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performance-profiler

Profile CPU, memory, bundle, and database performance in Node.js, Python, and Go services.

2|Updated Apr 8, 2026
Search Tags:#bundle analysis#load testing#performance profiling#database tuning#latency optimization#heap snapshot#p99 latency
One-click install
npx skills add https://github.com/miptah21/skills --skill performance-profiler-miptah21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-profiler
Source: https://github.com/miptah21/skills/tree/main/.agents/skills/performance-profiler
Command: npx skills add https://github.com/miptah21/skills --skill performance-profiler-miptah21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you identify the real bottlenecks causing slow application performance, excessive P99 latency, suspected memory leaks, bundle-size regressions, and slow database queries—then verify improvements with before/after measurements.

Core Features & Use Cases

  • Measure-first profiling workflows: Establish baselines before changing anything to avoid optimizing the wrong issue.
  • CPU, memory, and bundle analysis recipes: Generate Node.js profiling artifacts (CPU profiles, heap snapshots, GC tracing) and analyze bundle size impacts for web apps.
  • Database and load-testing guidance: Use SQL EXPLAIN/EXPLAIN ANALYZE and production-style query stats to diagnose query slowness, then validate results with k6 load testing.

Example use case: you deploy new dependencies, and your API’s P99 latency jumps—use profiling to confirm whether the issue is query performance, GC/memory growth, CPU hotspots, or increased bundle weight.

Quick Start

Run the performance-profiler skill to profile your application, collect baseline P50/P95/P99 and relevant metrics, apply targeted fixes (CPU, memory, bundle, DB, or API patterns), and re-measure to confirm improvement.

Frequently Asked Questions about performance-profiler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce high P99 latency in my Node.js API?▼

To reduce P99 latency in Node.js APIs, profile CPU hotspots and memory growth, then validate latency improvements with load testing. Establish P50/P95/P99 baseline metrics before optimization to ensure targeted fixes actually resolve the bottleneck.

What is the best way to diagnose a suspected memory leak in Python services?▼

Diagnosing suspected memory leaks in Python services requires capturing heap snapshots and tracing garbage collection behavior to identify growth patterns. Always measure baseline memory usage before applying fixes to confirm the leak is resolved.

How do I troubleshoot slow database queries causing API latency spikes?▼

Troubleshoot slow database queries by running SQL EXPLAIN or EXPLAIN ANALYZE to inspect execution plans and query behavior. Compare production-style query stats and latency before and after applying database tuning changes to verify improvements.

Can I use this performance profiling workflow for Go services under traffic spikes?▼

Yes, the performance profiling workflow supports Go services under traffic spike scenarios. It measures baseline metrics, profiles CPU and memory behavior, and validates stability using load testing to ensure traffic readiness.

Why do I need to measure baseline metrics before optimizing bundle size?▼

Measuring baseline bundle size metrics before optimization prevents you from fixing the wrong issue. Analyzing bundle regressions requires before and after comparisons to confirm that your changes actually reduced web app bundle weight.

How do I validate performance fixes after CPU and memory profiling?▼

Validate performance fixes after CPU and memory profiling by re-measuring metrics and running k6 load testing. Comparing before and after results confirms that optimizations resolved the instability and reduced latency.

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