seo-master

name	seo-master
description	Complete SEO + GEO (Generative Engine Optimization) + AEO (Answer Engine Optimization) playbook for 2026. Covers traditional search ranking, how AI engines + coding agents (ChatGPT/Bing, Claude+Claude Code/Codex/OpenClaw/Brave, Gemini/Google, Perplexity) discover/rank/cite pages, framework-aware implementation (Next.js App Router Metadata API + React SPA prerender), structured data, technical SEO, titles/meta, content strategy, IndexNow, Google + Bing Search Console, competitor analysis, the 2026 I/O + core-update changes, and a prioritized checklist. Use when optimizing any website for search engines and AI answer engines, planning content/SEO strategy, adding structured data, fixing titles/meta, getting cited by AI chatbots/agents, or setting up Search Console.
license	MIT
user-invocable	true
argument-hint	[site path or URL to optimize]
when_to_use	Optimizing a website for search + AI answer engines; planning content/SEO strategy; implementing SEO in Next.js or React; adding structured data; fixing titles/meta; getting cited by ChatGPT/Claude/Gemini/coding agents; setting up Google/Bing Search Console + IndexNow.

SEO Master Skill (SEO + GEO + AEO, 2026)

Battle-tested playbook distilled from optimizing a real SaaS (letx.app, a collaborative LaTeX editor) against AI-native competitors. Everything here is applied knowledge, not theory.

The one-paragraph mental model

In 2026 there are two audiences: classic search crawlers (Google, Bing) and AI answer engines (ChatGPT, Claude, Gemini, Perplexity, Copilot). They overlap but differ. SEO ranks you in a list of links. GEO/AEO gets you cited inside an AI's generated answer. A solid SEO foundation is required for both; AEO/GEO adds: answer-first writing, dense structured data (especially FAQPage), fact density, freshness, and being present where each AI grounds its search. Critically — each AI uses a different search index, so "getting indexed" is not one task but several.

How to use this skill

1. Detect the stack first (package.json → Next.js App/Pages Router, React SPA, Astro, Remix…). Implementation differs by render model. See references/framework-seo.md.
2. Read references/ai-engines-and-indexes.md — determines where to optimize. Wrong index = invisible to that AI. (Coding agents like Claude Code / Codex / OpenClaw / Hermes mostly ground on Brave.)
3. Skim references/whats-new-2026.md for the I/O 2026 + May core-update context (AI Mode on Gemini 3.5 Flash; "AEO/GEO is still SEO"; llms.txt is optional).
4. Run the audit in references/implementation-checklist.md against the site.
5. Fix in priority order: indexing reach → structured data → answer-first content → titles/meta → content moat.
6. Use references/search-console-setup.md for Google + Bing + IndexNow, and references/structured-data.md / references/content-and-aeo.md as copy/paste references while implementing.

Reference files (read as needed)

File	What's in it
references/ai-engines-and-indexes.md	Which index each AI + coding agent uses (Brave/Bing/Google), how each cites, per-engine tactics
references/framework-seo.md	Detect the stack; Next.js App Router Metadata API (metadata/generateMetadata, sitemap/robots/OG conventions, render modes) + Pages Router + React SPA
references/whats-new-2026.md	Google I/O 2026, May 2026 core update, "AEO/GEO still SEO", llms.txt contested, AI-Mode query shift
references/structured-data.md	Every schema type with ready JSON-LD: FAQPage, HowTo, Course, SoftwareApplication, Article, Breadcrumb, ItemList, Product. Policy rules (fake ratings).
references/titles-and-meta.md	Title formula, the brand-suffix decision, meta description, OG/Twitter, canonical
references/content-and-aeo.md	Answer-first writing, fact density, FAQ pattern, content hubs, the "problems/errors" play, courses
references/technical-seo.md	Prerendering SPAs, sitemap, robots.txt + Content-Signals, llms.txt, code-splitting, Core Web Vitals, canonical/redirects
references/search-console-setup.md	Google Search Console, Bing Webmaster Tools + AI Performance, IndexNow (Cloudflare + script), submission steps
references/competitor-and-positioning.md	How to research competitors, find the open lane, off-site listicle strategy
references/implementation-checklist.md	Prioritized, copy-paste audit + execution checklist
references/gsc-diagnostics-and-recovery.md	Diagnose a site FROM Search Console data: brand/non-brand segmentation, striking-distance vs page-2 cohorts, slash-pair detection, the prerendered-SPA canonical trap + fix recipe, CTR surgery that still works in 2026, CLS quick wins, verification curl checks

Hard rules (learned the hard way)

• Never fabricate aggregateRating/reviews in schema. Google structured-data policy violation → manual action. Only use rating schema backed by visible, real on-page reviews. Use featureList / plain claims instead.
• Never copy a competitor's stat. ("20M users" belonged to Overleaf, not us — caught and removed.) Every claim must be true and yours.
• Title ≤ 60 chars or Google truncates. Long brand descriptors don't fit on content pages — use a short | Brand suffix there.
• FAQ schema requires the FAQ to be visible on the page. Schema-only FAQ = policy risk. Always render the questions AND emit JSON-LD.
• Code-split heavy content (course data, KaTeX, etc.) so the main JS bundle every visitor downloads stays small. Prerendered HTML keeps full content for SEO regardless.
• IndexNow does NOT notify Google — only Bing/Yandex/DuckDuckGo/etc. Google uses its own crawl + Search Console.
• Detect the framework before implementing. Next.js App Router uses the Metadata API (metadata/generateMetadata) — not helmet, not prerender. Only hand-rolled React SPAs need the Puppeteer prerender. Wrong tool = wasted work.
• Don't over-optimize for AI citations. The May 2026 core update appears to demote shallow answer-bait. Depth + real E-E-A-T + genuine fact density win; thin Q&A spam is now a risk, not a hack.
• llms.txt is optional, low priority — Google says it "isn't needed for AI Search." Add it only if cheap; never before content/schema/indexing.
• Exactly ONE canonical per page, verified with curl on the deployed site. Prerendered SPAs bake the static index.html canonical into every page and helmet adds a second — both wrong. The crawler reads baked HTML, not your DevTools. (Found live on a 500-user product; suppressed the whole site.)
• Trailing slash: standardize on whatever the HOST redirects to (Cloudflare Pages 308s to slash). Canonicals, sitemap, og:url, and internal hrefs must all use that one form, or GSC splits impressions across slash pairs.
• Segment GSC before concluding anything. Blended CTR/position lie: split brand vs non-brand, pos 4–15 (snippet problem) vs pos >15 (content-depth problem). New-content launches drop blended metrics mechanically — mix shift, not a penalty.
• FAQ rich results no longer show for normal sites (gov/health only since 2023). Keep FAQPage for AI citation; for SERP CTR use title/meta rewrites and ItemList-with-images on collection pages instead.
• Don't merge "duplicates" reflexively. Two pages at pos 5–6 for one query = double SERP presence (keep + differentiate); and near-identical slugs may be genuinely different products — verify content before 301ing.

The priority order (highest ROI first)

1. Indexing reach — be in Google, Bing, and Brave indexes; wire IndexNow. (Feeds ChatGPT via Bing, Claude via Brave.)
2. Structured data — FAQPage on every page that can carry it (~3.2× AI Overview citation). Plus SoftwareApplication/Product/Article/HowTo.
3. Answer-first content — direct answer in first 40–60 words; 134–167-word self-contained passages.
4. Titles/meta — consistent, keyword-first, ≤60 chars.
5. Content moat — guides/tutorials/error-fix hub targeting the long-tail the competitor monetizes.
6. Off-site — get listed in the "alternatives" listicles AI engines cite.