets in API routes, use variables without the EXPO_PUBLIC_ prefix

TypeScript support:

tsx
// types/env.d.ts
declare global {
  namespace NodeJS {
    interface ProcessEnv {
      EXPO_PUBLIC_API_URL: string;
      EXPO_PUBLIC_API_VERSION?: string;
    }
  }
}

export {};

7. Request Cancellation

AbortController on unmount (React Query cancels automatically): see ./references/offline-and-cancellation.md.


Decision Tree

User asks about networking
  |-- Route-level data loading (web, SDK 55+)?
  |   \-- Expo Router loaders — see references/expo-router-loaders.md
  |
  |-- Basic fetch?
  |   \-- Use fetch API with error handling
  |
  |-- Need caching/state management?
  |   |-- Complex app -> React Query (TanStack Query)
  |   \-- Simpler needs -> SWR or custom hooks
  |
  |-- Authentication?
  |   |-- Token storage -> expo-secure-store
  |   \-- Token refresh -> Implement refresh flow
  |
  |-- Error handling?
  |   |-- Network errors -> Check connectivity first
  |   |-- HTTP errors -> Parse response, throw typed errors
  |   \-- Retries -> Exponential backoff
  |
  |-- Offline support?
  |   |-- Check status -> NetInfo
  |   \-- Queue requests -> React Query persistence
  |
  |-- Environment/API config?
  |   |-- Client-side URLs -> EXPO_PUBLIC_ prefix in .env
  |   |-- Server secrets -> Non-prefixed env vars (API routes only)
  |   \-- Multiple environments -> .env.development, .env.production
  |
  \-- Performance?
      |-- Caching -> React Query with staleTime
      |-- Deduplication -> React Query handles this
      \-- Cancellation -> AbortController or React Query

Common Mistakes

Wrong: No error handling

tsx
const data = await fetch(url).then((r) => r.json());

Right: Check response status

tsx
const response = await fetch(url);
if (!response.ok) throw new Error(`HTTP ${response.status}`);
const data = await response.json();

Wrong: Storing tokens in AsyncStorage

tsx
await AsyncStorage.setItem("token", token); // Not secure!

Right: Use SecureStore for sensitive data

tsx
await SecureStore.setItemAsync("token", token);

Example Invocations

User: "How do I make API calls in React Native?" -> Use fetch, wrap with error handling

User: "Should I use React Query or SWR?" -> React Query for complex apps, SWR for simpler needs

User: "My app needs to work offline" -> Use NetInfo for status, React Query persistence for caching

User: "How do I handle authentication tokens?" -> Store in expo-secure-store, implement refresh flow

User: "API calls are slow" -> Check caching strategy, use React Query staleTime User: "How do I configure different API URLs for dev and prod?" -> Use EXPO_PUBLIC_ env vars with .env.development and .env.production files User: "Where should I put my API key?" -> Client-safe keys: EXPO_PUBLIC_ in .env. Secret keys: non-prefixed env vars in API routes only

User: "How do I load data for a page in Expo Router?" -> See references/expo-router-loaders.md for route-level loaders (web, SDK 55+). For native, use React Query or fetch.

Submitting Feedback

If you encounter errors, misleading or outdated information in this skill, report it so Expo can improve:

bash
npx --yes submit-expo-feedback@latest --category skills --subject "expo-data-fetching" "<actionable feedback>"

Only submit when you have something specific and actionable to report. Include as much relevant context as possible. If an AI agent repeatedly failed or the user had to take over an Expo task, load the expo-skill-feedback skill and follow its eval-candidate flow instead of reusing the command above.

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 Expo Data Fetching AI skill do?

Framework (OSS). Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, loading/empty/error screen states, and Expo Router data loaders (`useLoaderData`).

Why use Expo Data Fetching on TypingMind?

Because you install it once and use it with any model. Expo Data Fetching 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 Expo Data Fetching in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/expo/skills/tree/main/plugins/expo/skills/expo-data-fetching. 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 Expo Data Fetching?

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 Expo Data Fetching?

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

Is the Expo Data Fetching AI skill free?

Yes. It is published on GitHub by expo 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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