| name | seo-geo |
| description | Generative Engine Optimization (GEO) — audit and implementation for AI search surfaces: Google AI Overviews and AI Mode, ChatGPT search, Perplexity, Bing Copilot. Covers AI-crawler accessibility, question-based citability scoring, passage extraction, server-side rendering checks for AI bots, llms.txt (with the primary-source caveat on real support), agent-friendly page checks, and a 0-100 scoring rubric. Use when user says "AI Overviews", "AI Mode", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", "agent-friendly", or "AI visibility". Triggers on: GEO, AEO, AI Overviews, AI Mode, generative engine optimization, llms.txt, AI crawler, citability, Perplexity, ChatGPT search.
|
| version | 1.1.0 |
AI Search / GEO Optimization
GEO is the practice of making content extractable and citable by AI search
surfaces. This skill is the technical/audit + implementation half: crawler
access, render strategy, passage citability, and a falsifiable scoring rubric.
For the broader content-strategy and AI-visibility-monitoring side (competitive
citation tracking, content briefs, getting cited as a source over time), see the
ai-seo skill — cross-reference it, don't duplicate it here.
Primary Source: Google's AI Optimization Guidance
Google's own position (Search Central): optimizing for generative AI search is
still SEO. "AEO" and "GEO" are mostly rebranded labels for the same
fundamentals — quality content, crawlable/indexable pages, good structure,
unique value. Frame GEO findings as SEO fundamentals applied to AI-search
surfaces, not as a separate discipline.
Things Google has explicitly said do not move the needle (treat community
advice that contradicts this with skepticism, and note the contradiction in any
report):
llms.txt is not a citation/ranking signal for Google's AI search.- Artificially chunking content for "AI parsing" is unnecessary.
- AI-rephrasing existing copy to sound "LLM-friendly" does not help.
- Mention-farming / coordinated brand-mention campaigns are not a ranking lever.
The durable test for any page is the classic quality framing — Who made it
(real expertise/authorship), How it was made (original effort, not spun),
and Why it exists (to help users vs. to game search). If a tactic fails the
Who/How/Why test, drop it.
Relationship between the two Google AI surfaces
Google runs two distinct citation engines, and they cite different URLs:
| Surface | Selection behaviour | Optimize for |
|---|
| AI Overviews | Strongly ranking-correlated — cites pages that already rank well | Classic SEO + passage optimization |
| AI Mode | Weakly ranking-correlated; broader pool (~9 domains cited/query) | Freshness, entity authority, citable passages beyond position 5 |
AI Mode and AI Overviews reach the same conclusion most of the time but cite
the same URLs a small minority of the time (Ahrefs, large query-pair study).
Treat them as separate surfaces and score both: ranking well in classic Search
feeds AI Overviews, but AI Mode draws from a broader pool where freshness and
entity authority outweigh raw position.
Key Statistics (directional, verify before quoting to a client)
| Metric | Value | Source |
|---|
| AI Overviews reach | ~1.5B users/month, 200+ countries | Google |
| AI Overviews query coverage | 50%+ of queries | Industry data |
| AI Mode monthly users | 1B+ | Google |
| AI-referred sessions growth | 527% (Jan–May 2025) | SparkToro |
| ChatGPT weekly active users | ~900M | OpenAI |
Brand mentions correlate ~3x more strongly with AI visibility than backlinks
(Ahrefs, Dec 2025, 75k brands). Of the mention signals, YouTube mentions show
the strongest correlation (~0.737); Domain Rating from backlinks is weak
(~0.266). Only ~11% of domains are cited by both ChatGPT and Google AIO for the
same query — platform-specific optimization is real.
GEO Scoring Rubric (0–100)
Score each dimension 0–100, then weight. Every dimension has a falsifiability
check: how you would know it failed, plus the leading indicator to watch.
| # | Dimension | Weight |
|---|
| 1 | Citability | 25% |
| 2 | Structural readability | 20% |
| 3 | Authority & freshness | 20% |
| 4 | Technical accessibility (AI crawlers + render) | 20% |
| 5 | Multi-modal content | 15% |
final = round(0.25·cite + 0.20·struct + 0.20·auth + 0.20·tech + 0.15·multi)
Bands: 80–100 AI-ready · 60–79 competitive, gaps remain · 40–59
significant work · <40 largely invisible to AI surfaces.
1. Citability (25%)
Run passage scoring against boilerplate-stripped main text (strip nav,
header, footer, sidebars — use your own extractor/crawler), not raw HTML, so
chrome doesn't dilute the signal.
- Optimal self-contained answer block: ~134–167 words.
- Front-load it: a large share of AI citations come from the first ~30% of a
page (SE Ranking) — put the most citable answer near the top, not below the fold.
- Direct answer in the first 40–60 words of each section.
- "X is…" / "X refers to…" definition patterns score high.
- Specific facts/statistics with source attribution; unique data points.
- Penalize: vague generalities, buried conclusions, opinion without evidence.
Falsifiability: Failed if an AI surface, asked the page's target question,
answers without citing the page despite the page ranking on p1. Leading
indicator: of the top question-headings, what % have a clean, extractable
40–60-word answer block immediately under them? Below ~50% predicts low citation.
2. Structural readability (20%)
- Clean H1→H2→H3 hierarchy; no skipped levels.
- Question-based headings that mirror real query phrasing (see citability
scoring below).
- Short paragraphs (2–4 sentences); lists for steps/multi-item; tables for
comparative data; FAQ Q&A blocks.
- Penalize: wall-of-text, inconsistent hierarchy, no lists/tables.
Falsifiability: Failed if a heading outline extracted from the DOM reads
as topical labels ("Overview", "Features") rather than questions a user types.
Leading indicator: ratio of question-form headings to total headings.
3. Authority & freshness (20%)
- Author byline with real credentials; Organization + Person entity signals.
- Citations to primary sources (studies, official docs, data).
- Entity presence: Wikipedia/Wikidata, Reddit, YouTube, LinkedIn (
sameAs).
- Freshness is high-leverage. Recent content (under ~3 months) is markedly
more likely to be cited; pages left stale 6+ months lose citation eligibility
(SE Ranking, 1.3M-citation study). A scheduled refresh program — with a real
dateModified change, not a touched timestamp — is one of the best GEO plays.
- Penalize: anonymous authorship, missing dates, no sources.
Falsifiability: Failed if the most valuable pages have dateModified
older than 6 months with no substantive update. Leading indicator: median age
of top-traffic pages.
4. Technical accessibility (20%)
AI crawlers generally do NOT execute JavaScript — content that only appears
after client hydration is invisible to most of them. Server render it.
- SSR/RSC vs client-only: is the answer in the initial HTML?
- AI crawler access in
robots.txt (see table below).
- "Agent-friendly" basics: stable URLs, real
<a href> links (not JS-only
click handlers), text content in HTML rather than canvas/image-only.
llms.txt presence (reported, zero citation weight — see caveat).
Falsifiability: Failed if curl-ing the URL (no JS) returns a shell
without the primary answer text. Leading indicator: ratio of
extractable-text bytes in raw HTML vs. post-hydration DOM.
5. Multi-modal content (15%)
Pages with relevant images/video/charts/interactive elements show materially
higher AI selection rates. Check for text + relevant media, supporting
structured data, and that media has real alt/captions (text the crawler reads).
Falsifiability: Failed if a how-to/comparison page is text-only where the
query clearly wants a visual. Leading indicator: presence of at least one
captioned, relevant media element per major section.
Question-based citability scoring (per heading)
AI answers are question-shaped. Score each section heading and its lead passage:
- Is the heading a question (or trivially rephrasable as one matching a real
query)? Headings like "How do I…", "What is…", "X vs Y" are prime.
- Does the first sentence answer it directly in 40–60 words, self-contained?
- Is there a quotable fact/number with attribution in the block?
- Is the block extractable without surrounding context?
Score each 0/1, average over headings → section citability sub-score. This is the
fastest lever: rewriting H2/H3s as questions and adding a direct lead answer
raises citability without new content.
AI Crawler Access (robots.txt)
| Crawler | Owner | Purpose |
|---|
| GPTBot | OpenAI | ChatGPT web/search |
| OAI-SearchBot | OpenAI | OpenAI search features |
| ChatGPT-User | OpenAI | ChatGPT browsing on user request |
| ClaudeBot | Anthropic | Claude web features |
| PerplexityBot | Perplexity | Perplexity AI search |
| Google-Extended | Google | Gemini/Vertex training opt-out token |
| CCBot | Common Crawl | Training data (often blocked) |
| Bytespider | ByteDance | TikTok/Douyin AI |
Recommendation: allow GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot,
PerplexityBot for AI-search visibility. Note that Google-Extended is a
training opt-out token — blocking it does not remove you from AI Overviews/AI
Mode (those follow normal Googlebot crawling). Block CCBot / training-only
crawlers only if licensing policy requires it.
Next.js robots config (App Router)
import type { MetadataRoute } from 'next'
const SITE = process.env.NEXT_PUBLIC_SITE_URL ?? 'https://example.com'
const AI_SEARCH_BOTS = ['GPTBot', 'OAI-SearchBot', 'ChatGPT-User', 'ClaudeBot', 'PerplexityBot']
export default function robots(): MetadataRoute.Robots {
return {
rules: [
{ userAgent: '*', allow: '/' },
...AI_SEARCH_BOTS.map((userAgent) => ({ userAgent, allow: '/' })),
{ userAgent: 'CCBot', disallow: '/' },
],
sitemap: `${SITE}/sitemap.xml`,
}
}
Server-side rendering: the make-or-break check
Because AI crawlers don't run JS, the citable answer must be in the initial
HTML payload. On our stack this means rendering it in a Server Component
(default), not gating it behind a 'use client' boundary that fetches on mount.
import { getTranslations } from 'next-intl/server'
import { getGuide } from '@/lib/payload'
export default async function GuidePage({ params }: { params: Promise<{ slug: string; locale: string }> }) {
const { slug } = await params
const guide = await getGuide(slug)
return (
<article>
<h1>{guide.title}</h1>
{/* Lead answer block: 40–60 words, self-contained, first thing in the DOM */}
<p className="lead">{guide.summary}</p>
{guide.sections.map((s) => (
<section key={s.id}>
{/* Question-form heading mirrors the target query */}
<h2>{s.question}</h2>
<p>{s.directAnswer}</p>
{s.body}
</section>
))}
</article>
)
}
Verify with a JS-disabled fetch (use your own crawler/tooling, or
curl -A "GPTBot" <url>): the answer text must be present in the raw HTML.
llms.txt — implement, but no false hope
The /llms.txt convention lets a site advertise its structured content to
agents. Primary-source caveat: Google has stated it is not a citation or
ranking signal for AI search, and server-log audits show major AI search systems
rarely fetch it. Report its presence in an audit but assign it zero
citation-ranking weight. It is cheap and harmless to ship (some agent tooling
reads it), so treat it as hygiene, not a lever — never promise visibility gains
from it.
# Example /llms.txt
# Acme Docs
> Developer documentation for the Acme platform.
## Core
- [Quickstart](https://example.com/docs/quickstart): 5-minute setup
- [API reference](https://example.com/docs/api): Full endpoint reference
## Key facts
- Founded 2019, EU-hosted, GDPR-compliant
You can serve it statically (public/llms.txt) or generate it from the CMS:
import { getDocsIndex } from '@/lib/payload'
const SITE = process.env.NEXT_PUBLIC_SITE_URL ?? 'https://example.com'
export const revalidate = 3600
export async function GET() {
const pages = await getDocsIndex()
const body = [
'# Acme Docs',
'> Developer documentation for the Acme platform.',
'',
'## Core',
...pages.map((p) => `- [${p.title}](${SITE}${p.path}): ${p.summary}`),
].join('\n')
return new Response(body, { headers: { 'Content-Type': 'text/plain; charset=utf-8' } })
}
Structured data for AI discoverability (RSC, JSON-LD)
Emit entity signals server-side. Keep dateModified honest — bump it only on a
real content change so the freshness signal stays trustworthy.
function JsonLd({ guide, site }: { guide: Guide; site: string }) {
const data = {
'@context': 'https://schema.org',
'@type': 'Article',
headline: guide.title,
datePublished: guide.publishedAt,
dateModified: guide.updatedAt,
author: {
'@type': 'Person',
name: guide.author.name,
jobTitle: guide.author.role,
sameAs: guide.author.profiles,
},
publisher: {
'@type': 'Organization',
name: 'Acme',
sameAs: [`${site}`, 'https://www.linkedin.com/company/acme'],
},
}
return <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify(data) }} />
}
For FAQ/HowTo markup and full schema patterns, defer to the seo-schema
skill rather than re-deriving it here.
Platform-Specific Optimization
| Platform | Key citation sources | Focus |
|---|
| Google AI Overviews | Pages that already rank well | Classic SEO + passage optimization |
| Google AI Mode | Broader pool, weak rank correlation | Freshness, entity authority, citable passages beyond p5 |
| ChatGPT | Wikipedia (~48%), Reddit (~11%) | Entity presence, authoritative sources |
| Perplexity | Reddit (~47%), Wikipedia | Community validation, recency, structure |
| Bing Copilot | Bing index, authoritative sites | Bing SEO, IndexNow |
Audit Output
Produce GEO-ANALYSIS.md:
- GEO Readiness Score: XX/100 + per-dimension breakdown
- Per-surface scores (AI Overviews, AI Mode, ChatGPT, Perplexity, Copilot)
- AI crawler access status — exactly which bots allowed/blocked + the
robots.txt directives to fix it
- SSR/render check — does the raw HTML contain the answer text? (per key page)
- Passage citability — question-heading score, identified weak blocks, and
specific 134–167-word rewrites
- Authority & freshness — author/entity signals, median page age
- llms.txt status — present/absent (zero ranking weight; template if absent)
- Top 5 highest-impact changes with effort estimates
- Falsifiability notes — for each top change, how we'd know it worked
(leading indicator) and how we'd know it failed
When a community recommendation contradicts Google's primary-source guidance,
defer to Google and flag the contradiction.
Error handling
| Scenario | Action |
|---|
| URL unreachable | Report clearly; don't guess content; ask user to verify URL |
| AI crawlers blocked | List exactly which are blocked; provide robots.txt fix |
No llms.txt | Note absence (zero weight); provide template; don't oversell it |
| Answer absent from raw HTML | Flag client-only rendering; recommend RSC/SSR move |
| No structured data | Recommend Article/Organization/Person (defer to seo-schema) |
Priority Playbook
Quick wins — rewrite H2/H3s as questions; add a 40–60-word direct answer
under each; front-load the most citable block; add specific stats with sources;
publish/updated dates; allow key AI crawlers in robots.txt; Person schema for
authors.
Medium — move client-only answer content into RSC/SSR; author bios with
credentials + sameAs; comparison tables; ship /llms.txt (hygiene only);
set up a content-freshness refresh cadence.
High impact — original research/surveys (unique citability); entity
presence (Wikipedia/Wikidata, YouTube, Reddit); comprehensive sameAs entity
linking; build/own a useful tool or calculator that earns mentions.
Parts adapted from claude-seo (MIT, © 2026 agricidaniel).
