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Core web vitals

Skill jgamaraalv/delivery-loop/.claude/skills/core-web-vitals

Continuous fullstack delivery loops — orchestrates frontend, backend, and quality subagents (behaviour drivers, engineers, UI/UX specialist, code/security reviewers, architects) in a test → diagnose → fix → review → secure → re-test cycle until the work is production-ready

Install
npx -y skills add jgamaraalv/delivery-loop --skill core-web-vitals

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Core Web Vitals reference for measuring, diagnosing, and improving LCP, INP, and CLS page performance against Google's thresholds

SKILL.md

5.4 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Core Web Vitals

The three stable Core Web Vitals, each measured at the 75th percentile of real page loads (segmented by mobile and desktop):

MetricMeasuresGoodNeeds ImprovementPoor
LCP — Largest Contentful PaintLoading≤ 2.5 s2.5–4.0 s> 4.0 s
INP — Interaction to Next PaintInteractivity≤ 200 ms200–500 ms> 500 ms
CLS — Cumulative Layout ShiftVisual stability≤ 0.10.1–0.25> 0.25

A page passes Core Web Vitals only if all three metrics meet "Good" at the 75th percentile.

Supporting metrics (non-Core but diagnostic)

  • FCP (First Contentful Paint) — diagnoses render-blocking resources upstream of LCP
  • TTFB (Time to First Byte) — server response time; directly affects LCP
  • TBT (Total Blocking Time) — lab proxy for INP; identifies long tasks

Tools matrix

ToolTypeLCPINPCLSNotes
Chrome User Experience Report (CrUX)Field28-day rolling window of real users
PageSpeed InsightsField + LabField = CrUX data; Lab = Lighthouse
Search Console CWV reportFieldGroups URLs by template
Chrome DevTools Performance panelField + LabLocal profiling, interaction tracing
LighthouseLabTBT*CI integration; INP → use TBT as proxy

*Lighthouse uses Total Blocking Time (TBT) as a lab proxy for INP. TBT correlates with INP but does not replace field measurement.

Metric lifecycle

Metrics progress through: Experimental → Pending → Stable. All three current Core Web Vitals (LCP, CLS, INP) are Stable. INP replaced FID (First Input Delay) in March 2024. Changes to stable metrics follow an annual cadence with advance notice.

Quick setup: measure all three in the field

npm install web-vitals
import { onCLS, onINP, onLCP } from "web-vitals";

function sendToAnalytics(metric) {
  const body = JSON.stringify(metric);
  (navigator.sendBeacon && navigator.sendBeacon("/analytics", body)) ||
    fetch("/analytics", { body, method: "POST", keepalive: true });
}

onCLS(sendToAnalytics);
onINP(sendToAnalytics);
onLCP(sendToAnalytics);

Each callback receives { name, value, rating, delta, id, navigationType }. rating is "good", "needs-improvement", or "poor".

The web-vitals library handles bfcache restores, prerendered pages, iframe aggregation, and other edge cases that raw PerformanceObserver does not.

Generate a visual report

When the user provides metric values or a Lighthouse JSON file, generate an interactive HTML report and open it in the browser:

To locate the script, find scripts/visualize.py relative to this skill's directory. The path depends on how ts-dev-kit is installed:

  • Project scope: skills/core-web-vitals/scripts/visualize.py or .claude/skills/core-web-vitals/scripts/visualize.py
  • Personal scope: ~/.claude/skills/core-web-vitals/scripts/visualize.py
  • Plugin scope: resolve via node_modules/@jgamaraalv/ts-dev-kit/skills/core-web-vitals/scripts/visualize.py

Use find or ls to discover the actual path, then run:

# From manual values (replace SCRIPT_PATH with the discovered path)
python3 SCRIPT_PATH/visualize.py \
  --lcp 2.1 --inp 180 --cls 0.05 \
  --url https://example.com

# From a Lighthouse JSON output
python3 SCRIPT_PATH/visualize.py \
  --lighthouse lighthouse-report.json

# Custom output path, no auto-open
python3 SCRIPT_PATH/visualize.py \
  --lcp 3.8 --inp 420 --cls 0.12 \
  --output cwv-report.html --no-open

The script (scripts/visualize.py) requires only Python 3 stdlib — no packages to install. It outputs a self-contained HTML file with color-coded metric cards, a visual progress bar showing where each value falls on the Good/Needs Improvement/Poor scale, and an overall PASS/FAIL/NEEDS IMPROVEMENT verdict.

When to read reference files

ReferenceRead when…
references/lcp.mdLCP > 2.5 s, diagnosing slow image/text load, preload/CDN questions
references/inp.mdINP > 200 ms, slow click/key/tap response, long task investigations
references/cls.mdCLS > 0.1, elements jumping on scroll or load, font/image shift
references/tools.mdSetting up monitoring, using DevTools/Lighthouse/PSI, top-9 optimization checklist

What ships with it: 5 files

26.3 KB alongside SKILL.md, 1 of them executable

references/

scripts/

Gives 0 of the 12 instructions most seo skills give in ~1.3k tokens

Counted across 454 of the 460 authors here whose files we hold, read 2026-08-07

  • Implement structured data using JSON-LDin 29 of 454, across 25 files
  • Write unique meta descriptions under 160 charactersin 25 of 454, across 20 files
  • Verify one H1 exists per pagein 24 of 454, across 15 files
  • Maintain a single H1 per pagein 24 of 454, across 15 files
  • Use JSON-LD format for all schema markupin 23 of 454, across 16 files
  • Use descriptive anchor text for internal linksin 22 of 454, across 18 files
  • Add descriptive alt text to imagesin 19 of 454, across 15 files
  • Read product marketing context before auditingin 19 of 454, across 11 files
  • Write unique title tags under 60 charactersin 19 of 454, across 14 files
  • Add unique title and meta description per pagein 19 of 454, across 17 files
  • Fetch robots.txt and sitemap.xmlin 19 of 454, across 18 files
  • Place the primary keyword in the first 100 wordsin 18 of 454, across 17 files

Said here and by no other author read

  • require all three metrics to pass
  • install the web-vitals package
  • send metric data to an analytics endpoint
  • generate an interactive HTML report
  • use python3 to run the visualization script
  • read lcp reference when LCP exceeds 2.5 seconds

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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