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android-benchmark-comparison
GitHub用于对比 Android 物理设备基准测试配置,排查排名不一致或基于测量结果选择默认值。强调实验可重复性、控制设备条件及统计分析,不用于无配置对比的代码级性能诊断。
触发场景
比较不同 Android 硬件或软件配置的基准测试结果
调查基准测试排名不一致或波动问题
基于实测数据选择 Android 默认配置
安装
npx skills add chrisbanes/skills --skill android-benchmark-comparison -g -y
SKILL.md
Frontmatter
{
"name": "android-benchmark-comparison",
"description": "Use when comparing physical Android benchmark configurations, investigating inconsistent rankings, or selecting an Android default from measured results. Do not use for code-level Compose performance diagnosis without a configuration comparison."
}
Android benchmark comparison
Core principle
Treat a physical Android configuration comparison as a reproducible experiment: verify comparable workloads and device conditions before interpreting a ranking or choosing a default.
Procedure
- State the decision, configurations, workloads, metric definitions, and repetitions. Preserve exact build identity, configuration, raw results, and traces; then verify every intended case and iteration ran. Distinguish missing, failed, and excluded runs; do not compare only the fastest survivors.
- Control and record relevant device conditions, including device model and state, thermal and power mode, display brightness, background load, and network or input conditions. Keep device-specific commands and CPU masks in the project's runbook.
- Balance or reverse run order and repeat the comparison. Report the spread and whether the ordering holds; do not discard slow iterations after seeing the result.
- When rankings reverse or variability is material, investigate before making the decision. Use traces to check the measured interval and plausible causes such as CPU placement, contention, or thermal state. A fixed- performance setting does not prove CPU placement. If affinity is used, discover the device topology, verify placement during the measured trace interval, label the outcome a controlled comparison, and restore device settings afterwards.
- Calculate summaries from unrounded observations, then round only for presentation. Name the aggregation explicitly: the mean of per-run percentiles is not a percentile of pooled observations. Choose an aggregation that answers the stated decision; do not prescribe one statistic universally.
- Separate controlled-experiment evidence from normal user performance. If several conditions changed together, report the comparison as more controlled but do not attribute its whole difference to one control. Use CPU frame-duration evidence to inform a visual quality/performance decision, without claiming it measures GPU shader time.
- Finish with the raw-evidence location, completed-case counts, variability, trace findings, controls and restoration status, plus the bounded decision or remaining uncertainty.
Boundaries
- A single stable benchmark run can support a narrow observation, but not a robust configuration ranking.
- Do not turn a device-specific CPU mask, brightness value, iteration count, or summary statistic into a permanent default.
- When traces or repeat coverage cannot resolve a reversal, keep the default unchanged or make a provisional decision with that limitation explicit.
版本历史
- 2026.9.21 当前 2026-09-22 08:33


