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    Web Performance Vitals Auditor

    by RuH

    1

    A diagnostic engine for Core Web Vitals that identifies root causes for slow LCP, INP, and CLS scores.

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    See it in action

    You say

    My LCP is 4.5s on mobile according to PageSpeed Insights. Here is my hero component code and the Lighthouse JSON. What is the bottleneck?

    Your agent does

    Diagnosis: Your LCP image is being lazy-loaded and lacks a fetchpriority. It is also a CSS background-image, delaying discovery until the stylesheet loads. Fixes: 1. Change to with fetchpriority="high" and loading="eager" (Impact: High, Effort: S). 2. Add a preload link in the head.

    What you get

    Identify the specific resource or script delaying Largest Contentful Paint.Reduce Cumulative Layout Shift by pinning down unreserved DOM element growth.Fix Interaction to Next Paint issues by chunking long JS tasks.Generate a prioritized performance roadmap based on implementation effort.

    About this skill

    The problem

    Vague performance complaints like "the site feels slow" rarely lead to actionable fixes. Developers often waste hours on generic optimizations that fail to move the needle on Core Web Vitals or search rankings.

    What it does

    • Identifies the specific bottlenecks behind LCP, INP, and CLS scores using a systematic diagnostic process.
    • Audits resource discovery paths to find delayed hero images, render-blocking scripts, and unoptimized font loading.
    • Pinpoints main-thread execution issues causing interaction lag and poor responsiveness.
    • Detects layout shift culprits like missing dimensions, unreserved ad space, and font metric mismatches.
    • Provides a prioritized action plan ranked by estimated impact versus implementation effort.

    Frameworks & tools

    React, Next.js, Vue, Nuxt, WordPress, and Vanilla JS. Expert knowledge of Lighthouse reports, PageSpeed Insights JSON, and Chrome DevTools Performance profiles.

    Why this beats prompting it yourself

    Generic LLMs often suggest "lazy-loading all images," which can actually destroy your LCP score if applied to hero elements. This skill follows a strict "metric-first" logic, ensuring every recommendation is backed by a technical mechanism and a predicted outcome.

    Use cases

    • Pre-shipment PR reviews to catch performance regressions in new components.
    • Debugging "Poor" Core Web Vitals ratings that are hurting SEO rankings.
    • Optimizing bundle sizes and hydration overhead in heavy SSR/SSG frameworks.
    • Converting messy Lighthouse JSON exports into an executive summary for stakeholders.

    Known limitations

    Requires an external data source (URL, Lighthouse report, or code) as it cannot crawl live sites independently. Performance estimates are directional and not exact measurements.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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