Geo Fundamentals

If you're publishing content and want AI engines like ChatGPT or Perplexity to actually cite you instead of just training on your words, this gives you the framework. It breaks down how RAG retrieval works, what content structures get pulled into responses (original data, clean definitions, FAQ sections), and includes a Python auditor script to check your citation readiness. The SEO versus GEO comparison tables are genuinely useful. Fair warning that measurement is still mostly manual monitoring since there's no Google Search Console equivalent for AI citations yet. Treat this as the early playbook for a space that's still figuring itself out.

skills.sh installs: 949
GitHub stars: 43.1K
last updated: Jul 14, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill geo-fundamentals --agent claude-code

Installs into .claude/skills of the current project.

Files

GEO Fundamentals

Optimization for AI-powered search engines.

1. What is GEO?

GEO = Generative Engine Optimization

Goal	Platform
Be cited in AI responses	ChatGPT, Claude, Perplexity, Gemini

SEO vs GEO

Aspect	SEO	GEO
Goal	#1 ranking	AI citations
Platform	Google	AI engines
Metrics	Rankings, CTR	Citation rate
Focus	Keywords	Entities, data

2. AI Engine Landscape

Engine	Citation Style	Opportunity
Perplexity	Numbered [1][2]	Citation behavior varies by query/product
ChatGPT	Inline/footnotes	Custom GPTs
Claude	Contextual	Long-form content
Gemini	Sources section	SEO crossover

3. RAG Retrieval Factors

Potential retrieval considerations include relevance, source quality, freshness and diversity. The platforms do not publish a universal weighting formula; do not assign invented percentages or infer citation probability from this checklist.