bencium-aeo

AEO Content Optimization Skill

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AEO Content Optimization Skill

Answer Engine Optimization - Optimize content for AI citations, not traditional search rankings.

When to Use This Skill

Use this skill when:

  • User asks to optimize content for AI search/citations

  • User mentions ChatGPT, Claude, Gemini visibility

  • User wants FAQ schema, JSON-LD, or structured data for AI

  • User asks about GEO (Generative Engine Optimization)

  • User wants to analyze content for AI extraction readiness

  • User mentions "AI Overviews" or "answer engines"

NOT for traditional SEO - This is specifically for AI/LLM citation optimization.

Core Reference

Full templates and guidelines: Read prd.md in this directory for complete implementation details.

Quick Reference: Key Principles

The 18-Token Extraction Rule

LLMs extract self-contained sentences of ~18 tokens (~15-20 words). Key claims must be complete, quotable statements requiring zero surrounding context.

Good: "Eight-API synthesis reduces property analysis errors by 67%." (9 tokens) Bad: "Our system is incredibly fast and delivers amazing results." (vague)

Single-Topic Focus Pages

Single-concept pages vastly outperform multi-topic content. Create focused URLs like domain.com/specific-concept rather than comprehensive guides.

Citations + Statistics = 30-40% More Visibility

Every major claim needs:

  • Verifiable data with methodology

  • Date of data collection

  • Expert attribution (Name + Credentials + Org)

Freshness is Critical

95% of AI citations come from content updated in last 10 months. Static content dies.

Authority Level Determines Strategy

Authority Level Optimization Approach

Challenger (new sites, low authority) Aggressive: 5-7 extraction points per page, heavy citations, weekly micro-updates

Established (top-ranked, well-known) Light touch: 1-2 strategic points, trust existing credibility, avoid over-optimization

Princeton finding: Rank-5 sites gained 115% visibility with aggressive optimization. Rank-1 sites that over-optimized lost 30%.

What to Generate

When user requests AEO content, generate:

  1. Product Overview (50 words)
  • What it is (one clause)

  • Scope/timeframe context

  • Why it matters (value proposition)

  • "Last updated" date

  1. 15 FAQs with Schema
  • Questions: 7-12 words, natural language

  • Answers: 30-50 words (sweet spot for AI extraction)

  • FAQPage JSON-LD schema with datePublished and dateModified

  • Persistent anchor IDs (#faq-slug)

  1. Evidence Panels

For every important claim:

  • Claim statement

  • Methodology

  • Data source + URL

  • Date of data collection

  • Limitations

  • Contact for questions

  1. JSON-LD Schema
  • FAQPage (most important)

  • HowTo (for guides)

  • Product (for product pages)

  • Organization (for About page)

Anti-Patterns (What to Avoid)

Traditional SEO Tactics Harm GEO

  • Keyword stuffing

  • Generic listicles without original insight

  • Vague hedged language ("may help", "could potentially")

  • Multi-topic comprehensive guides

  • Over-optimization on established sites

Content Structure Errors

  • FAQ answers over 50 words

  • Buried answers (put conclusion first)

  • Pronoun ambiguity ("it" instead of "the product")

  • Missing dates and freshness signals

  • No schema markup

Assessment Framework

When analyzing content for AEO readiness, score (0-10):

Dimension What to Check

Extraction How many citation-ready sentences under 18 tokens?

Focus Single topic or sprawling multi-topic?

Authority Expert attribution with credentials? Citations?

Freshness Updated within 90 days? Dated content?

Quick test: Can you copy-paste 3 sentences that fully answer a question without context?

Implementation Checklist

  • Product overview: 50 words, dated, under H1

  • 15 FAQs: 30-50 words each, natural questions

  • Evidence panels: method, data, date, limitations

  • "Last updated" dates on every section

  • FAQPage JSON-LD schema in <head>

  • Persistent anchor IDs for FAQs

  • Validated with Google Rich Results Test

Testing Protocol

After implementation, test with:

  • Recognition: "What is [Product]?" (ChatGPT, Claude, Gemini)

  • Comparison: "Compare [Product] to [Competitor]"

  • Best for: "What's the best [category] for [use case]?"

  • How-to: "How do I [task with product]?"

Track: Mentioned? Linked? Accurate? Evidence quoted?

Full Documentation

For complete templates, examples, and detailed guidelines, read:

  • prd.md

  • Full AEO content generation guide with HTML templates

  • story-structured.md

  • Framework summary from Princeton study

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