🌐 GEO & AEO AI Agent Optimization Skill
This skill defines deterministic rules, schemas, and SOPs for optimizing web content, technical documentation, and AI agent output for Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO).
🎯 1. Core Principles of GEO/AEO vs Traditional SEO
| Dimension | Traditional SEO | AEO (Answer Engine Opt.) | GEO (Generative Engine Opt.) |
|---|---|---|---|
| Primary Target | Blue links on Google SERP | Featured Snippets, PAA (People Also Ask) | Citations in Perplexity, ChatGPT Search, Gemini, Google AI Overviews |
| Parsing Unit | Keywords in Title & H1 | 40-55 word direct answer block below H2 | Knowledge graphs, semantic entity triples, verified numbers |
| Validation Gate | Backlinks & CTR | Rich Results & schema matching | Information Gain score & consensus authority |
📐 2. Deterministic Content Formatting Rules (For AI Agents)
Inverted Pyramid Answer Block (First 60 Words):
- Immediately below any major heading (
<h2>,<h3>), place a 40–55 word direct, declarative answer. - Prohibit pleasantries ("In this article...", "It is important to remember").
- Formula:
[Entity/Topic] is [Category/Definition] that [Primary Function/Mechanism] by [Concrete Differentiator].
- Immediately below any major heading (
Structured Table Extraction (Markup Rule):
- Always format multi-item comparisons (features, pricing, benchmarks) into native HTML
<table>blocks with strict<th>and<td>tags. - LLM crawlers prioritize tabular data over narrative prose for comparative queries.
- Always format multi-item comparisons (features, pricing, benchmarks) into native HTML
Information Gain & Primary Statistics:
- Every article must contain at least 2 proprietary or specific numerical findings (e.g., "Based on an evaluation of 500+ endpoints...").
- LLMs favor citing primary sources with novel metrics rather than duplicate paraphrases.
Entity Bold Formatting in Lists:
- In bullet points, always start with a bold entity label (e.g.,
**1. Protocol Latency:** ...).
- In bullet points, always start with a bold entity label (e.g.,
🏷️ 3. JSON-LD Entity Graph Schema Template
Every optimized page must inject unambiguous Schema.org entity definitions linking to external knowledge graphs (Wikidata / Crunchbase):
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"@id": "https://example.com/#organization",
"name": "BrandName",
"url": "https://example.com",
"sameAs": [
"https://www.wikidata.org/wiki/Q...",
"https://www.crunchbase.com/organization/..."
]
},
{
"@type": "TechArticle",
"@id": "https://example.com/post-slug/#article",
"isPartOf": { "@id": "https://example.com/#website" },
"headline": "Exact Target Topic Headline",
"about": [
{
"@type": "Thing",
"name": "Primary Entity",
"sameAs": "https://en.wikipedia.org/wiki/..."
}
],
"author": {
"@type": "Person",
"name": "Yevhen Shaforostov",
"jobTitle": "AI Product Manager & Full-Stack AI Engineer",
"sameAs": "https://github.com/yevhens-hue"
}
}
]
}
</script>
✅ 4. AI Agent Quality & Validation Gate (Pass/Fail Checklist)
Before publishing or accepting an agent output, run this 5-point verification:
- Inverted Answer Block: Does the H2 have an immediate 40–55 word direct definition?
- Entity Ambiguity: Are all brand and technical terms linked to unambiguous schema definitions?
- Information Gain: Does the text contain at least one verifiable quantitative statistic or benchmark?
- Tabular Comparison: Is comparative data presented in a clean
<table>instead of a paragraph? - Zero Fluff: Are all filler introductions removed?