performance-profiler
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.
DeepseekModel
キュレーション済みスキル
品質 優秀 · 90
v1.0.0
取得
https://deepseekmodel.com/api/download.php?id=alirezarezvani-claude-skills-engineering-skills-performance-profiler-skill-md&format=skill
ダウンロード .skill
標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能
.skill ファイルの system_prompt フィールドの実際の内容。
name performance-profiler description Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production. Performance Profiler Tier: POWERFUL Category: Engineering Domain: Performance Engineering Overview Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after. Core Capabilities CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go Memory profiling — heap snapshots, leak detection, GC pressure Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection Load testing — k6 scripts, Artillery scenarios, ramp-up patterns Before/after measurement — establish baseline, profile, optimize, verify When to Use App is slow and you don't know where the bottleneck is P99 latency exceeds SLA before a release Memory usage grows over time (suspected leak) Bundle size increased after adding dependencies Preparing for a traffic spike (load test before launch) Database queries taking >100ms Quick Start # Analyze a project for performance risk indicators python3 scripts/performance_profiler.py /path/to/project # JSON output for CI integration python3 scripts/performance_profiler.py /path/to/project --json # Custom large-file threshold python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256 Golden Rule: Measure First # Establish baseline BEFORE any optimization # Record: P50, P95, P99 latency | RPS | error rate | memory usage # Wrong: "I think the N+1 query is slow, let me fix it" # Right: Profile → confirm bottleneck → fix → measure again → verify improvement Node.js Profiling → See references/profiling-recipes.md for details References references/profiling-recipes.md — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots references/optimization-playbook.md — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
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ダウンロードした .skill に含まれるフィールド。
| フィールド | 説明 |
|---|---|
| format | フォーマット識別子（skill/v1） |
| skill_id | スキル固有 ID |
| name | スキル名 |
| version | バージョン |
| description | 説明 |
| category | カテゴリ（配列） |
| trigger_words | トリガーワード |
| tags | タグ |
| source | ソース |
| source_url | ソース URL（本ページ） |
| exported_at | エクスポート日時（ダウンロード毎） |
| system_prompt | システムプロンプト本文 |
| model_config | モデル設定：provider / model / temperature / max_tokens / top_p |
| examples | サンプル |
| install_guide | 各プラットフォームの導入説明（Coze / Dify / Claude / カスタム） |
