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aeo-optimization skill

/skills/aeo-optimization

This skill helps optimize content for AI engines by structuring semantic triples, category explainers, and product pages to boost authoritative citations.

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---
name: aeo-optimization
description: AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
---

# AI Engine Optimization (AEO) Skill

*Load with: base.md + web-content.md + site-architecture.md*

**Purpose:** Optimize content for AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) so your brand gets cited in AI-generated answers.

**Source:** Based on [HubSpot's AEO Guide](https://www.hubspot.com/aeo) and industry best practices.

---

## Why AEO Matters Now

```
┌────────────────────────────────────────────────────────────────┐
│  THE GREAT DECOUPLING                                          │
│  ────────────────────────────────────────────────────────────  │
│  Impressions ≠ Clicks anymore.                                 │
│  AI engines compile answers from multiple sources.             │
│  More buyer journey happens inside chat experiences.           │
│  58% of Google searches = zero clicks (AI overviews).          │
├────────────────────────────────────────────────────────────────┤
│  THE OPPORTUNITY                                               │
│  ────────────────────────────────────────────────────────────  │
│  Shape what AI engines say about your category and product.    │
│  Get cited as the authoritative source.                        │
│  Best answer > Best page ranking.                              │
└────────────────────────────────────────────────────────────────┘
```

**Key Stats:**
- 70% of consumers use ChatGPT for searches
- 47% of Google queries show AI overviews
- Average ChatGPT prompt: 23 words (vs 4.2 for Google)
- AEO market: $886M (2024) → $7.3B (2031)

---

## How AI Engines Choose Answers

AI engines use three main signals to select content for answers:

### 1. Consensus

Facts that appear across multiple credible sources get trusted and reused.

**How to build consensus:**
- Repeat key facts consistently across your own pages
- Use same terminology as industry leaders
- Link to and from authoritative external sources
- Create internal content clusters that reinforce each other

### 2. Information Gain

Net-new insight beats generic advice. AI engines prefer content that adds value.

**How to add information gain:**
- Original research and data
- Concrete examples with specifics
- Clear point of view (not fence-sitting)
- Expert quotes with credentials
- Case studies with metrics

### 3. Entities & Structure

Clear entities and tidy structure reduce ambiguity and boost quotability.

**How to optimize structure:**
- Use semantic triples (Subject → Verb → Object)
- Clear headings with entity names
- Schema markup (Article, FAQ, Product)
- Short, scannable paragraphs (2-4 sentences)

---

## Semantic Triples (Critical for AEO)

**What they are:** Compact facts that AI engines (and humans) can't misread.

**Pattern:** `[Subject]` `[verb]` `[object]`.

### Examples

```
✅ GOOD (clear triples):
- HubSpot CRM syncs contact and company data.
- Lead Scoring assigns priority based on engagement.
- Workflows trigger email sequences from events.

❌ BAD (vague, no clear entity):
- The system helps with various tasks.
- It can do many things for users.
- This improves overall performance.
```

### Triple Checklist

For every key claim, ask:
- [ ] Is the subject a clear entity (product, feature, brand)?
- [ ] Is the verb specific and active?
- [ ] Is the object concrete and measurable?

---

## Paragraph Pattern (Feature → How → Outcome)

Every substantive paragraph should follow this structure:

```
[Feature] helps [User/Role] with [Job].
It [mechanism/inputs] to [process].
Teams see [metric/result] in [timeframe/context].

Triples:
- [Subject] [verb] [object].
- [Subject] [verb] [object].
```

### Example

```markdown
Lead Scoring helps sales teams prioritize prospects. It combines
page views, email engagement, and firmographic data to assign a
numeric score, then auto-enrolls high scorers into follow-up
sequences. Reps focus on qualified accounts and book 40% more
meetings.

- Lead Scoring assigns scores from engagement data.
- High scorers trigger automated follow-up sequences.
```

---

## Page Templates

### Template 1: Category Explainer

**Goal:** Define the category, tie it to your product, earn citations.

```markdown
# What is [Category]? — [1-2 line value promise]

## What is [Category]? (~80 words)
[Plain definition in everyday language. Name adjacent entities.]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## Why it matters now (~60 words)
[One paragraph. Mention shift to answers over links; tie to buyer outcomes.]

## How to apply it (3-5 bullets)
- [Action 1]
- [Action 2]
- [Action 3]

## FAQ
**Q: [Question]?**
A: [~1 sentence answer]

**Q: [Question]?**
A: [~1 sentence answer]

**Q: [Question]?**
A: [~1 sentence answer]

---
**Links:** [Category hub] | [Product/Feature] | [Credible source 1] | [Credible source 2]
**CTA:** [Demo / Template / Signup]
**Schema:** Article + FAQ. Author + last updated.
```

---

### Template 2: Product & Feature Page

**Goal:** Clarify capability, fit, and next step; reinforce category linkage.

```markdown
# [Product/Feature] — [Outcome in 3-5 words]

**[Product/Feature] enables [Outcome] for [User/Role].**

## [Feature Area 1]
[2-4 sentences using Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## [Feature Area 2]
[2-4 sentences using Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## [Feature Area 3]
[2-4 sentences using Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## FAQ
**Q: [Question]?**
A: [~1 sentence]

**Q: [Question]?**
A: [~1 sentence]

**Q: [Question]?**
A: [~1 sentence]

---
**Links:** Back to [Category Explainer] | Forward to [Demo/Trial]
**Proof:** [Benchmark/Analyst/Customer proof]
**Notes:** Requirements/limits (pricing tier, integrations)
**Schema:** Article + FAQ. Author + last updated.
```

---

### Template 3: Comparison / Alternatives Page

**Goal:** Help readers decide with clear criteria; earn fair citations.

```markdown
# [Product] vs. [Alternative] — Which fits [Use case]?

## Comparison Table

| Criterion | [Product] | [Alt A] | [Alt B] | Source |
|-----------|-----------|---------|---------|--------|
| [Feature/Limit] | [value] | [value] | [value] | [link] |
| [Requirement] | [value] | [value] | [value] | [link] |
| [Best for] | [value] | [value] | [value] | [link] |

*Source-back all claims in the table or footnotes.*

## Fit Statements

1. **[Product]** suits [Team/Use case] when [Condition].
2. **[Alt A]** fits [Team/Use case] when [Condition].
3. **[Alt B]** works for [Team/Use case] when [Condition].

---
**Links:** [Category Explainer] | [Feature pages]
**CTA:** [Try / Demo / Talk to Sales]
**Schema:** Article. Author + last updated.
```

---

### Template 4: Use Case / Industry Page

**Goal:** Connect product to outcomes in a context readers recognize.

```markdown
# [Industry/Use Case] — [Outcome KPI]

**Teams reduce [Metric] by [Y%] in [Timeframe].**

## Mini Case Study
[Company/Role] used [Product/Feature] to [Action], resulting in
[Metric improvement] within [Timeframe].

## How It Works

### [Feature 1]
[Feature → How → Outcome paragraph]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

### [Feature 2]
[Feature → How → Outcome paragraph]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## Who Uses This
**Roles:** [Role 1], [Role 2], [Role 3]
**Workflows:** [Workflow 1], [Workflow 2]
**Integrations:** [Integration 1], [Integration 2]

---
**Links:** [Product/Feature pages] | [Supporting blog]
**CTA:** [Industry template / Demo variant]
**Schema:** Article. Author + last updated.
```

---

### Template 5: Supporting Blog Post

**Goal:** Add information gain and support your content cluster.

```markdown
# [Topic] — [Specific promise]

## Opening (~60-80 words)
[State the problem. Align terminology with Category Explainer. Preview outcome.]

## [Section 1 Heading] (~120 words max)
[Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

**Internal link:** [Related page]
**External citation:** [Credible source]

## [Section 2 Heading] (~120 words max)
[Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

**Internal link:** [Related page]
**External citation:** [Credible source]

## Key Takeaway
[1-2 lines summarizing the main point]

**CTA:** [Single primary action]

---
**Schema:** Article. Author + last updated.
```

---

## Site-Wide Trust Signals

### Required on Every Page

| Element | Implementation |
|---------|----------------|
| **Schema markup** | Article + FAQ (if FAQ exists) |
| **Author attribution** | Name, bio, credentials, photo |
| **Last updated date** | Visible, machine-readable |
| **Internal links** | 3-5 per page (upstream/downstream) |
| **External citations** | 1-2 credible sources per section |
| **Single CTA** | Demo, template, or signup (repeated once near end) |

### Schema Implementation

```html
<!-- Article Schema -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "[Page Title]",
  "author": {
    "@type": "Person",
    "name": "[Author Name]",
    "url": "[Author Bio URL]"
  },
  "datePublished": "[ISO Date]",
  "dateModified": "[ISO Date]",
  "publisher": {
    "@type": "Organization",
    "name": "[Company]",
    "logo": "[Logo URL]"
  }
}
</script>

<!-- FAQ Schema (if FAQ section exists) -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "[Question 1]",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "[Answer 1]"
      }
    },
    {
      "@type": "Question",
      "name": "[Question 2]",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "[Answer 2]"
      }
    }
  ]
}
</script>
```

---

## Content Cluster Architecture

```
                    ┌─────────────────────┐
                    │  Category Explainer │
                    │   "What is AEO?"    │
                    └──────────┬──────────┘
                               │
        ┌──────────────────────┼──────────────────────┐
        │                      │                      │
        ▼                      ▼                      ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│ Product Page  │    │ Product Page  │    │ Product Page  │
│  "Feature A"  │    │  "Feature B"  │    │  "Feature C"  │
└───────┬───────┘    └───────┬───────┘    └───────┬───────┘
        │                    │                    │
        ▼                    ▼                    ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│  Blog Post    │    │  Use Case     │    │  Comparison   │
│  (supports)   │    │  (industry)   │    │  (vs. alt)    │
└───────────────┘    └───────────────┘    └───────────────┘
```

**Linking Rules:**
- Category Explainer links DOWN to all product pages
- Product pages link UP to Category Explainer
- Product pages link ACROSS to related features
- Blog posts link UP to Product pages
- Comparison pages link to Category Explainer + relevant Product pages

---

## AEO Writing Checklist

### Per-Paragraph Checklist

- [ ] Follows Feature → How → Outcome pattern
- [ ] Contains 2-4 sentences (scannable)
- [ ] Includes 1-2 semantic triples
- [ ] Names specific entities (not vague "it" or "this")
- [ ] Uses active voice verbs

### Per-Section Checklist

- [ ] Has 1 internal link (upstream or downstream)
- [ ] Has 1 external citation (credible source)
- [ ] Section heading names an entity
- [ ] ~120 words max

### Per-Page Checklist

- [ ] H1 contains primary entity + value promise
- [ ] Opening claim is a semantic triple
- [ ] 3-5 internal links total
- [ ] 1-2 external citations total
- [ ] Mini-FAQ with 3 questions (if applicable)
- [ ] Single primary CTA
- [ ] Schema markup (Article + FAQ)
- [ ] Author name + bio link
- [ ] Last updated date visible

### Site-Wide Checklist

- [ ] Category Explainer exists for each key category
- [ ] Product pages link back to Category Explainer
- [ ] Content cluster architecture documented
- [ ] Author bio pages exist with credentials
- [ ] Consistent terminology across all pages

---

## Measuring AEO Success

### Key Metrics

| Metric | How to Track |
|--------|--------------|
| **AI citations** | Manual checks in ChatGPT, Claude, Perplexity |
| **Brand mentions in AI** | Search "[brand] + [category]" in AI engines |
| **Share of answer** | How often you're cited vs competitors |
| **LLM traffic** | GA4 referral from chatgpt.com, claude.ai, perplexity.ai |
| **Impressions-to-clicks gap** | GSC impressions vs actual clicks |

### Tools

- **HubSpot AEO Grader** - Grade your brand's AI visibility
- **Google Analytics 4** - Track LLM referral traffic
- **Google Search Console** - Monitor impressions vs clicks gap
- **Manual AI queries** - Regularly test your brand in AI engines

---

## Common AEO Mistakes

| Mistake | Fix |
|---------|-----|
| Vague language ("it helps with things") | Use specific entities and triples |
| No clear structure | Use Feature → How → Outcome |
| Missing schema | Add Article + FAQ schema |
| No author attribution | Add author name, bio, credentials |
| Generic content | Add original data, examples, POV |
| Orphan pages | Link into content cluster |
| Fence-sitting ("it depends") | Take a clear position |
| No external citations | Add 1-2 credible sources per section |

---

## AEO vs Traditional SEO

| Aspect | Traditional SEO | AEO |
|--------|-----------------|-----|
| **Goal** | Rank on page 1 | Get cited in AI answers |
| **Success metric** | Click-through rate | Share of answer |
| **Content focus** | Keywords | Entities + facts |
| **Structure** | Headers for scanning | Triples for extraction |
| **Links** | Backlinks for authority | Citations for consensus |
| **Updates** | Periodic refresh | Continuous accuracy |

---

## Quick Reference

### Semantic Triple Pattern
```
[Entity/Product] [active verb] [concrete object/result].
```

### Paragraph Pattern
```
[Feature] helps [User] with [Job].
It [mechanism] to [process].
Teams see [result] in [timeframe].
```

### Page Minimums
- 3-5 internal links
- 1-2 external citations per section
- 3 FAQ questions with schema
- Author + last updated
- Single CTA

### Content Hierarchy
1. Category Explainer (top)
2. Product/Feature pages (middle)
3. Use case / Comparison / Blog (supporting)

Overview

This skill packages an opinionated AEO (AI Engine Optimization) playbook to get your brand cited by AI answers. It provides semantic triples, page templates, content-cluster architecture, schema snippets, and checklists to produce quotable, high‑trust content. Use it to design pages that AI engines can parse, trust, and repeat as citations.

How this skill works

The skill inspects content structure and outputs concrete artifacts: semantic triples, Feature→How→Outcome paragraphs, page templates (category, product, comparison, use case, blog), and JSON‑LD schema snippets. It enforces site‑wide linking rules, citation requirements, and per‑page checklists so content builds consensus, delivers information gain, and names clear entities. The result is a repeatable content cluster that improves AI citation share and measurable LLM referrals.

When to use it

  • Launching a new product or category page aimed at getting cited by AI
  • Rewriting existing pages to increase quotability and clarity for LLMs
  • Designing content clusters and internal linking for enterprise sites
  • Creating technical pages where precise facts and entities matter
  • Adding schema and author signals to improve trust with AI overviews

Best practices

  • Write short 2–4 sentence paragraphs following Feature → How → Outcome
  • Embed 1–2 semantic triples per paragraph and use explicit entity names
  • Add 1–2 credible external citations per section and 3–5 internal links per page
  • Include Article + FAQ schema, visible author attribution, and last‑updated date
  • Favor original data, concrete examples, and a clear point of view over generic advice

Example use cases

  • Create a Category Explainer that defines the market and links to product pages so AI cites your definition
  • Produce Product & Feature pages with triples that state capability → mechanism → outcome for sales enablement
  • Build Comparison pages with sourced criterion tables to appear in AI decision answers
  • Publish supporting blog posts with research and internal links to raise consensus across the cluster
  • Assemble industry use‑case pages with mini case studies and measurable KPI outcomes

FAQ

How many semantic triples should a page include?

Aim for 2–5 triples across the opening and major sections; include at least one opening triple in the H1/lead.

What schema is required?

Add Article schema on every content page and FAQ schema when a FAQ exists; include author, datePublished, and dateModified fields.

How do I measure AEO success?

Track AI citations via manual queries (ChatGPT, Claude, Perplexity), monitor LLM referral traffic in GA4, and compare impressions‑to‑clicks in Search Console.