What is GEO and how does it differ from SEO?

GEO (Generative Engine Optimization) focuses on getting content cited in AI-powered search responses, while SEO targets traditional search rankings. GEO measures citation rate and emphasizes entities/data, whereas SEO emphasizes keywords and ranking position on Google.

Which AI engines cite content and how do they do it?

Perplexity uses numbered citations ([1][2]) with the highest citation rate; ChatGPT cites inline or via footnotes; Claude cites contextually and favors long-form content; Gemini uses a sources section with SEO crossover.

What content elements are most likely to get cited by AI engines?

Original statistics, expert quotes, clear definitions, step-by-step guides, comparison tables, and FAQ sections are the content elements most frequently cited by AI engines.

What are the key technical elements needed for GEO?

The GEO content checklist includes Article schema with dates, Person schema for the author, FAQPage schema, sub-2.5s load time, clean HTML structure, and content elements like question-based titles, summaries, original data, expert quotes, and author credentials.

How do I audit my content for AI citation readiness?

You can run the companion script `python scripts/geo_checker.py ` to perform an automated GEO audit that flags anti-patterns like missing dates, vague attributions, missing author info, and thin content.

Skill

Optimize Content for AI Search Engine Citations

Name: GEO Fundamentals
Availability: OnlineOnly
Author: Antigravity

GEO fundamentals for getting cited by AI engines like ChatGPT, Claude, and Perplexity, with a content checklist and an audit script.

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Updated yesterday

Version 15.7.0

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Why it matters

Maximize your brand's visibility and authority within AI-powered search engines like ChatGPT, Claude, and Perplexity by optimizing your content for Generative Engine Optimization (GEO).

Outcomes

What it gets done

Implement GEO strategies to increase AI citation rates.

Audit content for AI citation readiness using provided checklists and scripts.

Understand AI engine citation factors and content preferences.

Manage AI crawler access to your digital assets.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-geo-fundamentals | bash

Overview

GEO Fundamentals

Explains Generative Engine Optimization - getting content cited by AI engines like ChatGPT, Claude, and Perplexity - covering RAG retrieval factors, a citation-readiness content and schema checklist, AI crawler access strategy, and a companion audit script. Use when optimizing content to be cited by AI answer engines rather than to rank in traditional search results.

What it does

Explains Generative Engine Optimization (GEO) - optimizing content to be cited in AI-powered search responses rather than to rank #1 in traditional search. It contrasts GEO against SEO (citation rate vs. rankings, entities/data vs. keywords, AI engines vs. Google), maps the AI engine landscape, breaks down what factors AI retrieval weighs, and provides a concrete checklist plus an audit script for making content AI-citable.

When to use - and when NOT to

Use this skill when working on GEO fundamentals tasks or workflows that need guidance, best practices, or checklists for getting content cited by AI engines. Do not use it for tasks unrelated to GEO, or when a different domain or tool is actually needed.

Inputs and outputs

AI engine landscape: Perplexity uses numbered citations ([1][2]) and has the highest citation rate; ChatGPT cites inline/via footnotes and offers Custom GPT opportunities; Claude cites contextually and favors long-form content; Gemini uses a sources section with SEO crossover.

RAG retrieval factors and their approximate weight in what gets cited: semantic relevance (~~40%), keyword match (~~20%), authority signals (~~15%), source diversity (~~15%), and freshness (~10%).

Content elements that get cited: original statistics (unique, citable data), expert quotes (authority transfer), clear definitions (easy to extract), step-by-step guides (actionable value), comparison tables (structured info), and FAQ sections (direct answers).

The GEO content checklist covers content elements (question-based titles, a summary/TL;DR at top, original sourced data, named expert quotes, a 3-5 question FAQ section, clear definitions, a last-updated timestamp, and author credentials) and technical elements (Article schema with dates, Person schema for the author, FAQPage schema, sub-2.5s load time, and clean HTML structure).

Entity building actions: pursuing a Google Knowledge Panel, Wikipedia notability where applicable, consistent information across the web for entity consolidation, and industry mentions as authority signals.

AI crawler access strategy covers key user-agents (GPTBot for ChatGPT/OpenAI, Claude-Web for Claude, PerplexityBot for Perplexity, Googlebot shared with Gemini) and the decision to allow all, block specific bots like GPTBot, or apply selective access.

Measurement covers tracking AI citations via manual monitoring, searching for "According to [Brand]" mentions in AI responses, comparing citation share against competitors, and tracking AI-referred traffic via UTM parameters.

A companion script performs a GEO audit:

python scripts/geo_checker.py <project_path>

Anti-patterns flagged: publishing without dates, vague source attributions, skipping author info, and thin content instead of comprehensive coverage.

Integrations

Includes scripts/geo_checker.py for automated AI-citation-readiness auditing of a project path.

Who it's for

Content and marketing teams who want their content cited by AI answer engines (ChatGPT, Claude, Perplexity, Gemini) rather than just ranked in traditional search - anyone building out schema, entity presence, and citation-ready content structure.

FAQ

Common questions

Discussion