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Firebase Analytics

Community
evanca
firebase-analytics

Use when logging analytics events, setting user properties, configuring default event parameters, building funnels, or adding screen-view tracking.

Overview

Publisherevanca
Repositoryflutter-ai-rules
Skill namefirebase-analytics
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 Analytics 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-analytics
  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-analytics .claude/skills/firebase-analytics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firebase Analytics 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 Analytics 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 Analytics 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 Analytics Skill

This skill defines how to correctly implement Firebase Analytics in Flutter applications, covering setup, event logging, user properties, and data collection best practices.

When to Use

Use this skill when:

  • Setting up and configuring Firebase Analytics in a Flutter project.
  • Logging predefined or custom analytics events.
  • Setting user properties or default event parameters.
  • Implementing screen view tracking with GoRouter or Navigator observers.
  • Building conversion funnels or tracking user flows.

1. Setup and Configuration

flutter pub add firebase_analytics
flutter run
dart
import 'package:firebase_analytics/firebase_analytics.dart';

// After Firebase.initializeApp():
FirebaseAnalytics analytics = FirebaseAnalytics.instance;
  • Initialize Firebase before using any Firebase Analytics features.
  • Analytics automatically logs some events and user properties — no additional code needed for those.
  • On iOS, if your app does not use the IDFA (Advertising Identifier), use the IDFA-free Analytics dependency (FirebaseAnalyticsCore under Swift Package Manager, or FirebaseAnalytics/Core under CocoaPods) instead of the default FirebaseAnalytics dependency to avoid App Store review questions about advertising identifiers:
    • Swift Package Manager: set FIREBASE_ANALYTICS_WITHOUT_ADID=true when building (FIREBASE_ANALYTICS_WITHOUT_ADID=true flutter build ios).

Add Navigator Observer for Automatic Screen Tracking

dart
MaterialApp(
  navigatorObservers: [
    FirebaseAnalyticsObserver(analytics: FirebaseAnalytics.instance),
  ],
);

For GoRouter, log screen views manually on route changes:

dart
GoRouter(
  observers: [FirebaseAnalyticsObserver(analytics: FirebaseAnalytics.instance)],
);

Verification Checklist

  1. Confirm Firebase.initializeApp() completes before accessing FirebaseAnalytics.instance.
  2. Run the app and check the Firebase DebugView console for incoming events.
  3. Confirm automatic events (first_open, session_start) appear without extra code.

2. Event Logging

Use predefined event methods when possible for maximum detail in reports and access to future Google Analytics features:

dart
await FirebaseAnalytics.instance.logSelectContent(
  contentType: "image",
  itemId: itemId,
);

Use the general logEvent() method for both predefined and custom events:

dart
await FirebaseAnalytics.instance.logEvent(
  name: "select_content",
  parameters: {
    "content_type": "image",
    "item_id": itemId,
  },
);

Custom Event Example — E-commerce Add-to-Cart

dart
Future<void> logAddToCart(String productId, String productName, double price) async {
  await FirebaseAnalytics.instance.logEvent(
    name: 'add_to_cart',
    parameters: {
      'product_id': productId,
      'product_name': productName,
      'price': price,
      'currency': 'USD',
    },
  );
}
  • Event names are case-sensitive — names differing only in case create two distinct events.
  • Up to 500 different event types with no limit on total event volume.
  • Event names must start with an alphabetic character, contain only alphanumeric characters and underscores, and be no longer than 40 characters.

3. Parameters and Properties

  • Parameter names: up to 40 characters, must start with an alphabetic character, contain only alphanumeric characters and underscores.
  • String parameter values: up to 100 characters.
  • The prefixes firebase_, google_, and ga_ are reserved — do not use them for parameter names.
  • Up to 25 custom parameters per event.
  • Register custom parameters in the Analytics console to use them as dimensions or metrics in reports.

Set default parameters for all future events (not supported on web):

dart
await FirebaseAnalytics.instance.setDefaultEventParameters({
  'app_version': '1.2.3',
  'environment': 'production',
});

Clear a default parameter by setting it to null.


4. User Properties

dart
await FirebaseAnalytics.instance.setUserProperty(
  name: 'favorite_food',
  value: favoriteFood,
);

Set the user ID to correlate events across devices:

dart
await FirebaseAnalytics.instance.setUserId(id: 'user_12345');
  • Create custom definitions for user properties in the Analytics console before using them.
  • Up to 25 custom user properties per project.
  • Use user properties for audience segmentation, report filtering, or A/B test targeting.

5. Best Practices

  • Request necessary permissions before collecting user data, especially on platforms with strict privacy controls.
  • Never log sensitive or personally identifiable information in events or user properties.
  • Use consistent naming conventions (snake_case) for custom events and parameters.
  • Group related events to track user flows and conversion funnels.
  • Use DebugView in the Firebase console during development — enable it on a physical device with:
    • Android: adb shell setprop debug.firebase.analytics.app <package_name>
    • iOS: Add -FIRDebugEnabled to scheme arguments in Xcode.
  • Test analytics implementation before deploying to production by confirming events appear in DebugView.

References

Frequently asked questions

What does the Firebase Analytics AI skill do?

Use when logging analytics events, setting user properties, configuring default event parameters, building funnels, or adding screen-view tracking.

Why use Firebase Analytics on TypingMind?

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

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

Which AI models can use Firebase Analytics?

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 Analytics?

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

Is the Firebase Analytics 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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