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android-benchmark-comparison — Claude Code skill: how to install

Enforces a reproducible procedure for comparing physical Android benchmark configurations. Helps resolve reversed rankings and pick measured defaults safely.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add chrisbanes/skills --skill android-benchmark-comparison -a claude-code
Open on GitHub

What android-benchmark-comparison does

Treats physical Android benchmark comparisons as reproducible experiments. The skill requires recording exact build IDs, thermal states, brightness levels, and background loads before analyzing results. Missing or failed runs must be tracked explicitly. Slower iterations cannot be discarded after seeing the numbers, and run orders must be balanced or reversed across repetitions.

When rankings reverse between runs, any default decision is put on hold. Resolving these reversals requires inspecting hardware traces for thread contention, thermal changes, and CPU placement during overlapping timestamps. Fixed-performance mode does not prove CPU placement. If CPU affinity was adjusted, the original topology must be verified, restored, and checked again before the next benchmark run.

This skill does not diagnose Compose code issues without a configuration comparison. CPU frame duration does not measure GPU shader execution. Hardware parameters like CPU masks or screen brightness belong in a project runbook, not as hardcoded repository defaults.

What it helps with

  • compare physical Android benchmark configurations using reproducible experiments
  • investigate reversed configuration rankings across benchmark repetitions
  • verify CPU affinity and fixed-performance modes against trace timestamps
  • aggregate unrounded observations without discarding slower iterations

Example prompts for the agent

  • "Help me compare benchmark results between two configurations on a physical Android device."
  • "Investigate why configuration rankings reversed between our initial and repeat benchmark runs."
  • "Check overlapping trace timestamps to verify CPU placement and confirm affinity restoration."

How to install android-benchmark-comparison

1Skills CLI (Vercel)

npx skills add chrisbanes/skills --skill android-benchmark-comparison -a claude-code

2Manually: put the files in place

~/.claude/skills/android-benchmark-comparison/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/android-benchmark-comparison/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about android-benchmark-comparison

How do I install android-benchmark-comparison in Claude Code?

Run npx skills add chrisbanes/skills --skill android-benchmark-comparison -a claude-code.

What does android-benchmark-comparison do?

It provides an experimental protocol for running Android benchmarks on physical hardware, enforcing balanced run orders, trace validation, and controlled device settings.

Can this skill diagnose Compose performance issues directly in application code?

No. The skill is explicitly not intended for code-level Compose performance diagnosis without a configuration comparison. It focuses on physical device conditions, thermal states, and benchmark reproducibility.

Source text

SKILL.md · Apache-2.0
---
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

1. 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.
2. 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.
3. 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.
4. When rankings reverse or variability is material, defer a firm default
   decision until the reversal is resolved. Give the complete next comparison,
   not just its first blocker: confirm the same named cases and iterations,
   repeat with balanced or reversed run order, and inspect trace data whose
   timestamps overlap each measured interval for placement, contention, or
   thermal changes. Fixed-performance mode does not
   prove CPU placement. If an affinity experiment was attempted, discover the
   device topology and verify placement during the measured interval. Restore
   the recorded original affinity settings after the experiment and verify that
   restoration before another run; do not merely note that restoration needs
   checking. Label verified affinity runs as controlled comparisons. If the
   original settings or any other check are unavailable, state the gap and keep
   any default choice explicitly provisional.
5. 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.
6. 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.
7. Finish with the raw-evidence location, completed-case counts, variability,
   trace findings, controls and restoration status, plus the bounded decision
   or remaining uncertainty. When a reversal is unresolved, state the full
   sequence still needed: matching coverage, balanced or reversed order,
   measured-interval trace inspection, and restoration of any changed affinity
   settings. If fixed-performance mode or an affinity command is cited, state
   that neither proves CPU placement without trace verification. Name *run order* explicitly in the recommendation: a "balanced
   comparison" alone does not tell the team to balance or reverse run order.
   Label any earlier default choice provisional.

## 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.

© chrisbanes · Apache-2.0 · chrisbanes/skills

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