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.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add alirezarezvani/claude-skills --skill "performance-profiler" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/alirezarezvani/claude-skills /tmp/claude-skills && cp -r /tmp/claude-skills/engineering/skills/performance-profiler ~/.claude/skills/performance-profiler-alirezarezvani-2This skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
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
```bash
# 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
```bash
# 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](references/profiling-recipes.md) — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots
- [references/optimization-playbook.md](references/optimization-playbook.md) — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code