Android performance observability

Android skills repository for Kotlin, Compose, XML, testing, CI, release work, and legacy upgrades

Install

npx -y skills add krutikJain/android-agent-skills --skill android-performance-observability

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Measure startup, rendering, memory, jank, vitals, logs, and crash signals for Android apps with actionable traces.

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Use this skill when the request is about: android performance profiling, baseline profile or macrobenchmark, app startup issue android.
Primary outcome: Measure startup, rendering, memory, jank, vitals, logs, and crash signals for Android apps with actionable traces.
Read references/patterns.md when you need the measurement ladder for startup, jank, traces, and production signals.
Read references/scenarios.md for repeatable profiling and trace-oriented entry points.
Handoff skills when the scope expands:
android-compose-performance
android-ci-cd-release-playstore

Classify the symptom before choosing tools: cold start, warm start, frame/jank, scrolling, memory, ANR, crash, battery, or production vitals drift.
Measure on release-like builds and physical devices whenever possible; avoid debugging from debug-only traces or profile-unfriendly builds.
Pick the smallest tool that answers the question: Macrobenchmark for startup/scroll numbers, Baseline Profiles for ahead-of-time optimization, Perfetto/System Tracing for deep traces, JankStats or FrameMetrics for frame quality, and Play Vitals for field evidence.
Change one thing at a time, then compare before and after traces or benchmark outputs instead of stacking multiple optimizations blindly.
Hand off UI-specific rendering changes or release rollouts only after the measurement surface is stable and the bottleneck is evidenced.

Treat benchmarks, traces, and vitals as different evidence sources with different noise profiles; do not mix them casually.
Prefer reproducible release-build measurements over debug-build intuition.
Tie optimizations back to user-facing metrics such as startup time, frame pacing, ANRs, or battery impact.
Keep the profiling setup stable enough that regressions are attributable to code changes instead of device or environment churn.

Chasing micro-optimizations before identifying whether the problem is startup, rendering, I/O, or field reliability.
Reading one noisy trace and presenting the result as settled fact.
Measuring debug builds and assuming the same behavior in production.
Adding Baseline Profiles or macrobenchmarks without checking whether the target path is stable enough to compare.

Startup: cold and warm launch, expensive initialization, and Baseline Profile coverage.
Rendering: jank, skipped frames, Compose or View invalidation churn, and long main-thread work.
Memory and reliability: allocations, leaks, ANRs, crashes, and Play Vitals trends.
Evidence quality: repeatable commands, release-like variants, and documented before/after comparisons.

Scenario: Audit the repo for profiling surfaces and benchmark-related hooks before proposing a measurement plan.
Command: rg -n "baseline|macrobenchmark|profileable|JankStats|Trace|Perfetto" .

Scenario: Keep startup and rendering investigations grounded in repeatable release-like builds.
Command: cd examples/orbittasks-compose && ./gradlew :app:assembleDebug

Scenario: Keep observability requests distinct from Compose-only tuning or release automation.
Command: python3 scripts/eval_triggers.py --skill android-performance-observability