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performance-profiler

Investigate slow Node.js, Python, and Go applications by measuring CPU use, memory, database queries, and data transfer. Identify bottlenecks, inspect frontend bundle sizes, run load tests, and compare measurements before and after changes to verify improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

alirezarezvani/claude-skills
설치 방법 보기

이럴 때 유용합니다

  • Investigating slow API responses
  • Tracking memory growth and suspected leaks
  • Analyzing bundle growth after adding dependencies
  • Testing capacity before a traffic spike

포함된 파일(4)

  • SKILL.md2.7 KB
  • references/optimization-playbook.md3.2 KB
  • references/profiling-recipes.md11.3 KB
  • scripts/performance_profiler.py6.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

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