Performance Profiler
SkillDocs & knowledgeWhen an application is slow, this helps your AI find out why. It profiles Node.js, Python, and Go apps to pinpoint CPU, memory, I/O, and database bottlenecks, then measures again after changes to confirm the fix actually worked. It can also generate flamegraphs, analyze bundle sizes, and run load tests.
Available today. Use it from your connected AI after setup.
No other account needed.
Add it, then ask your AI to investigate a slow application or endpoint and profile what is happening. Any suggested fix comes with before and after measurements so you can see whether it helped.
Then ask your AI: use the Performance Profiler skill
What your AI can do with it
- Profile Node.js, Python, and Go applications to locate slowdowns
- Identify CPU, memory, and I/O bottlenecks
- Optimize database queries
- Generate flamegraphs showing where time is spent
- Analyze bundle sizes
- Run load tests with k6 and Artillery
What this skill tells your AI
The instructions your AI receives, as published by borghei/claude-skills in engineering/performance-profiler/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 740
- Forks
- 135
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
performance-profiler- Source
- github.com/borghei/claude-skills