How does this skill help with Interaction to Next Paint (INP)?

It provides patterns to break up long main-thread tasks using techniques like scheduler.yield and React’s useTransition to ensure the browser remains responsive to user input.

Does it support the upcoming 2026 performance thresholds?

Yes, the skill includes specific guidance for the stricter 2026 thresholds (e.g., LCP ≤ 2.0s) to help developers prepare for future search ranking requirements.

Does it integrate with monitoring tools?

Yes, it provides implementation patterns for the web-vitals library to send real user metrics (RUM) to your analytics backends for field-data tracking.

Can it help prevent Cumulative Layout Shift (CLS)?

Absolutely, it implements strategies like aspect-ratio CSS, explicit image dimensions, and font-display settings to eliminate unexpected content jumping during page load.

Core Web Vitals Optimizer

Name: Core Web Vitals Optimizer
Author: yonatangross

byyonatangross

•

分析と監視

Optimizes web application performance by implementing best practices for LCP, INP, and CLS according to current and future Google thresholds.

The Core Web Vitals skill provides specialized guidance and implementation patterns to improve Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS). It equips developers with strategies for preloading critical resources, breaking up long main-thread tasks, and implementing Real User Monitoring (RUM) to track real-world performance. By targeting upcoming 2026 stricter thresholds, this skill helps future-proof search rankings and user experience through production-ready code for SSR, image optimization, and performance budgeting.

主な機能

01Automated performance budget configuration for CI/CD

02INP reduction using main-thread yielding and React transitions

03LCP optimization via resource preloading and priority hints

0469 GitHub stars

05RUM integration patterns for field data collection

06CLS prevention through aspect-ratio sizing and layout-stable animations

ユースケース

01Diagnosing and fixing performance regressions identified in Lighthouse audits

02Future-proofing frontend applications for 2026 Core Web Vitals thresholds

03Implementing Real User Monitoring (RUM) to capture field performance data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit core-web-vitals

For use in Claude.ai and ChatGPT

主な機能

01Automated performance budget configuration for CI/CD

02INP reduction using main-thread yielding and React transitions

03LCP optimization via resource preloading and priority hints

0469 GitHub stars

05RUM integration patterns for field data collection

06CLS prevention through aspect-ratio sizing and layout-stable animations

ユースケース

01Diagnosing and fixing performance regressions identified in Lighthouse audits

02Future-proofing frontend applications for 2026 Core Web Vitals thresholds

03Implementing Real User Monitoring (RUM) to capture field performance data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit core-web-vitals

For use in Claude.ai and ChatGPT