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Firebase In App Messaging

Community
evanca
firebase-in-app-messaging

Use when running in-app message campaigns, triggering/suppressing messages, configuring opt-in data collection, testing on specific devices, or handling message callbacks.

Overview

Publisherevanca
Repositoryflutter-ai-rules
Skill namefirebase-in-app-messaging
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 In App Messaging 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-in-app-messaging
  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-in-app-messaging .claude/skills/firebase-in-app-messaging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firebase In App Messaging 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 In App Messaging 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 In App Messaging 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 In-App Messaging Skill

This skill defines how to correctly implement Firebase In-App Messaging in Flutter applications, covering setup, programmatic triggers, privacy controls, testing workflows, and campaign management.

When to Use

Use this skill when:

  • Setting up Firebase In-App Messaging in a Flutter project.
  • Triggering or suppressing in-app messages programmatically.
  • Implementing opt-in data collection for GDPR or user privacy compliance.
  • Testing campaigns with specific devices before rollout.
  • Configuring message types, targeting rules, and A/B tests.

1. Setup and Configuration

flutter pub add firebase_in_app_messaging
dart
import 'package:firebase_in_app_messaging/firebase_in_app_messaging.dart';
  • Initialize Firebase before using any In-App Messaging features.
  • In-App Messaging retrieves messages from the server once per day by default to conserve power.

Setup Checklist

  1. Confirm Firebase.initializeApp() completes before accessing In-App Messaging.
  2. Create a test campaign in the Firebase console to verify the integration.
  3. Locate the Installation ID for device-specific testing (see Testing section).

2. Message Triggering and Display

Use Google Analytics events to trigger in-app messages without additional code. For programmatic triggering:

dart
// Trigger a message tied to a custom event
await FirebaseInAppMessaging.instance.triggerEvent("purchase_complete");

Suppress Messages During Critical Flows

dart
class PaymentScreen extends StatefulWidget {
  @override
  State<PaymentScreen> createState() => _PaymentScreenState();
}

class _PaymentScreenState extends State<PaymentScreen> {
  @override
  void initState() {
    super.initState();
    // Suppress messages during payment
    FirebaseInAppMessaging.instance.setMessagesSuppressed(true);
  }

  @override
  void dispose() {
    // Re-enable messages when leaving payment flow
    FirebaseInAppMessaging.instance.setMessagesSuppressed(false);
    super.dispose();
  }
}
  • Suppression is automatically turned off on app restart.
  • Suppressed messages are ignored — their trigger conditions must be met again after suppression is lifted.
  • Common flows to suppress: payment processing, onboarding wizards, form submission screens.

3. User Privacy and Opt-In Data Collection

By default, In-App Messaging automatically delivers messages to all targeted users.

Disable automatic collection — iOS (Info.plist):

xml
<key>FirebaseInAppMessagingAutomaticDataCollectionEnabled</key>
<false/>

Disable automatic collection — Android (AndroidManifest.xml):

xml
<meta-data
    android:name="firebase_inapp_messaging_auto_data_collection_enabled"
    android:value="false" />

Enable for users who opt in at runtime:

dart
// Call after user consents to data collection
Future<void> enableMessaging() async {
  await FirebaseInAppMessaging.instance.setAutomaticDataCollectionEnabled(true);
}

// Call if user revokes consent
Future<void> disableMessaging() async {
  await FirebaseInAppMessaging.instance.setAutomaticDataCollectionEnabled(false);
}
  • Manually set preferences persist through app restarts, overriding configuration file values.
  • For GDPR compliance, disable automatic collection by default and enable only after explicit user consent.

4. Testing and Debugging

Find the Installation ID

  • Android: Filter logcat by FIAM.Headless — look for Starting InAppMessaging runtime with Installation ID YOUR_INSTALLATION_ID.
  • iOS: Add -FIRDebugEnabled as a runtime argument in Xcode scheme settings. Look for [Firebase/InAppMessaging][I-IAM180017] in the console.

Test a Campaign

  1. In the Firebase console, open the campaign and select Test on your Device.
  2. Enter the Installation ID from the step above.
  3. The test message appears on the next app launch or foreground event.
  • Always test on actual devices for proper rendering and behavior.
  • Test each message type (modal, banner, card, image-only) to verify layout on different screen sizes.

5. Campaign Management

  • Create campaigns in the Firebase console under Messaging > In-App Messaging.
  • Message types: modal, banner, card, or image-only.

Campaign Configuration Workflow

  1. Design — Select the message type and customize appearance (title, body, image, button).
  2. Target — Define the audience by app version, language, user segment, or Analytics-based conditions.
  3. Schedule — Set start/end dates and frequency caps (once, once per session, etc.).
  4. Trigger — Choose the Analytics event that displays the message (e.g., app_open, purchase_complete).
  5. Test — Use device testing before publishing.
  6. Publish — Launch the campaign and monitor performance.
  • Use custom metadata (key-value pairs) to pass additional info accessible when users interact with messages.
  • Use A/B testing to optimize message content, timing, and conversion rates.
  • Monitor campaign performance (impressions, clicks, conversions) through Firebase console analytics.

References

Frequently asked questions

What does the Firebase In App Messaging AI skill do?

Use when running in-app message campaigns, triggering/suppressing messages, configuring opt-in data collection, testing on specific devices, or handling message callbacks.

Why use Firebase In App Messaging on TypingMind?

Because you install it once and use it with any model. Firebase In App Messaging 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 In App Messaging in TypingMind?

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

Which AI models can use Firebase In App Messaging?

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 In App Messaging?

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

Is the Firebase In App Messaging 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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