Instrument mobile apps for crash-free rate, ANR and hang detection, and release health you can gate rollouts on. Use when setting up mobile monitoring or deciding whether a release is healthy enough to expand.
Installs into .claude/skills of the current project.
Are you the author of Mobile Observability?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/amey-thakur-mobile-observability)
---
name: mobile-observability
description: Instrument mobile apps for crash-free rate, ANR and hang detection, and release health you can gate rollouts on. Use when setting up mobile monitoring or deciding whether a release is healthy enough to expand.
---
# Mobile observability
You cannot attach a debugger to a customer's phone. Observability is the
replacement: every question you will ask during an incident must be
answered by data the app already ships home.
## Method
1. **Anchor on crash-free sessions.** Crash-free sessions (not users) is
the rollout gate metric; set a floor (99.5% is a common bar) and halt
expansion below it. Track it per release version, since blends hide a
bad build behind a good installed base.
2. **Symbolicate everything, automatically.** Upload dSYMs / mapping files
in CI for every build, including bitcode-stripped and R8-minified ones.
An unsymbolicated stack trace is a support ticket, not a diagnosis.
3. **Watch responsiveness, not just crashes.** Android ANRs and iOS hangs
(main thread blocked > ~250ms) hurt more users than crashes and are
store-ranking inputs. Instrument startup time (cold and warm), frame
drops on the key scrolling surfaces, and the p95 of your three
business-critical flows.
4. **Ship breadcrumbs, scrub PII.** Navigation events, network failures,
and flag states attached to each crash report reconstruct the path into
the failure. Strip tokens, emails, and free-text user content at the
SDK layer; observability must not become a data-leak vector.
5. **Segment before you debug.** Every metric sliced by app version, OS
version, device class, and network type; most "mystery" regressions are
one OS release or one low-memory device tier. An on-demand log-level
bump via remote flag turns a reproducing user into a trace without a
new build.
6. **Alert on releases, not noise.** Page on: new crash signature trending
in the latest version, crash-free rate crossing the floor, ANR rate
above the store's bad-behavior threshold. Everything else is a
dashboard reviewed at rollout checkpoints.
## Boundaries
- Session replay and screen recording carry consent and privacy weight;
involve legal review before enabling them, and never in auth or payment
flows.
- Offline and low-end devices delay telemetry; judge a rollout on 24h of
data, not the first hour.
- Observability tells you what broke, not why users leave; product
analytics is a separate discipline with separate consent.