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Output: instrument plan, baseline, fix list, post-fix metrics. Pulled by the `mobile` specialist. TRIGGER: \"profile the app's cold-start time\", \"the scroll is janky on X screen\", \"optimize memory on Y screen\", \"mobile performance audit of X\", \"why is the app slow on low-end devices\", \"reduce battery usage on X feature\". DO NOT TRIGGER for: writing the feature in Swift (use `mobile-ios-swift`), Kotlin (use `mobile-android-kotlin`), or RN (use `mobile-react-native`); backend latency work (backend group); CI build-time optimization (devops-ci-cd-pipeline); observability of app telemetry in prod (devops-observability-setup); QA test-strategy definition (qa-test-strategy).\nwhen_to_use: The parent `mobile` specialist pulls this skill when the task requires measuring and improving runtime performance of a mobile app. Also fires on explicit user request.\ntype: specialist\n---\n\n# mobile-performance-tuning\n\nMeasure first, optimize second; any speed-up without a before\u002Fafter number is folklore.\n\n## What you do\n\nPick the right instrument (Instruments \u002F Xcode Organizer, Android Studio Profiler \u002F Perfetto, Flipper, React Native Performance Monitor), capture a baseline on a real low-end device, ship the smallest fix that moves the metric, and confirm with a post-fix capture.\n\n- Define the target metric up front: cold start (time-to-first-frame), scroll jank (p95 frame time), memory peak, steady-state CPU, battery per session.\n- Test on real representative hardware — simulators hide thermal, GPU, and IO behavior.\n- Reduce startup work: lazy-init SDKs, move heavy work off the main thread, trim launch-time DI.\n- Scroll fixes: cell reuse, fixed row heights, off-screen prefetch, image decode off main.\n- Memory: track retained heap over time, kill caches that never evict.\n- Report before\u002Fafter with the same device, OS, build flavor, and scenario — otherwise the comparison is noise.\n\n## Output shape\n\nA short report:\n\n1. **Instrument plan** — which tool, what device, what scenario, what metric.\n2. **Baseline** — captured traces \u002F numbers with screenshots of the flame graph or trace.\n3. **Fix list** — ordered, each with estimated impact and cost.\n4. **Post-fix metrics** — same scenario, delta from baseline.\n5. **Regression guard** — an automated perf test or a CI check to prevent bitrot.\n\n## Anti-patterns\n\n- Optimizing without measuring — random speculative changes without a baseline.\n- Micro-benchmarks on a high-end device when users are on a Moto G — unrepresentative.\n- Ignoring low-end devices entirely — median user, not p1 reviewer hardware.\n- Fixing cold start while making warm start worse — track all scenarios you care about.\n- Releasing perf wins with no regression test — the next feature undoes them silently.\n- Optimizing the wrong layer — e.g. JS fixes when the bottleneck is native image decode.\n\n## Handoff\n\nReturn the report path and the list of code changes (if any) to the invoking `mobile` specialist. Fix implementation typically chains to `mobile-ios-swift`, `mobile-android-kotlin`, or `mobile-react-native`. If the cause is server-side, handoff goes to the backend group. This skill does not dispatch.\n","# mobile-performance-tuning\n\nMeasure first, optimize second; any speed-up without a before\u002Fafter number is folklore.\n\n## What you do\n\nPick the right instrument (Instruments \u002F Xcode Organizer, Android Studio Profiler \u002F Perfetto, Flipper, React Native Performance Monitor), capture a baseline on a real low-end device, ship the smallest fix that moves the metric, and confirm with a post-fix capture.\n\n- Define the target metric up front: cold start (time-to-first-frame), scroll jank (p95 frame time), memory peak, steady-state CPU, battery per session.\n- Test on real representative hardware — simulators hide thermal, GPU, and IO behavior.\n- Reduce startup work: lazy-init SDKs, move heavy work off the main thread, trim launch-time DI.\n- Scroll fixes: cell reuse, fixed row heights, off-screen prefetch, image decode off main.\n- Memory: track retained heap over time, kill caches that never evict.\n- Report before\u002Fafter with the same device, OS, build flavor, and scenario — otherwise the comparison is noise.\n\n## Output shape\n\nA short report:\n\n1. **Instrument plan** — which tool, what device, what scenario, what metric.\n2. **Baseline** — captured traces \u002F numbers with screenshots of the flame graph or trace.\n3. **Fix list** — ordered, each with estimated impact and cost.\n4. **Post-fix metrics** — same scenario, delta from baseline.\n5. **Regression guard** — an automated perf test or a CI check to prevent bitrot.\n\n## Anti-patterns\n\n- Optimizing without measuring — random speculative changes without a baseline.\n- Micro-benchmarks on a high-end device when users are on a Moto G — unrepresentative.\n- Ignoring low-end devices entirely — median user, not p1 reviewer hardware.\n- Fixing cold start while making warm start worse — track all scenarios you care about.\n- Releasing perf wins with no regression test — the next feature undoes them silently.\n- Optimizing the wrong layer — e.g. JS fixes when the bottleneck is native image decode.\n\n## Handoff\n\nReturn the report path and the list of code changes (if any) to the invoking `mobile` specialist. Fix implementation typically chains to `mobile-ios-swift`, `mobile-android-kotlin`, or `mobile-react-native`. If the cause is server-side, handoff goes to the backend group. 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