performance-profiling

davila7/claude-code-templates · updated Apr 8, 2026

$npx skills add https://github.com/davila7/claude-code-templates --skill performance-profiling
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summary

Measure, analyze, optimize - in that order.

skill.md

Performance Profiling

Measure, analyze, optimize - in that order.

🔧 Runtime Scripts

Execute these for automated profiling:

Script Purpose Usage
scripts/lighthouse_audit.py Lighthouse performance audit python scripts/lighthouse_audit.py https://example.com

1. Core Web Vitals

Targets

Metric Good Poor Measures
LCP < 2.5s > 4.0s Loading
INP < 200ms > 500ms Interactivity
CLS < 0.1 > 0.25 Stability

When to Measure

Stage Tool
Development Local Lighthouse
CI/CD Lighthouse CI
Production RUM (Real User Monitoring)

2. Profiling Workflow

The 4-Step Process

1. BASELINE → Measure current state
2. IDENTIFY → Find the bottleneck
3. FIX → Make targeted change
4. VALIDATE → Confirm improvement

Profiling Tool Selection

Problem Tool
Page load Lighthouse
Bundle size Bundle analyzer
Runtime DevTools Performance
Memory DevTools Memory
Network DevTools Network

3. Bundle Analysis

What to Look For

Issue Indicator
Large dependencies Top of bundle
Duplicate code Multiple chunks
Unused code Low coverage
Missing splits Single large chunk

Optimization Actions

Finding Action
Big library Import specific modules
Duplicate deps Dedupe, update versions
Route in main Code split
Unused exports Tree shake

4. Runtime Profiling

Performance Tab Analysis

Pattern Meaning
Long tasks (>50ms) UI blocking
Many small tasks Possible batching opportunity
Layout/paint Rendering bottleneck
Script JavaScript execution

Memory Tab Analysis

Pattern Meaning
Growing heap Possible leak
Large retained Check references
Detached DOM Not cleaned up

5. Common Bottlenecks

By Symptom

Symptom Likely Cause
Slow initial load Large JS, render blocking
Slow interactions Heavy event handlers
Jank during scroll Layout thrashing
Growing memory Leaks, retained refs

6. Quick Win Priorities

Priority Action Impact
1 Enable compression High
2 Lazy load images High
3 Code split routes High
4 Cache static assets Medium
5 Optimize images Medium

7. Anti-Patterns

❌ Don't ✅ Do
Guess at problems Profile first
Micro-optimize Fix biggest issue
Optimize early Optimize when needed
Ignore real users Use RUM data

Remember: The fastest code is code that doesn't run. Remove before optimizing.

Discussion

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general reviews

Ratings

4.548 reviews
  • Chinedu Liu· Dec 28, 2024

    I recommend performance-profiling for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Lucas Yang· Dec 8, 2024

    Solid pick for teams standardizing on skills: performance-profiling is focused, and the summary matches what you get after install.

  • Chinedu Smith· Dec 4, 2024

    performance-profiling fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Rahul Santra· Nov 23, 2024

    Solid pick for teams standardizing on skills: performance-profiling is focused, and the summary matches what you get after install.

  • Chinedu Ghosh· Nov 23, 2024

    We added performance-profiling from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Emma Menon· Nov 19, 2024

    Useful defaults in performance-profiling — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aarav Patel· Nov 19, 2024

    performance-profiling is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Oct 14, 2024

    performance-profiling is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Lucas Zhang· Oct 14, 2024

    performance-profiling reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aarav Rao· Oct 10, 2024

    Registry listing for performance-profiling matched our evaluation — installs cleanly and behaves as described in the markdown.

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