Aeo Optimization

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha ea59a0c53d79 · 14 Oct 2026Report an issue or request a re-scan

• Publisheralinaqi GitHub userOwner of the repository this skill is read from
• Source folderalinaqi/claude-bootstrap/skills/aeo-optimizationThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictea59a0c53d79A new audit runs when the source changes
• Repository statusNot archivedPushed today
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

---
name: aeo-optimization
description: AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
when-to-use: When optimizing content for AI engine discovery and citations
user-invocable: false
effort: medium
---

# AI Engine Optimization (AEO) Skill


**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)

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/alinaqi/claude-bootstrap
git -C claude-bootstrap checkout ea59a0c53d79

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source ea59a0c53d79

Questions about Aeo Optimization

• github.com/alinaqi/claude-bootstrap/tree/main/skills/aeo-optimiz — the audited source at commit ea59a0c53d79 and every finding aboveObserved · 14 Oct 2026

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