This is a reference guide for optimizing content so AI engines like ChatGPT, Perplexity, and Claude actually cite you in their responses. It covers the RAG retrieval factors that matter (semantic relevance, authority signals, freshness), what content format gets cited (original stats, comparison tables, FAQ sections), and includes a Python audit script to check your site's citation readiness. The comparison tables are practical, especially the SEO vs GEO breakdown and the AI crawler user-agent list. The core insight is sound: AI engines prefer structured, attributed, timestamped content that's easy to extract. If you're trying to shift from ranking in Google to being quoted by LLMs, this gives you the checklist without the usual marketing fluff about a trend that's still forming.

skills.sh installs
258
GitHub stars
33.2K
last updated
Aug 21, 2026

Install

$ npx -y skills add davila7/claude-code-templates --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

GoalPlatform
Be cited in AI responsesChatGPT, Claude, Perplexity, Gemini

SEO vs GEO

AspectSEOGEO
Goal#1 rankingAI citations
PlatformGoogleAI engines
MetricsRankings, CTRCitation rate
FocusKeywordsEntities, data

2. AI Engine Landscape

EngineCitation StyleOpportunity
PerplexityNumbered [1][2]Highest citation rate
ChatGPTInline/footnotesCustom GPTs
ClaudeContextualLong-form content
GeminiSources sectionSEO crossover

3. RAG Retrieval Factors

How AI engines select content to cite:

FactorWeight
Semantic relevance~40%
Keyword match~20%
Authority signals~15%
Freshness~10%
Source diversity~15%

4. Content That Gets Cited

Sponsors

Details

Repository
github.com/davila7/claude-code-templates
First seen
Jun 3, 2026

More from davila7/claude-code-templates

All 618

Recommended

1juliusbrussee avatarcavemanjuliusbrussee/cavemanUltra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like caveman while keeping full technical accuracy. Supports intensity levels618.3Kskills.sh installs
2mattpocock avatargrill-memattpocock/skillsA relentless interview to sharpen a plan or design.1.5Mskills.sh installs
3shadcn avatarimproveshadcn/improveSurvey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on s40Kskills.sh installs
4obra avatarsystematic-debuggingobra/superpowersUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes326.7Kskills.sh installs
5forrestchang avatarkarpathy-guidelinesforrestchang/andrej-karpathy-skillsBehavioral guidelines to reduce common LLM coding mistakes through explicit assumptions, simplicity, and verifiable success criteria.17.9Kskills.sh installs
6vercel-labs avatarfind-skillsvercel-labs/skillsHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express int4.3Mskills.sh installs