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storefront-health

Realize uma auditoria de desempenho de loja virtual com Lighthouse e análise de Core Web Vitals.

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Atualizado há 8 meses

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Ler descrição completa

You are helping the team run a performance audit on the Jocko Fuel storefront.

Follow these steps:

Step 1: Identify Page to Audit

Ask the user which page(s) to audit. Common targets:

  • Homepage (jockofuel.com)
  • Product listing pages (collections)
  • Product detail pages (specific products)
  • Cart and checkout flow
  • Custom landing pages

If the user says "everything," start with homepage + top 3 traffic pages.

Step 2: Run Lighthouse Analysis

Delegate to the performance-auditor agent to run a Lighthouse audit on each page. Collect scores for:

  • Performance (target: 90+)
  • Accessibility (target: 90+)
  • Best Practices (target: 90+)
  • SEO (target: 90+)

For each category below target, capture the specific failing audits.

Step 3: Check Core Web Vitals

Delegate to the performance-auditor agent to measure:

  • LCP (Largest Contentful Paint) — target: < 2.5s
  • INP (Interaction to Next Paint) — target: < 200ms
  • CLS (Cumulative Layout Shift) — target: < 0.1
  • TTFB (Time to First Byte) — target: < 800ms

Flag any metrics in "needs improvement" or "poor" ranges.

Step 4: Analyze Theme Impact

Delegate to the theme-analyzer agent to check:

  • Liquid template render complexity
  • Unused CSS/JS being loaded
  • Image optimization status (format, sizing, lazy loading)
  • Font loading strategy

Step 5: Generate Report

Compile results into a report with:

  • Executive summary: Overall health score and top 3 issues
  • Scores table: All Lighthouse scores by page
  • Core Web Vitals table: All CWV metrics by page
  • Prioritized recommendations: Ordered by impact and effort
  • Quick wins: Changes that can be made immediately

Error Handling

  • If a page is behind authentication, ask the user for access or skip that page
  • If Lighthouse fails to run, suggest using Chrome DevTools manually and provide instructions
  • If metrics vary significantly between runs, note the variance and recommend multiple samples
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