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Firebase Crashlytics

Community
evanca
firebase-crashlytics

Use when implementing crash reporting, capturing fatal/non-fatal errors, recording isolate/async exceptions, customizing reports, or uploading obfuscated symbols.

Overview

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

Use it in TypingMind

Enable Firebase Crashlytics 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 Crashlytics 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 Crashlytics 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 Crashlytics Skill

This skill defines how to correctly use Firebase Crashlytics in Flutter applications.

When to Use

Use this skill when:

  • Implementing crash reporting in a Flutter project.
  • Capturing fatal errors, non-fatal exceptions, and async/isolate errors.
  • Customizing crash reports with keys, logs, and user identifiers.
  • Configuring opt-in data collection or disabling reporting in debug builds.
  • Uploading symbol files for obfuscated builds.

1. Setup and Configuration

flutter pub add firebase_crashlytics
flutter pub add firebase_analytics  # enables breadcrumb logs for better crash context

Run flutterfire configure to update the Firebase configuration and add the required Crashlytics Gradle plugin for Android.

dart
import 'package:firebase_crashlytics/firebase_crashlytics.dart';

Obfuscated code:

  • For apps built with --split-debug-info and/or --obfuscate, upload symbol files for readable stack traces.
  • iOS: Flutter 3.12.0+ and Crashlytics Flutter plugin 3.3.4+ handle symbol upload automatically.
  • Android: Use Firebase CLI (v11.9.0+) to upload Flutter debug symbols:
bash
firebase crashlytics:symbols:upload --app=FIREBASE_APP_ID PATH/TO/symbols

2. Error Handling

Configure comprehensive error capture in main() to catch errors from all sources:

Fatal Flutter errors:

dart
void main() async {
  WidgetsFlutterBinding.ensureInitialized();
  await Firebase.initializeApp();

  // Pass all uncaught fatal errors from the framework to Crashlytics
  FlutterError.onError = FirebaseCrashlytics.instance.recordFlutterFatalError;

  // Catch async errors not handled by the Flutter framework
  PlatformDispatcher.instance.onError = (error, stack) {
    FirebaseCrashlytics.instance.recordError(error, stack, fatal: true);
    return true;
  };

  runApp(MyApp());
}

Non-fatal Flutter errors: use recordFlutterError instead of recordFlutterFatalError.

Isolate errors:

dart
Isolate.current.addErrorListener(RawReceivePort((pair) async {
  final List<dynamic> errorAndStacktrace = pair;
  await FirebaseCrashlytics.instance.recordError(
    errorAndStacktrace.first,
    errorAndStacktrace.last,
    fatal: true,
  );
}).sendPort);

Caught exceptions (non-fatal):

dart
await FirebaseCrashlytics.instance.recordError(
  error,
  stackTrace,
  reason: 'a non-fatal error',
  information: ['further diagnostic information about the error', 'version 2.0'],
);
  • Crashlytics only stores the most recent 8 non-fatal exceptions per session — older ones are discarded.

3. Crash Report Customization

Custom keys (max 64 key/value pairs, up to 1 kB each):

dart
FirebaseCrashlytics.instance.setCustomKey('str_key', 'hello');
FirebaseCrashlytics.instance.setCustomKey('bool_key', true);
FirebaseCrashlytics.instance.setCustomKey('int_key', 1);

Custom log messages (limit: 64 kB per session):

dart
FirebaseCrashlytics.instance.log("User tapped on payment button");

User identifier:

dart
FirebaseCrashlytics.instance.setUserIdentifier("user-123");
// Clear by setting to blank string
FirebaseCrashlytics.instance.setUserIdentifier("");
  • Avoid putting unique values (user IDs, timestamps) directly in exception messages — use custom keys instead.

4. Performance and Optimization

  • Crashlytics processes exceptions on a dedicated background thread to minimize performance impact.
  • Fatal reports are sent in real-time without requiring an app restart.
  • Non-fatal reports are written to disk and sent with the next fatal report or on app restart.
  • Use breadcrumb logs (requires Firebase Analytics) to understand user actions leading up to a crash.

Disable Crashlytics in debug builds:

dart
if (kReleaseMode) {
  await FirebaseCrashlytics.instance.setCrashlyticsCollectionEnabled(true);
} else {
  await FirebaseCrashlytics.instance.setCrashlyticsCollectionEnabled(false);
}

5. Testing and Debugging

Force a test crash to verify the setup:

dart
FirebaseCrashlytics.instance.crash();

Verification workflow:

  1. Build and run the app in release mode.
  2. Trigger the test crash.
  3. Reopen the app so the crash report is uploaded.
  4. Check the Firebase Console Crashlytics dashboard within 5 minutes.
  5. Verify that custom keys, logs, and user identifiers appear on the crash report.
  • Verify stack traces are properly symbolicated when using code obfuscation.

6. Opt-in Reporting

By default, Crashlytics automatically collects crash reports for all users.

To give users control over data collection:

  • Disable automatic reporting and enable it only via setCrashlyticsCollectionEnabled(true) when users opt in.
  • The override value persists across all subsequent app launches.
  • To opt a user out, pass false — this applies from the next app launch.
  • When disabled, crash info is stored locally; if later enabled, locally stored crashes are sent to Crashlytics.

References

Frequently asked questions

What does the Firebase Crashlytics AI skill do?

Use when implementing crash reporting, capturing fatal/non-fatal errors, recording isolate/async exceptions, customizing reports, or uploading obfuscated symbols.

Why use Firebase Crashlytics on TypingMind?

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

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

Which AI models can use Firebase Crashlytics?

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 Crashlytics?

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

Is the Firebase Crashlytics 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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