SEO/GEO Optimization

Optimize web content for both traditional search engines (Google, Bing) and AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude).

GEO = Generative Engine Optimization - Optimizing content to be cited by AI search engines.

Key Insight: AI search engines don't rank pages - they cite sources. Being cited is the new ranking #1.

When to Use

• Auditing a website's SEO/GEO status
• Optimizing content for AI search engine citations
• Implementing or fixing structured data (JSON-LD, Schema.org)
• Researching keywords and competitor strategies
• Ensuring AI bots can crawl your site
• Adding meta tags, Open Graph, Twitter Cards
• Improving content structure for both humans and AI

Don't use for:

• General content writing without SEO focus (use creative-writing skill)
• Backend API performance (use performance-optimizer)

Workflow

digraph seo_geo {
  rankdir=TB;
  "Audit current state" -> "Keyword research";
  "Keyword research" -> "GEO optimization";
  "GEO optimization" -> "Traditional SEO";
  "Traditional SEO" -> "Validate & monitor";
  "Validate & monitor" -> "Audit current state" [label="iterate", style=dashed];
}

Step 1: Website Audit

Get the target URL and analyze current SEO/GEO status.

Quick technical check:

# Check meta tags and schema markup
curl -sL "https://example.com" | grep -E "<title>|<meta name=\"description\"|<meta property=\"og:|application/ld\+json" | head -20

# Check robots.txt - verify AI bots are allowed
curl -s "https://example.com/robots.txt"

# Check sitemap structure
curl -s "https://example.com/sitemap.xml" | head -50

AI bots to verify access for:

Step 2: Keyword Research

Use WebSearch to research target keywords:

• {keyword} keyword difficulty site:ahrefs.com OR site:semrush.com
• {keyword} search volume {current_year}
• site:{competitor.com} {keyword}

Analyze search volume, difficulty, competitor strategies, long-tail opportunities, and international keyword conflicts.

Step 3: GEO Optimization (AI Search Engines)

Apply the 9 Princeton GEO Methods (see reference/geo-methods.md):

Best combination: Fluency + Statistics = Maximum boost

Content structure for AI citation:

• "Answer-first" format (direct answer at top)
• Clear H1 > H2 > H3 hierarchy
• Bullet points and numbered lists
• Tables for comparison data
• Short paragraphs (2-3 sentences max)

FAQPage schema adds +40% AI visibility. See templates/schema-faqpage.json.

Step 4: Traditional SEO

Apply meta tags, Open Graph, Twitter Cards, and JSON-LD schema markup. See templates/meta-tags.md for copy-paste templates.

Quick checks:

• H1 contains primary keyword
• Images have descriptive alt text
• Internal links to related content
• External links have rel="noopener noreferrer"
• Mobile-friendly layout
• Page loads in < 3 seconds

Step 5: Validate & Monitor

# Schema validation
open "https://search.google.com/test/rich-results?url={encoded_url}"
open "https://validator.schema.org/?url={encoded_url}"

# Check indexing
open "https://www.google.com/search?q=site:{domain}"
open "https://www.bing.com/search?q=site:{domain}"

Generate report using checklists/seo-geo-audit.md.

Platform-Specific Quick Reference

Each AI search engine has different citation criteria. See reference/platform-optimization.md for full details.

Common Mistakes

Collaboration

• Works with: content-strategist agent (strategy + keyword planning), creative-writing skill (content quality)
• Complements: Content strategy with technical SEO implementation and GEO methodology
• Outputs: Optimized markup, schema, audit reports, GEO-enhanced content

Supporting Documentation

• reference/ - Detailed optimization guides

  • geo-methods.md - Princeton GEO research (9 methods in depth)
  • platform-optimization.md - Per-platform citation criteria

• checklists/ - Audit and validation

  • seo-geo-audit.md - Complete SEO/GEO audit checklist with report template

• templates/ - Copy-paste ready markup

  • meta-tags.md - Meta tags, Open Graph, Twitter Cards
  • schema-faqpage.json - FAQPage JSON-LD template
  • schema-templates.md - All JSON-LD schema types