agentleFS
schweppsschwepps/skills/geo-aeo-optimization/SKILL.md

geo-aeo-optimization

Skill16 starsChanged 10 months agoMITView on GitHub

Optimize content for AI search engines (ChatGPT, Perplexity, Claude, Gemini) and featured snippets. Covers Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), E-E-A-T signals, and content structuring for maximum AI citation probability. Use when optimizing content for AI visibility or auditing AI search readiness.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use question-format H2 headers

    line 67
  2. Distinctive:

    Critical: Ensure server-side rendering.

    line 212

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

GEO & AEO Optimization Framework

Professional methodology for visibility in AI-powered search engines and generative AI responses.

Understanding GEO vs AEO vs SEO

SEO  → Rank in traditional search results (blue links)
AEO  → Appear in featured snippets, AI Overviews, voice answers
GEO  → Get cited in generative AI responses (ChatGPT, Claude, Perplexity)

2025 Reality:

  • 58% of queries are conversational
  • AI Overviews appear in ~47% of Google searches
  • Traditional CTR dropped 34.5% for top results due to AI answers
  • These strategies are complementary, not competing

When to Use This Skill

  • Optimizing content for AI search engines
  • Improving featured snippet capture rate
  • Auditing AI citation readiness
  • Restructuring content for AI extractability
  • Implementing E-E-A-T signals
  • Preparing for voice search

The Triple-Threat Framework

1. E-E-A-T Foundation (Required for All)

AI systems prioritize trusted, authoritative sources:

Experience   → First-hand knowledge, case studies, original research
Expertise    → Credentials, depth of knowledge, technical accuracy
Authority    → Citations, backlinks, mentions, brand recognition
Trustworthiness → Accuracy, transparency, clear sourcing

Implementation Checklist:

□ Author bio with credentials on every article
□ Link to authoritative sources
□ Include original data/research
□ Show methodology and sources
□ Update content regularly with visible dates
□ Display contact information
□ Include editorial guidelines

2. AEO: Featured Snippets & AI Overviews

Lead with the Answer:

  • First 40-60 words should directly answer the query
  • Use question-format H2 headers
  • Provide concise, extractable definitions

Content Structure for AEO:

## What is [Topic]?

[Direct answer in 1-2 sentences that can be extracted as a snippet]

[Supporting details and context follow]

Snippet-Optimized Formats:

Query TypeBest Format
"What is X?"Definition paragraph (40-60 words)
"How to X"Numbered steps (5-8 steps)
"Best X"Bulleted list with brief descriptions
"X vs Y"Comparison table
"Why X?"Direct answer + explanation

3. GEO: AI Citation Optimization

The CRASP Framework:

C - Clarity    → Unambiguous statements AI can quote
R - Relevance  → Match conversational query intent
A - Authority  → Cited sources, expert credentials
S - Structure  → Scannable, extractable format
P - Precision  → Specific facts, numbers, dates

High-Citation Content Elements:

  1. TL;DR blocks at the top
  2. Statistics with sources ("According to [Source], X increased by Y%")
  3. Expert quotes with attribution
  4. Definition lists for key terms
  5. Comparison tables with clear data
  6. FAQ sections with complete answers

Content Structure Template

# [H1: Primary Question or Topic]

**TL;DR:** [2-3 sentence summary with key facts]

## What is [Topic]? {#definition}

[Direct definition in 40-60 words - optimized for extraction]

[Expanded explanation with context]

## How [Topic] Works {#how-it-works}

[Step-by-step explanation]

1. **Step One**: [Description]
2. **Step Two**: [Description]
3. **Step Three**: [Description]

## Key Statistics

| Metric | Value | Source |
|--------|-------|--------|
| [Stat 1] | [Value] | [Source, Year] |
| [Stat 2] | [Value] | [Source, Year] |

## [Topic] vs [Alternative]

| Factor | [Topic] | [Alternative] |
|--------|---------|---------------|
| [Factor 1] | [Value] | [Value] |
| [Factor 2] | [Value] | [Value] |

## Frequently Asked Questions

### [Question 1]?

[Complete, standalone answer - 50-100 words]

### [Question 2]?

[Complete, standalone answer - 50-100 words]

## Expert Insights

> "[Quotable statement with specific insight]"
> — [Expert Name], [Title], [Organization]

---
*Last updated: [Date] | Author: [Name], [Credentials]*

Fact-Density Checklist

Extractability:
□ TL;DR or summary present at top
□ Paragraphs <120 words each
□ Clear topic sentences (first sentence = main point)
□ Bullet lists for features/benefits
□ Tables for comparisons
□ FAQ section with schema

Fact-Density:
□ Statistics with sources cited
□ Specific numbers and dates
□ Verifiable data points
□ Expert quotes with attribution
□ Original research/data
□ Named entities (people, companies, places)

Structure for AI:
□ Question-based H2 headers
□ Direct answers in first 40-60 words
□ Logical information hierarchy
□ Schema markup (FAQPage, HowTo, Article)

AI Crawler Configuration

robots.txt for AI Search Visibility:

# ALLOW search/chat features
User-agent: OAI-SearchBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: ClaudeBot
Allow: /

# OPTIONAL: Block training (keeps search)
User-agent: GPTBot
Disallow: /

User-agent: Google-Extended
Disallow: /

Critical: Ensure server-side rendering. AI crawlers often don't execute JavaScript well.

Schema Markup for AI

Priority Schema Types:

  1. FAQPage - Highest AI citation impact
  2. HowTo - Step-by-step visibility
  3. Article + Author - E-E-A-T signals
  4. Organization - Brand authority

FAQPage Example:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What is [topic]?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "[Complete answer that can be extracted]"
    }
  }]
}

Output Format

# GEO/AEO Audit Report

**Page:** [URL]
**Date:** [Date]
**Overall Score:** X/100

## Executive Summary
[2-3 sentence AI search readiness assessment]

## Scores by Category

| Category | Score | Status |
|----------|-------|--------|
| E-E-A-T Signals | X/25 | [Good/Needs Work/Critical] |
| Content Structure | X/20 | [Good/Needs Work/Critical] |
| Fact-Density | X/20 | [Good/Needs Work/Critical] |
| Extractability | X/20 | [Good/Needs Work/Critical] |
| AI Technical Readiness | X/15 | [Good/Needs Work/Critical] |

## Priority Issues
1. **[Issue]** - [Impact] - [Fix]
2. **[Issue]** - [Impact] - [Fix]
3. **[Issue]** - [Impact] - [Fix]

## AI Visibility Assessment
[Current citation probability and gaps]

## Action Plan

**Immediate:**
- [ ] Add TL;DR to top articles
- [ ] Implement FAQ schema

**Short-term:**
- [ ] Restructure headers as questions
- [ ] Add author credentials

**Ongoing:**
- [ ] Monitor AI platform citations
- [ ] Update content freshness

Quick Audit (5-Point Check)

  1. TL;DR - Summary present at top?
  2. Structure - Question-based headers?
  3. Facts - Statistics with sources?
  4. E-E-A-T - Author credentials visible?
  5. Schema - FAQPage/HowTo markup?

Scoring Guide

  • 90-100: Excellent - High AI citation probability
  • 70-89: Good - Likely cited with minor improvements
  • 50-69: Needs Work - Significant optimization needed
  • Below 50: Critical - Major restructuring required

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.