# Lookatitude Guild Mobile Performance Tuning > mobile-performance-tuning - Skill: `tomevault-io/lookatitude-guild-mobile-performance-tuning` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/lookatitude-guild-mobile-performance-tuning` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/lookatitude-guild-mobile-performance-tuning/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/lookatitude-guild-mobile-performance-tuning --- # mobile-performance-tuning Implements `guild-plan.md §6.1` (mobile · performance-tuning) under `§6.4` engineering principles: measure first, optimize second; any speed-up without a before/after number is folklore. ## What you do Pick the right instrument (Instruments / Xcode Organizer, Android Studio Profiler / 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. - 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. - Test on real representative hardware — simulators hide thermal, GPU, and IO behavior. - Reduce startup work: lazy-init SDKs, move heavy work off the main thread, trim launch-time DI. - Scroll fixes: cell reuse, fixed row heights, off-screen prefetch, image decode off main. - Memory: track retained heap over time, kill caches that never evict. - Report before/after with the same device, OS, build flavor, and scenario — otherwise the comparison is noise. ## Output shape A short report: 1. **Instrument plan** — which tool, what device, what scenario, what metric. 2. **Baseline** — captured traces / numbers with screenshots of the flame graph or trace. 3. **Fix list** — ordered, each with estimated impact and cost. 4. **Post-fix metrics** — same scenario, delta from baseline. 5. **Regression guard** — an automated perf test or a CI check to prevent bitrot. ## Anti-patterns - Optimizing without measuring — random speculative changes without a baseline. - Micro-benchmarks on a high-end device when users are on a Moto G — unrepresentative. - Ignoring low-end devices entirely — median user, not p1 reviewer hardware. - Fixing cold start while making warm start worse — track all scenarios you care about. - Releasing perf wins with no regression test — the next feature undoes them silently. - Optimizing the wrong layer — e.g. JS fixes when the bottleneck is native image decode. ## Handoff Return 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. --- > Source: [lookatitude/guild](https://github.com/lookatitude/guild) — distributed by [TomeVault](https://tomevault.io).