android-performance

작성자: openai

adb 대상을 대상으로 Simpleperf CPU 프로파일, Perfetto 또는 Compose 트레이스, gfxinfo 프레임 데이터, dumpsys…를 사용하여 Android 성능 증거를 수집하고 해석합니다.

npx skills add https://github.com/openai/plugins --skill android-performance

Android Performance

Use this skill to capture Android performance evidence for adb-installable apps. CPU sampling usually requires a debuggable or profileable build; frame stats, Perfetto, and logcat can still help when an app cannot be sampled. Compose with ../android-emulator-qa/SKILL.md for device selection, build/install/launch, UI driving, screenshots, UI trees, and logcat capture.

Core Workflow

  1. Pick one focused user-visible flow.
  2. Choose the trace type that matches the question.
  3. Record the flow with clear start and stop boundaries.
  4. Pull or copy the trace produced by that run, then generate reports from that file.
  5. Interpret reports with caveats about device, build type, sample count, and profiler limits.

Avoid broad "use the app for a while" captures. They make traces hard to attribute and usually hide the functions that matter.

Use a local adb target for meaningful timing. Store outputs in a run-specific artifact folder outside the skill directory:

if [ -z "${ARTIFACT_DIR:-}" ]; then
  ARTIFACT_DIR="$(mktemp -d "${TMPDIR:-/tmp}/codex-android-perf.XXXXXX")"
fi
mkdir -p "$ARTIFACT_DIR"

Do not put ARTIFACT_DIR under SKILL_DIR; the skill folder is for bundled instructions and scripts, not run artifacts.

Choosing A Trace

  • Use Simpleperf when the question is "what functions are taking CPU time?" or when you need a sampled profile of Kotlin, Java, native, or framework execution.
  • Use Perfetto when the question is frame timing, startup timeline, scheduler gaps, binder work, lock contention, main-thread stalls, Compose recomposition, or why a flow felt janky.
  • Use gfxinfo framestats for a quick manual frame/jank snapshot. Pair it with Perfetto when you need root cause.
  • Use meminfo / heap dumps when the question is retained Java/Kotlin objects, PSS, native heap, or object counts after a focused flow.

Simpleperf CPU Profiles

Simpleperf --app works best when the installed package is debuggable or profileable from shell. Preflight before recording:

SERIAL="<adb-serial>"
PACKAGE="<app package>"

adb -s "$SERIAL" shell dumpsys package "$PACKAGE" | grep -Ei 'DEBUGGABLE|profileable|isProfileable' || true

If the package is not debuggable/profileable and simpleperf record --app fails, install a debug/profileable build when possible. If that is not possible, use Perfetto or gfxinfo instead of treating missing CPU samples as evidence.

Start recording in one terminal or as a long-running Codex command session:

SERIAL="<adb-serial>"
PACKAGE="<app package>"
MAX_DURATION_SECONDS=60

adb -s "$SERIAL" shell rm -f /data/local/tmp/perf.data
adb -s "$SERIAL" logcat -c

adb -s "$SERIAL" shell simpleperf record \
  --app "$PACKAGE" \
  -o /data/local/tmp/perf.data \
  -e cpu-clock -f 4000 -g \
  --duration "$MAX_DURATION_SECONDS"

While that command is running, perform exactly one focused flow with adb input, UI automation, or android-emulator-qa.

Stop Simpleperf from another command and wait for the recording command to exit:

adb -s "$SERIAL" shell 'pid="$(pidof simpleperf 2>/dev/null || true)"; [ -n "$pid" ] && kill -INT $pid'

If that returns Operation not permitted, send Ctrl-C to the original adb shell simpleperf record command session and wait for it to exit.

Pull and report the capture:

adb -s "$SERIAL" pull /data/local/tmp/perf.data "$ARTIFACT_DIR/perf.data"
adb -s "$SERIAL" logcat -d > "$ARTIFACT_DIR/logcat.txt"

SKILL_DIR="<absolute path to this loaded skill folder>"
FIRST_PARTY_REGEX="$(printf '%s' "$PACKAGE" | sed 's/\./\\./g')"
"$SKILL_DIR/scripts/simpleperf_hotspots.sh" \
  "$ARTIFACT_DIR/perf.data" \
  "$ARTIFACT_DIR" \
  --serial "$SERIAL" \
  --first-party-regex "$FIRST_PARTY_REGEX"

Do not derive SKILL_DIR from the target app repo's pwd; installed plugins usually live outside the app being profiled. Keep FIRST_PARTY_REGEX scoped to the app's package or app-owned module prefixes; avoid broad framework patterns such as kotlin, Compose, or androidx.compose when reporting app-owned rows.

The helper writes:

  • $ARTIFACT_DIR/simpleperf-self.txt
  • $ARTIFACT_DIR/simpleperf-children.txt
  • $ARTIFACT_DIR/simpleperf.csv when supported by the installed Simpleperf

If host Simpleperf is not installed, the helper searches Android Studio and Android SDK/NDK locations. If unavailable, it falls back to device-side adb shell simpleperf report when the device still has /data/local/tmp/perf.data.

Reading Simpleperf

Simpleperf reports sampled CPU execution. It does not directly measure suspended coroutines, network latency, lock wait time, or other wall-clock waits. If a flow feels slow but Simpleperf shows little app CPU, capture Perfetto to inspect scheduler gaps, binder work, locks, frame timing, and app trace sections.

Read reports this way:

  • Self/Overhead: samples where the function itself was executing. Use this for hot leaf work such as parsing, formatting, diffing, sorting, allocation-heavy iteration, or JSON/protobuf processing.
  • Children/inclusive: samples in the function and its callees. Use this for expensive entry points such as repositories, use cases, view models, Composables, startup initializers, or feature coordinators.
  • Shared Object / Symbol: prefer app-owned package frames, feature modules, domain/data/UI modules, and generated app code. Treat Android framework, Kotlin runtime, Compose, and native/runtime frames as context unless the app-owned caller is visible.
  • Percentages: useful for ranking functions inside one capture. For user-facing timing claims, pair with Perfetto, gfxinfo, or repeated wall-clock measurements.

When interpreting a hotspot, note symbol/function name, self or inclusive percentage, approximate sampled CPU time when available, caller stack or owning source file, flow steps, artifact paths, and whether the capture is single-run or repeated.

Perfetto / Compose Trace

If the app repo already documents a Perfetto/System Trace command for that project, use it. Otherwise use Perfetto directly. The light command below captures scheduler/frequency/Android atrace categories and app Trace sections for PACKAGE; it is not a substitute for a full project-specific Perfetto config when you need detailed frame timeline or Compose runtime internals.

SERIAL="<adb-serial>"
PACKAGE="<app package>"
TRACE_DURATION_SECONDS=30
TRACE_BASENAME="app-flow-$(date +%Y%m%d-%H%M%S).pftrace"
TRACE_DEVICE="/data/misc/perfetto-traces/$TRACE_BASENAME"

PERFETTO_PID="$(adb -s "$SERIAL" shell perfetto \
  --background-wait \
  -o "$TRACE_DEVICE" \
  -t "${TRACE_DURATION_SECONDS}s" \
  --app "$PACKAGE" \
  sched freq idle am wm gfx view binder_driver hal dalvik | tr -d '\r' | tail -n 1)"
printf 'Perfetto PID: %s\n' "$PERFETTO_PID"

Run exactly one focused flow before TRACE_DURATION_SECONDS expires. To stop early, gracefully terminate the background Perfetto process and give it a moment to flush:

adb -s "$SERIAL" shell kill -TERM "$PERFETTO_PID" 2>/dev/null || true
adb -s "$SERIAL" shell "
  last_size=-1
  stable_count=0
  i=0
  while [ \$i -lt 30 ]; do
    size=\$(ls -l '$TRACE_DEVICE' 2>/dev/null | awk '{ print \$5 }')
    if [ -n \"\$size\" ] && [ \"\$size\" -gt 0 ] && [ \"\$size\" = \"\$last_size\" ]; then
      stable_count=\$((stable_count + 1))
      [ \$stable_count -ge 2 ] && exit 0
    else
      stable_count=0
    fi
    last_size=\"\${size:-0}\"
    i=\$((i + 1))
    sleep 1
  done
  exit 1
"

Prefer letting TRACE_DURATION_SECONDS expire instead of stopping early. If the stop command fails because the trace already ended, still wait until the output file exists and its size is stable before pulling. If the direct command is too coarse, use Android Studio System Trace or a project-specific Perfetto config. Only report frame timeline or Compose recomposition details when those tracks/events are actually present in the captured trace; the light command above does not guarantee them.

Pull the exact on-device trace from this run:

adb -s "$SERIAL" pull "$TRACE_DEVICE" "$ARTIFACT_DIR/$TRACE_BASENAME"

In Perfetto, inspect:

  • main-thread slices around missed frames or long startup sections
  • frame scheduling, frame timeline, and render thread lanes
  • Compose runtime tracing sections for recomposition work when enabled
  • binder transactions, monitor contention, scheduler gaps, and app log markers

gfxinfo Framestats

Use this for a quick manual frame snapshot:

SERIAL="<adb-serial>"
PACKAGE="<app package>"

adb -s "$SERIAL" shell pidof "$PACKAGE"
adb -s "$SERIAL" shell dumpsys window | grep -F "$PACKAGE"
adb -s "$SERIAL" shell dumpsys gfxinfo "$PACKAGE" reset
# Perform the focused flow.
adb -s "$SERIAL" shell dumpsys gfxinfo "$PACKAGE" > "$ARTIFACT_DIR/gfxinfo.txt"
adb -s "$SERIAL" shell dumpsys gfxinfo "$PACKAGE" framestats > "$ARTIFACT_DIR/gfxinfo-framestats.txt"

Capture from a stable, responsive screen. If dumpsys gfxinfo fails to dump the process, or the device shows an ANR/dialog/splash screen instead of the flow, discard that capture and use Perfetto for root cause.

Read the headline summary first: total frames, janky frames, frame percentiles, slow UI thread, slow draw commands, and frame deadline misses. On emulators, absolute smoothness numbers are noisy; percentile spikes and slow draw/UI counters are still useful for deciding whether to take a Perfetto trace.

Memory / Leak Artifacts

Use this on an adb target after narrowing the investigation to one flow. Exercise the flow, return to a stable screen, then capture memory artifacts from that state.

For quick Java/native/PSS/object-count snapshots:

SERIAL="<adb-serial>"
PACKAGE="<app package>"

adb -s "$SERIAL" shell am force-stop "$PACKAGE"
adb -s "$SERIAL" shell monkey -p "$PACKAGE" 1
# Exercise the focused flow, then navigate back to a stable idle screen.
adb -s "$SERIAL" shell dumpsys meminfo "$PACKAGE" > "$ARTIFACT_DIR/meminfo-flow.txt"

Read TOTAL PSS, Java heap, native heap, graphics, Views, Activities, binder counts, and object counts. Treat one noisy sample as a lead, not a conclusion.

For retained Kotlin/Java objects, prefer Shark CLI when it is available. It works with Android heap dumps and produces text output the agent can inspect and cite.

HEAP="/data/local/tmp/app-flow.hprof"
HPROF="$ARTIFACT_DIR/app-flow.hprof"

if ! command -v shark-cli >/dev/null; then
  echo "Install Shark CLI, or analyze the HPROF with Android Studio Profiler / MAT." >&2
fi

adb -s "$SERIAL" shell am dumpheap -g "$PACKAGE" "$HEAP"
adb -s "$SERIAL" pull "$HEAP" "$HPROF"
adb -s "$SERIAL" shell rm -f "$HEAP"

if command -v shark-cli >/dev/null; then
  shark-cli --hprof "$HPROF" analyze | tee "$ARTIFACT_DIR/shark-analysis.txt"
fi

Read shark-analysis.txt first when it exists. Report suspected leaking objects, retained sizes, and reference chains. Look for retained feature objects, activities, fragments, view models, Compose state holders, repositories, listeners, callbacks, and caches that should have been released after leaving the flow. If Shark CLI is unavailable, still preserve the HPROF path and inspect it with the best available heap analyzer; do not claim leak roots from meminfo alone.

For native allocation growth, capture a Perfetto trace with heapprofd enabled. Keep the duration in the config; current Android perfetto rejects -t together with --config.

TRACE_DEVICE="/data/misc/perfetto-traces/native-alloc.pftrace"

adb -s "$SERIAL" shell perfetto -o "$TRACE_DEVICE" \
  --txt -c - <<EOF
duration_ms: 60000
buffers { size_kb: 262144 fill_policy: RING_BUFFER }
data_sources {
  config {
    name: "android.heapprofd"
    heapprofd_config {
      sampling_interval_bytes: 65536
      shmem_size_bytes: 8388608
      block_client: true
      process_cmdline: "$PACKAGE"
    }
  }
}
EOF

adb -s "$SERIAL" pull "$TRACE_DEVICE" "$ARTIFACT_DIR/native-alloc.pftrace"

Analyze the trace with trace_processor_shell and save the outputs:

SKILL_DIR="<absolute path to this loaded skill folder>"
"$SKILL_DIR/scripts/heapprofd_reports.sh" \
  "$ARTIFACT_DIR/native-alloc.pftrace" \
  "$ARTIFACT_DIR"

Read heapprofd-summary.txt, heapprofd-top-allocations.txt, heapprofd-top-stack.txt, heapprofd-health.txt, and meminfo together. Report net native allocation size, top allocating frames/mappings, the expanded stack for the largest callsite, and whether trace stats show heapprofd health issues such as client errors, packet loss, or buffer overruns. Prefer Java heap dumps for retained app objects; heapprofd is for native allocation behavior.

Report

Report:

  • exact flow, device/emulator, Android version, build variant, and run count
  • artifact paths for every trace/report used
  • top hotspots or frame/jank evidence with percentages, durations, or counts
  • whether evidence is CPU samples, frame timeline, frame stats, or memory artifacts
  • caveats such as emulator noise, low sample count, cold-start compilation, or missing symbols
  • next smallest trace or code change when current evidence is insufficient

openai의 다른 스킬

user-context
openai
데이터 분석 플러그인의 지속적인 소스 라우팅 기본 설정, 온보딩 로직, 설정 진행 상황 및 의미 계층 레지스트리를 로드하거나 관리합니다.
official
notion-research-documentation
openai
Notion 콘텐츠를 조사하고 인용문과 함께 구조화된 브리핑, 보고서 또는 비교 자료로 종합합니다. 대상 질의를 사용해 Notion 페이지를 검색하고 가져온 후, 인라인 출처 인용과 참고 문헌 섹션을 포함해 주제별로 결과를 정리합니다. 범위와 사용자 목표에 따라 네 가지 출력 형식(빠른 브리핑, 연구 요약, 비교, 종합 보고서) 중에서 선택합니다. 내장 템플릿을 사용해 Notion 페이지를 생성 및 업데이트하고, 새 정보가 도착하면 출처를 직접 연결하고 변경 사항을 추적합니다...
official
rcsb-pdb-skill
openai
핵심 메타데이터, Search API 쿼리 및 FASTA 다운로드를 위한 간결한 RCSB PDB 요청을 제출합니다. 사용자가 간결한 RCSB 요약을 원할 때 사용하며, 원시 JSON 또는…을 저장합니다.
official
pdf
openai
PDF 읽기, 생성 및 검증 기능을 제공하며, 시각적 렌더링과 프로그래매틱 생성을 지원합니다. Poppler(pdftoppm)를 사용하여 PDF 페이지를 PNG로 렌더링하여 레이아웃, 간격, 타이포그래피를 시각적으로 검사할 수 있습니다. reportlab을 사용하여 프로그래매틱 방식으로 PDF를 생성하여 안정적인 포맷을 보장하며, pdfplumber 또는 pypdf를 통해 텍스트와 메타데이터를 추출합니다. 품질 기준을 준수합니다: 잘린 텍스트, 겹치는 요소, 깨진 표, 렌더링 아티팩트가 없어야 하며, ASCII 하이픈만 사용하고 사람이 읽을 수 있는 인용을 사용합니다.
official
test-coverage-improver
openai
Improve test coverage in the OpenAI Agents JS monorepo: run `pnpm test:coverage`, inspect coverage artifacts, identify low-coverage files and branches, propose…
official
playwright
openai
터미널 기반 브라우저 자동화로 요소 스냅샷 및 대화형 UI 워크플로우 지원. playwright-cli 래퍼 스크립트를 통해 작동하며(npx 필요), 헤드리스 및 헤드 모드 모두 지원하여 시각적 디버깅 가능. 핵심 워크플로우: 페이지 열기, 안정적인 요소 참조를 위한 스냅샷 생성, 참조를 사용한 상호작용, 탐색 또는 DOM 변경 후 재스냅샷. 양식 작성, 클릭, 타이핑, 다중 탭 관리, 스크린샷/PDF 캡처, 흐름 디버깅을 위한 트레이스 기록 포함. 요소 참조(예: e3, e15)...
official
ukb-topmed-phewas-skill
openai
단일 변이에 대한 간결한 UKB-TOPMed PheWAS 요약을 가져오며, rsID, GRCh37 또는 GRCh38 입력을 받아 필요한 GRCh38 쿼리로 변환합니다. 다음과 같은 경우에 사용하세요…
official
code-review-context
openai
모델 가시 컨텍스트
official