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macos-perf

Activity Monitor, Instruments, top/htop, memory pressure, thermal state, powermetrics, profiling

⚡ おすすめ: コマンド1行でインストール(60秒)

下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。

🍎 Mac / 🐧 Linux
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o macos-perf.zip https://jpskill.com/download/22247.zip && unzip -o macos-perf.zip && rm macos-perf.zip
🪟 Windows (PowerShell)
$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/22247.zip -OutFile "$d\macos-perf.zip"; Expand-Archive "$d\macos-perf.zip" -DestinationPath $d -Force; ri "$d\macos-perf.zip"

完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。

💾 手動でダウンロードしたい(コマンドが難しい人向け)
  1. 1. 下の青いボタンを押して macos-perf.zip をダウンロード
  2. 2. ZIPファイルをダブルクリックで解凍 → macos-perf フォルダができる
  3. 3. そのフォルダを C:\Users\あなたの名前\.claude\skills\(Win)または ~/.claude/skills/(Mac)へ移動
  4. 4. Claude Code を再起動

⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。

🎯 このSkillでできること

下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。

📦 インストール方法 (3ステップ)

  1. 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
  2. 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
  3. 3. 展開してできたフォルダを、ホームフォルダの .claude/skills/ に置く
    • · macOS / Linux: ~/.claude/skills/
    • · Windows: %USERPROFILE%\.claude\skills\

Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。

詳しい使い方ガイドを見る →
最終更新
2026-05-18
取得日時
2026-05-18
同梱ファイル
1
📖 Claude が読む原文 SKILL.md(中身を展開)

この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。

macos-perf

Purpose

This skill enables monitoring and profiling macOS system performance using native tools, focusing on CPU, memory, thermal, and power metrics to identify bottlenecks and optimize applications.

When to Use

Use this skill for diagnosing high CPU usage in apps, investigating memory leaks, profiling resource-intensive processes, checking thermal states during heavy workloads, or analyzing power consumption on macOS devices.

Key Capabilities

  • Monitor real-time system metrics via Activity Monitor or top/htop for CPU, memory, and network usage.
  • Profile applications with Instruments, capturing detailed traces for CPU, memory, and energy.
  • Check memory pressure using vm_stat to detect low-memory conditions.
  • Query thermal state via powermetrics for CPU/GPU temperatures and fan speeds.
  • Analyze power metrics with powermetrics to measure battery drain and efficiency.
  • Use command-line tools like top -o cpu for sorted process lists or instruments -s devices for available templates.

Usage Patterns

Invoke this skill in scripts for automated monitoring or integrate into AI workflows for real-time analysis. For example, run periodic checks in a loop for server-like macOS setups, or trigger profiling when an app exceeds resource thresholds. Always specify exact tools and flags based on the task; e.g., use top for quick views and Instruments for deep dives. If integrating with automation, export data to JSON for parsing.

Common Commands/API

  • Use top -o cpu -s 1 to display processes sorted by CPU usage, updating every second; pipe output to grep for filtering, e.g., top -o cpu | grep "MyApp".
  • Check memory pressure with vm_stat | grep "Pages" to get free pages; sample code:
    output = subprocess.run(['vm_stat'], capture_output=True).stdout
    free_pages = int(re.search(r'free:\s+(\d+)', output.decode()).group(1))
  • Run powermetrics --samplers cpu,thermal -i 1000 for CPU and thermal data every 1 second.
  • Launch Instruments via CLI: instruments -w <device> -t Time Profiler -D output.trace /path/to/app; use exported .trace files for analysis.
  • For thermal state, use ioreg -l | grep "Ambient" to parse ambient temperature from system registry.
  • No API keys required for these tools; they run natively on macOS.

Integration Notes

Integrate by wrapping commands in Python or shell scripts; set environment variables for custom paths, e.g., export INSTRUMENTS_PATH=/Applications/Xcode.app/Contents/Developer/usr/bin/instruments. For data export, use plutil to convert .plist outputs to JSON. If combining with other skills, ensure macOS version compatibility (e.g., Instruments requires Xcode); check via sw_vers -productVersion. Avoid running multiple intensive tools simultaneously to prevent resource contention.

Error Handling

Check command exit codes; for example, if top fails, verify with echo $? and handle by logging errors or retrying. For Instruments, parse stderr for messages like "Template not found" and fallback to alternatives. Use try-except in scripts, e.g.:

  try:
      result = subprocess.run(['instruments', '-t', 'Time Profiler'], check=True)
  except subprocess.CalledProcessError as e:
      print(f"Error: {e.returncode} - {e.output}")

Common issues include permission errors (run with sudo) or missing dependencies (install Xcode command line tools via xcode-select --install).

Concrete Usage Examples

  1. To monitor CPU usage of a specific process: Run top -pid <processID> -o cpu in a loop script, then analyze output to alert if usage > 80%; example script line: while true; do top -pid 1234 -l 1 | grep CPU; sleep 5; done.
  2. For profiling an app's memory: Use instruments -t Allocations -D profile.trace /Applications/MyApp.app, then open the trace in Instruments GUI to identify leaks; integrate by scripting: instruments ... && open profile.trace.

Graph Relationships

  • Related to: macos cluster (e.g., macos-core for basic system ops), performance tag (links to general profiling skills), profiling tag (connects to app optimization tools).
  • Dependencies: Requires macos cluster skills for foundational access; no direct API links.