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Firebase Database

Community
evanca
firebase-database

Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.

Overview

Publisherevanca
Repositoryflutter-ai-rules
Skill namefirebase-database
Stars
651
Forks
67
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 evanca on GitHub. Read the source before you install it.

Installation

Install the Firebase Database 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/evanca/flutter-ai-rules/tree/main/skills/firebase-database
  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/evanca/flutter-ai-rules.git /tmp/flutter-ai-rules
mkdir -p .claude/skills
cp -r /tmp/flutter-ai-rules/skills/firebase-database .claude/skills/firebase-database
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firebase Database 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 Firebase Database 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 Firebase Database 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.

Firebase Realtime Database Skill

This skill defines how to correctly implement Firebase Realtime Database in Flutter applications, covering data modeling, queries, real-time sync, offline support, and security rules.

When to Use

Use this skill when working with Firebase Realtime Database for simple data models, low-latency sync, or presence functionality. For rich data models requiring complex queries and high scalability, use Cloud Firestore instead.


1. Database Selection

Choose Realtime Database when the app needs:

  • Simple data models with simple lookups.
  • Extremely low-latency synchronization (typical response times under 10ms).
  • Deep queries that return an entire subtree by default.
  • Access to data at any granularity, down to individual leaf-node values.
  • Frequent state-syncing with built-in presence functionality.

Choose Cloud Firestore instead for rich data models requiring queryability, scalability, and high availability.


2. Setup and Configuration

flutter pub add firebase_database
dart
import 'package:firebase_database/firebase_database.dart';

// After Firebase.initializeApp():
final DatabaseReference ref = FirebaseDatabase.instance.ref();
  • Select the database location closest to users.
  • Enable persistence for offline capabilities:
dart
FirebaseDatabase.instance.setPersistenceEnabled(true);
FirebaseDatabase.instance.setPersistenceCacheSizeBytes(10000000); // 10MB

Setup Checklist

  1. Confirm Firebase.initializeApp() completes before accessing FirebaseDatabase.instance.
  2. Set persistence before any read/write operations.
  3. Verify connectivity by writing a test value and reading it back.

3. Data Structure

  • Structure data as a flattened JSON tree — avoid deep nesting.
  • Maximum nesting depth is 32 levels.
  • Design the data structure to support the most common queries.
  • Use push IDs for unique identifiers in list-type data:
dart
final newPostKey = FirebaseDatabase.instance.ref().child('posts').push().key;
  • Denormalize data when necessary — Realtime Database does not support joins.
  • Custom keys must be UTF-8 encoded, max 768 bytes, and cannot contain . $ # [ ] / or ASCII control characters 0-31 or 127.

Flattened Structure Example

dart
// Instead of nesting chat messages inside rooms:
// rooms/roomId/messages/messageId/...

// Flatten into separate top-level paths:
// rooms/roomId: { name: "General", createdBy: "uid1" }
// room-members/roomId: { uid1: true, uid2: true }
// room-messages/roomId/messageId: { text: "Hello", sender: "uid1", timestamp: ... }

This pattern allows reading room metadata without downloading all messages.


4. Indexing and Querying

  • Queries can sort or filter on a property, but not both in the same query.
  • Use .indexOn in security rules to index frequently queried fields:
json
{
  "rules": {
    "dinosaurs": {
      ".indexOn": ["height", "length"]
    }
  }
}
  • Queries are deep by default and return the entire subtree.
  • Sort with orderByChild(), orderByKey(), or orderByValue():
dart
final query = FirebaseDatabase.instance.ref("dinosaurs").orderByChild("height");
  • Limit results with limitToFirst() or limitToLast():
dart
final query = ref.orderByChild("height").limitToFirst(10);
  • Range queries using startAt(), endAt(), and equalTo():
dart
// Find users whose name starts with "A"
final query = ref.child("users")
    .orderByChild("name")
    .startAt("A")
    .endAt("A\uf8ff");

5. Read and Write Operations

Read once:

dart
final snapshot = await FirebaseDatabase.instance.ref('users/123').get();
if (snapshot.exists) {
  print(snapshot.value);
}

Real-time listener:

dart
final subscription = FirebaseDatabase.instance
    .ref('users/123')
    .onValue
    .listen((event) {
      final data = event.snapshot.value;
      print(data);
    });

// Cancel when no longer needed:
subscription.cancel();

A DatabaseEvent fires every time data changes at the reference, including changes to children.

Write (replace):

dart
await ref.set({
  "name": "John",
  "age": 18,
  "created_at": ServerValue.timestamp,
});

Update (partial):

dart
await ref.update({"age": 19});

Atomic transaction:

dart
final result = await FirebaseDatabase.instance
    .ref('posts/123/likes')
    .runTransaction((currentValue) {
      return Transaction.success((currentValue as int? ?? 0) + 1);
    });
print('Likes: ${result.snapshot.value}');

Multi-path atomic update:

dart
final updates = <String, dynamic>{
  'posts/$postId': postData,
  'user-posts/$uid/$postId': postData,
};
await FirebaseDatabase.instance.ref().update(updates);
  • Keep individual write operations under 256KB.
  • Use listeners for real-time updates rather than polling.

6. Designing for Scale

  • Realtime Database scales to ~200,000 concurrent connections and 1,000 writes/second per database. For higher scale, shard data across multiple database instances.
  • Avoid storing large blobs — use Firebase Storage for files.
  • Use server timestamps for consistent time tracking:
dart
await FirebaseDatabase.instance.ref('posts/123/timestamp').set(ServerValue.timestamp);
  • Implement fan-out patterns for data accessed from multiple paths.
  • Avoid deep nesting — it leads to performance issues when retrieving data.

7. Offline Capabilities and Presence

dart
FirebaseDatabase.instance.setPersistenceEnabled(true);

// Keep critical paths synced when offline
FirebaseDatabase.instance.ref('important-data').keepSynced(true);

Connection State and Presence

dart
// Detect connection state
FirebaseDatabase.instance.ref('.info/connected').onValue.listen((event) {
  final connected = event.snapshot.value as bool? ?? false;
  if (connected) {
    // Set online status and configure onDisconnect cleanup
    final presenceRef = FirebaseDatabase.instance.ref('status/${uid}');
    presenceRef.set({'online': true, 'last_seen': ServerValue.timestamp});
    presenceRef.onDisconnect().set({
      'online': false,
      'last_seen': ServerValue.timestamp,
    });
  }
});
  • Use value events (onValue) to read data and get notified of updates — optimized for online/offline transitions.
  • Use get() only when data is needed once; it probes local cache if the server is unavailable.
  • onDisconnect() operations are executed server-side, ensuring cleanup even if the app crashes.

8. Security

json
{
  "rules": {
    "users": {
      "$uid": {
        ".read": "$uid === auth.uid",
        ".write": "$uid === auth.uid"
      }
    }
  }
}
  • Use .read, .write, .validate, and .indexOn to control access and validate data.
  • Read and write rules cascade in Realtime Database — a parent rule granting access cannot be revoked by a child rule.
  • Use the auth variable to authenticate users in security rules.

Data Validation Example

json
{
  "rules": {
    "messages": {
      "$messageId": {
        ".validate": "newData.hasChildren(['text', 'sender', 'timestamp'])",
        "text": {
          ".validate": "newData.isString() && newData.val().length <= 500"
        }
      }
    }
  }
}
  • Test rules thoroughly using the Firebase console's rules simulator.
  • Use the Firebase Emulator Suite for local testing.

References

Frequently asked questions

What does the Firebase Database AI skill do?

Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.

Why use Firebase Database on TypingMind?

Because you install it once and use it with any model. Firebase Database 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 Firebase Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/evanca/flutter-ai-rules/tree/main/skills/firebase-database. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Firebase Database?

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 Firebase Database?

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

Is the Firebase Database AI skill free?

Yes. It is published on GitHub by evanca 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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