mobile-observability

🥈Silver

Mobile observability plugin for Claude Code. Enables crash reporting, performance monitoring, and instrumentation for iOS, Android, and React Native apps. Helps operations teams debug production issues and optimize app performance.

10890Updated 2w ago

Intermediate30min to implementautomation

Saves ~60 min per use

Quick InstallView Source

git clone https://github.com/nexus-labs-automation/mobile-observability.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Setup Integration**: Install the mobile-observability plugin in your Claude Code environment and configure it with your app's crash reporting SDK (Firebase Crashlytics for iOS/Android, React Native Firebase for React Native apps). Ensure you have access to production crash logs and performance metrics. 2. **Data Collection**: Run the plugin with your app's bundle identifier or package name. For React Native apps, specify the platform (iOS/Android) and version range. Use the '--time-period' flag to focus on recent data (e.g., '--time-period 2weeks'). 3. **Analysis Execution**: Execute the analysis command in Claude Code: `mobile-observability analyze --app ShopEase --platform react-native --time-period 2weeks --crash-type frequency`. Review the generated report for crash trends and performance metrics. 4. **Action Planning**: Use the output to create a prioritized fix list. For each identified issue, run targeted debugging commands like `mobile-observability debug --crash-id null-pointer-cart` or `mobile-observability profile --metric frame-rate`. 5. **Validation**: After implementing fixes, re-run the analysis with the same parameters to verify improvements. Use the '--compare' flag to compare before/after metrics: `mobile-observability analyze --app ShopEase --compare 2024-05-01 2024-05-15`.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/nexus-labs-automation/mobile-observability

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the crash reports and performance metrics for [APP_NAME] ([PLATFORM]) from the last [TIME_PERIOD]. Identify the top 3 most frequent crashes by [CRASH_TYPE] and their root causes. For each crash, suggest specific code fixes or configuration changes to resolve the issue. Include performance bottlenecks affecting [METRIC] like frame rate or API response time. Provide a prioritized action plan for the engineering team with estimated effort and impact.

Example Output

For the mobile app 'ShopEase' (React Native, iOS/Android) over the past 2 weeks, the analysis revealed critical issues impacting user retention. The top crash affecting 12.4% of sessions is 'NullPointerException in CartScreen.js' caused by race conditions during state updates. This occurs when users rapidly tap 'Add to Cart' buttons, triggering concurrent state mutations. The second most frequent crash (8.7% of sessions) is 'Memory Leak in ImageLoader' due to unclosed file handles in the image caching system, particularly on Android devices with Android 12+.

Performance analysis showed a 34% drop in frame rate during checkout flows, correlating with spikes in 'API Timeout' errors (avg 2.8s vs 800ms baseline). The main bottleneck was identified in the payment gateway integration where 4 parallel network calls were made without proper cancellation handling. Memory usage spiked to 1.2GB during image-heavy product listings, with 68% of devices exceeding the recommended 1GB threshold.

Recommended fixes include: 1) Implementing debounce logic in CartScreen.js (15 min effort, high impact), 2) Adding proper resource cleanup in ImageLoader (20 min effort, medium impact), 3) Optimizing payment API calls with request batching (45 min effort, high impact), and 4) Implementing adaptive image loading based on device memory (60 min effort, high impact). The engineering team should prioritize these fixes based on the crash frequency and performance degradation metrics.