Systematic performance profiling for Node.js, Python

Performance profiler

by alirezarezvani · MIT license · ★ 26,349 on GitHub · updated Aug 30

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.

---
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

Overview

Tier: POWERFUL Category: Engineering Domain: Performance Engineering

Manual install (npx, ZIP)

Claude Codenpx skills add alirezarezvani/claude-skills --skill performance-profiler -g -a claude-code

Claude Code (manual)

mkdir -p ~/.claude/skills && curl -sL https://codeload.github.com/alirezarezvani/claude-skills/tar.gz/19392f7a0826 \
  | tar -xz -C ~/.claude/skills --strip-components=3 claude-skills-19392f7a0826/engineering/skills/performance-profiler

Windows (PowerShell)

New-Item -ItemType Directory -Force "$HOME\.claude\skills" | Out-Null; Invoke-WebRequest "https://m.agentalley.io/zip/b59d514335517ef90864/performance-profiler.zip" -OutFile "$env:TEMP\performance-profiler.zip"; Expand-Archive "$env:TEMP\performance-profiler.zip" -DestinationPath "$HOME\.claude\skills" -Force

Claude.ai / desktopDownload the ZIP and upload it under Settings → Capabilities → Skills.

Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

Files included

SKILL.md2.7 KB
references/2 files · 14.4 KB
scripts/1 file · 6.0 KB

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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

1	---
2	name: "performance-profiler"
3	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."
4	---
5
6	# Performance Profiler
7
8	Tier: POWERFUL
9	Category: Engineering
10	Domain: Performance Engineering
11
12	---
13
14	## Overview
15
16	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.
17
18	## Core Capabilities
19
20	- CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go
21	- Memory profiling — heap snapshots, leak detection, GC pressure
22	- Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer
23	- Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection
24	- Load testing — k6 scripts, Artillery scenarios, ramp-up patterns
25	- Before/after measurement — establish baseline, profile, optimize, verify
26
27	---
28
29	## When to Use
30
31	- App is slow and you don't know where the bottleneck is
32	- P99 latency exceeds SLA before a release
33	- Memory usage grows over time (suspected leak)
34	- Bundle size increased after adding dependencies
35	- Preparing for a traffic spike (load test before launch)
36	- Database queries taking >100ms
37
38	---
39
40	## Quick Start
41
42	```bash
43	# Analyze a project for performance risk indicators
44	python3 scripts/performance_profiler.py /path/to/project
45
46	# JSON output for CI integration
47	python3 scripts/performance_profiler.py /path/to/project --json
48
49	# Custom large-file threshold
50	python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256
51	```
52
53	---
54
55	## Golden Rule: Measure First
56
57	```bash
58	# Establish baseline BEFORE any optimization
59	# Record: P50, P95, P99 latency \| RPS \| error rate \| memory usage
60
61	# Wrong: "I think the N+1 query is slow, let me fix it"
62	# Right: Profile → confirm bottleneck → fix → measure again → verify improvement
63	```
64
65	---
66
67	## Node.js Profiling
68	→ See references/profiling-recipes.md for details
69
70	## References
71
72	- [references/profiling-recipes.md](references/profiling-recipes.md) — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots
73	- [references/optimization-playbook.md](references/optimization-playbook.md) — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
74
75