SEO
by addyosmani / web-quality-skills
Optimize for search engine visibility and ranking.
Optimize Core Web Vitals (LCP, INP, CLS) for better search rankings and user experience.
Listed ·Updated
Install data from skills.sh
Algo SEO Technical is a Technical SEO skill for AI agents, published in the seoskills.sh catalog. Reach for it when your work involves crawlability, indexing, site architecture, Core Web Vitals, and log-file analysis. Install it with one command and it runs inside your own agent, so the work happens in your workflow, not a separate SEO tool.
Core Web Vitals are Google's page experience metrics: LCP (loading), INP (interactivity), and CLS (visual stability). Measured on real user data (CrUX). Pass thresholds: LCP < 2.5s, INP < 200ms, CLS < 0.1.
Trigger conditions:
When NOT to use:
IRON LAW: CrUX Field Data Is the Source of Truth
Lab scores (Lighthouse) that pass can still FAIL in the field.
Google ranks based on REAL USER data (75th percentile):
- LCP < 2.5s (Good), 2.5-4.0s (Needs Improvement), > 4.0s (Poor)
- INP < 200ms (Good), 200-500ms (Needs Improvement), > 500ms (Poor)
- CLS < 0.1 (Good), 0.1-0.25 (Needs Improvement), > 0.25 (Poor)
Collect field data from CrUX API or Search Console. Run Lighthouse for lab baseline. Identify which metrics fail. Gate: Have both field and lab data; failing metrics identified.
LCP fixes: 1. Optimize largest element (hero image/text). 2. Preload critical resources. 3. Reduce server response time (TTFB). 4. Eliminate render-blocking resources.
INP fixes: 1. Break long tasks (> 50ms) into smaller chunks. 2. Reduce JavaScript execution time. 3. Use requestIdleCallback for non-critical work. 4. Minimize main thread blocking.
CLS fixes: 1. Set explicit dimensions on images/videos. 2. Reserve space for ads/embeds. 3. Avoid inserting content above existing content. 4. Use CSS contain for dynamic elements.
Re-run Lighthouse, deploy, then monitor CrUX for 28-day rolling average improvement. Gate: Lab scores pass; await field data confirmation (28-day cycle).
Return audit results with specific fix recommendations prioritized by impact.
{
"audit": {"lcp": {"value_ms": 3200, "status": "poor", "element": "hero-image.jpg", "fixes": ["preload", "compress"]}},
"metadata": {"url": "...", "data_source": "crux", "device": "mobile"}
}
Input: URL with LCP=4.1s, CLS=0.32, INP=150ms Expected: LCP and CLS flagged as poor; INP passes. Fix priorities: CLS (image dimensions) → LCP (hero image preload)
| Input | Expected | Why |
|---|---|---|
| SPA with client rendering | High LCP likely | No server-rendered content for LCP element |
| Page with ads | High CLS likely | Ad slots inject content dynamically |
| All metrics pass in lab | May still fail field | Real devices on slow networks differ from lab |
references/optimization-techniques.mdreferences/crux-monitoring.mdNot using the CLI? Copy the SKILL.md and paste it straight into ChatGPT, Claude, or any agent.