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android-performance-observabilityAndroid 性能可观测性

Agent Skill

android-performance-observability 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

235

周安装

10

GitHub Stars

5

下载量

82
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:android-performance-observability(Android 性能可观测性)
来源仓库:https://github.com/krutikjain/android-agent-skills
仓库路径:skills/android-performance-observability
安装命令:
npx skills add https://github.com/krutikjain/android-agent-skills --skill android-performance-observability
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/krutikjain/android-agent-skills --skill android-performance-observability

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合围绕仓库状态进行整理。

  • 适用于 Android 性能分析和基准测试,包括启动时间、渲染性能和内存监控。
  • 提供可操作的跟踪、日志记录和崩溃信号测量,支持生产环境性能监控。
  • 安装命令:npx skills add https://github.com/krutikjain/android-agent-skills --skill android-performance-observability
  • 注意权限范围和维护状态,避免触发不必要的联网或文件操作。

SKILL.md

Android Performance Observability

When To Use

  • Use this skill when the request is about: android performance profiling, baseline profile or macrobenchmark, app startup issue android.
  • Primary outcome: Measure startup, rendering, memory, jank, vitals, logs, and crash signals for Android apps with actionable traces.
  • Read references/patterns.md when you need the measurement ladder for startup, jank, traces, and production signals.
  • Read references/scenarios.md for repeatable profiling and trace-oriented entry points.
  • Handoff skills when the scope expands:
  • android-compose-performance
  • android-ci-cd-release-playstore

Workflow

  1. Classify the symptom before choosing tools: cold start, warm start, frame/jank, scrolling, memory, ANR, crash, battery, or production vitals drift.
  2. Measure on release-like builds and physical devices whenever possible; avoid debugging from debug-only traces or profile-unfriendly builds.
  3. Pick the smallest tool that answers the question: Macrobenchmark for startup/scroll numbers, Baseline Profiles for ahead-of-time optimization, Perfetto/System Tracing for deep traces, JankStats or FrameMetrics for frame quality, and Play Vitals for field evidence.
  4. Change one thing at a time, then compare before and after traces or benchmark outputs instead of stacking multiple optimizations blindly.
  5. Hand off UI-specific rendering changes or release rollouts only after the measurement surface is stable and the bottleneck is evidenced.

Guardrails

  • Treat benchmarks, traces, and vitals as different evidence sources with different noise profiles; do not mix them casually.
  • Prefer reproducible release-build measurements over debug-build intuition.
  • Tie optimizations back to user-facing metrics such as startup time, frame pacing, ANRs, or battery impact.
  • Keep the profiling setup stable enough that regressions are attributable to code changes instead of device or environment churn.

Anti-Patterns

  • Chasing micro-optimizations before identifying whether the problem is startup, rendering, I/O, or field reliability.
  • Reading one noisy trace and presenting the result as settled fact.
  • Measuring debug builds and assuming the same behavior in production.
  • Adding Baseline Profiles or macrobenchmarks without checking whether the target path is stable enough to compare.

Review Focus

  • Startup: cold and warm launch, expensive initialization, and Baseline Profile coverage.
  • Rendering: jank, skipped frames, Compose or View invalidation churn, and long main-thread work.
  • Memory and reliability: allocations, leaks, ANRs, crashes, and Play Vitals trends.
  • Evidence quality: repeatable commands, release-like variants, and documented before/after comparisons.

Examples

Happy path

  • Scenario: Audit the repo for profiling surfaces and benchmark-related hooks before proposing a measurement plan.
  • Command: rg -n "baseline|macrobenchmark|profileable|JankStats|Trace|Perfetto".

Edge case

  • Scenario: Keep startup and rendering investigations grounded in repeatable release-like builds.
  • Command: cd examples/orbittasks-compose &&./gradlew:app:assembleDebug

Failure recovery

  • Scenario: Keep observability requests distinct from Compose-only tuning or release automation.
  • Command: python3 scripts/eval_triggers.py --skill android-performance-observability

Done Checklist

  • The bottleneck is classified and tied to an evidence source that can be re-run.
  • The chosen tools match the symptom instead of duplicating noisy measurements.
  • Before/after comparisons are explicit for any recommended optimization.
  • UI-only or release-only work is separated from measurement and tracing.

Official References

适合场景

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能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.29%
按下载量换算30

Claude

29.13%
按下载量换算24

Cursor

18.61%
按下载量换算15

Gemini CLI

10.31%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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