--- name: crash-analytics description: When the user wants to monitor, triage, or reduce their app's crash rate — including setting up Crashlytics, prioritizing which crashes to fix first, interpreting crash data, and understanding how crashes affect App Store ranking. Use when the user mentions "crash", "crashlytics", "crash rate", "ANR", "app not responding", "crash-free sessions", "crash-free users", "symbolication", "stability", "firebase crashes", "app crashing", or "crash report". For overall analytics setup, see app-analytics. metadata: version: 1.0.0 --- # Crash Analytics You help triage, prioritize, and reduce app crashes — and understand how crash rate affects App Store discoverability and ratings. ## Why Crash Rate Is an ASO Signal - **App Store ranking** — Apple's algorithm penalizes apps with high crash rates - **App Store featuring** — High crash rate disqualifies editorial consideration - **Ratings** — Crashes are the #1 cause of 1-star reviews - **Retention** — A crash in the first session destroys Day 1 retention **Target:** crash-free sessions > 99.5% | crash-free users > 99% ## Tools | Tool | What it provides | Setup | |------|-----------------|-------| | **Firebase Crashlytics** | Real-time crashes, ANRs, symbolicated stack traces | Add `FirebaseCrashlytics` pod/SPM package | | **App Store Connect** | Crash rate trend, crashes per session | Built-in, no code needed | | **Xcode Organizer** | Aggregated crash logs from TestFlight + App Store | Xcode → Window → Organizer → Crashes | | **MetricKit** | On-device diagnostics, hang rate, launch time | iOS 13+, automatic | **Recommended:** Crashlytics (real-time alerts + search) + App Store Connect (trend validation) ## Crashlytics Setup ### iOS (Swift) ```swift // AppDelegate or @main App struct import FirebaseCore import FirebaseCrashlytics @main struct MyApp: App { init() { FirebaseApp.configure() // Crashlytics is auto-initialized } } ``` ### Non-fatal errors (track without crashing) ```swift // Log a non-fatal error Crashlytics.crashlytics().record(error: error) // Log a custom key for debugging context Crashlytics.crashlytics().setCustomValue(userId, forKey: "user_id") Crashlytics.crashlytics().setCustomValue(screenName, forKey: "current_screen") ``` ### Android (Kotlin) ```kotlin // build.gradle (app) implementation("com.google.firebase:firebase-crashlytics:18.x.x") // No additional code needed — auto-captures unhandled exceptions // For non-fatal: FirebaseCrashlytics.getInstance().recordException(throwable) ``` ## Triage Framework Not all crashes are equal. Prioritize by impact: **Priority Score = Crash Frequency × Affected Users × User Segment Weight** | Priority | Criteria | Response time | |----------|---------|---------------| | P0 — Critical | Crashes on launch / checkout / core feature; >1% of sessions | Fix today | | P1 — High | Crashes in common flows; >0.1% of sessions | Fix this release | | P2 — Medium | Edge case crashes; <0.1% of sessions | Fix next release | | P3 — Low | Rare, non-blocking crashes; <0.01% of sessions | Backlog | ### Crashlytics Dashboard Triage 1. Sort by **"Impact"** (unique users affected), not frequency 2. Group: `onboarding`, `checkout`, `core feature`, `background`, `launch` 3. Assign P0/P1 to the top 3–5 issues 4. Set a **velocity alert** in Crashlytics for any issue affecting >0.5% of users ## Reading a Crash Report ``` Fatal Exception: com.example.NullPointerException at com.example.UserProfileVC.loadData:87 at com.example.HomeVC.viewDidLoad:45 Keys: user_id: 12345 current_screen: "home" app_version: "2.3.1" os_version: "iOS 17.3" ``` **Steps to debug:** 1. Open the file and line in Xcode (`UserProfileVC.swift:87`) 2. Check what can be nil at that point 3. Reproduce with the user context (OS version, device, screen) 4. Write a failing test before fixing ## Symbolication Crashlytics auto-symbolicates if you upload dSYMs. If you see unsymbolicated traces: ```bash # Manually upload dSYMs ./Pods/FirebaseCrashlytics/upload-symbols -gsp GoogleService-Info.plist -p ios MyApp.app.dSYM ``` For Bitcode-enabled builds, download dSYMs from App Store Connect → Activity → Build → dSYMs. ## App Store Connect Crash Data - **App Store Connect → App Analytics → Crashes** — Crash rate trend per version - Compare crash rate before and after each release - A spike on a specific version = regression in that release **Crash rate formula:** Crashes / Sessions × 100 ## Release Strategy to Minimize Blast Radius Use phased releases to catch crashes before full rollout: **iOS:** App Store Connect → Version → Phased Release (7-day rollout: 1% → 2% → 5% → 10% → 20% → 50% → 100%) **Android:** Play Console → Production → Managed publishing → Rollout percentage **Rule:** Monitor Crashlytics for 24 hours at each phase. If crash rate increases >0.2%, pause rollout. ## Responding to Crash-Driven 1-Star Reviews 1. Identify the app version where crash-related 1-stars appeared 2. Fix the crash 3. Reply to each crash-related review: "Fixed in version X.X — please update" 4. After update ships, use `rating-prompt-strategy` to recover rating ## Output Format ### Crash Audit Report ``` Stability Report — [App Name] v[version] ([period]) Crash-free sessions: [X]% (target: >99.5%) Crash-free users: [X]% (target: >99%) Top crash issues: P0 Issues (fix immediately): #1 [Exception type] — [X] users, [X]% of sessions File: [filename:line] Cause: [hypothesis] Fix: [specific action] P1 Issues (this release): #2 [Exception type] — [X] users, [X]% of sessions ... Action Plan: Today: Fix P0 issue #1 → release hotfix This week: Fix P1 issues #2, #3 → include in v[X.X] Monitoring: Set velocity alert at 0.5% session threshold ``` ## Related Skills - `app-analytics` — Full analytics stack; Crashlytics is one piece - `rating-prompt-strategy` — Recover rating after fixing crash-driven 1-stars - `review-management` — Respond to crash-related reviews - `retention-optimization` — Crashes on Day 1 destroy retention metrics - `app-store-featured` — Crash rate > 2% disqualifies editorial featuring --- Source attribution: Eronred Original project: https://github.com/Eronred/aso-skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 SkillsHub Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Eronred Original project: https://github.com/Eronred/aso-skills Attribution basis: Original repository namespace preserved by package mirror Fields of work: Development & Engineering, Marketing, Product Management Roles: Growth Marketer, Mobile Developer, Product Manager Specialties: API, Mobile Tools: Firebase Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a13641ba9909ffe5df37d4273d4b6b9b4ba31225573d73af6ff52681b4d85051 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.