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Aeo Optimization

  • 4 installs
  • 706 repo stars
  • Updated July 14, 2026
  • alinaqi/maggy

aeo-optimization is a Claude skill that structures content with semantic triples and templates so AI engines cite the brand in generated answers.

About

aeo-optimization helps optimize content for AI engines like ChatGPT, Claude, Perplexity, and Google AI Overviews so a brand gets cited in AI-generated answers. It applies semantic triples, a Feature-How-Outcome paragraph pattern, and page templates for category explainers and product pages, targeting the AI signals of consensus, information gain, and clear entities. A developer or marketer uses it when structuring content for AI discovery and citations rather than traditional page ranking.

  • Optimizes content for AI engines like ChatGPT, Claude, and AI Overviews
  • Uses semantic triples and a Feature-How-Outcome paragraph pattern
  • Provides category-explainer and product-page templates with FAQ and schema

Aeo Optimization by the numbers

  • 4 all-time installs (skills.sh)
  • Ranked #1,606 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

aeo-optimization capabilities & compatibility

Capabilities
seo · copywriting · marketing
Use cases
seo · copywriting · marketing
Pricing
Free
From the docs

What aeo-optimization says it does

**Purpose:** Optimize content for AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) so your brand gets cited in AI-generated answers.
SKILL.md
**What they are:** Compact facts that AI engines (and humans) can't misread.
SKILL.md
Best answer > Best page ranking.
SKILL.md
npx skills add https://github.com/alinaqi/maggy --skill aeo-optimization

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Listed on Skillselion
Installs4
repo stars706
Last updatedJuly 14, 2026
Repositoryalinaqi/maggy

What it does

Restructure content with semantic triples and templates so AI engines cite it in generated answers.

Who is it for?

Structuring pages so ChatGPT, Perplexity, and AI Overviews cite them as the authoritative source

Skip if: Traditional keyword-rank SEO tactics, since it targets AI answer citations over page ranking

When should I use this skill?

Optimizing content for AI engine discovery and citations

What you get

Content structured to be quoted and cited by AI engines as the authoritative source

  • semantic-triple statements
  • AEO-structured category and product pages with FAQ and schema

By the numbers

  • Three AI answer signals: consensus, information gain, entities
  • Two page templates: category explainer and product/feature page

Files

SKILL.mdMarkdownGitHub ↗

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 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

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.

# 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.

# [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.

# [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.

# [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.

# [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

ElementImplementation
Schema markupArticle + FAQ (if FAQ exists)
Author attributionName, bio, credentials, photo
Last updated dateVisible, machine-readable
Internal links3-5 per page (upstream/downstream)
External citations1-2 credible sources per section
Single CTADemo, template, or signup (repeated once near end)

Schema Implementation

<!-- 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

MetricHow to Track
AI citationsManual checks in ChatGPT, Claude, Perplexity
Brand mentions in AISearch "[brand] + [category]" in AI engines
Share of answerHow often you're cited vs competitors
LLM trafficGA4 referral from chatgpt.com, claude.ai, perplexity.ai
Impressions-to-clicks gapGSC 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

MistakeFix
Vague language ("it helps with things")Use specific entities and triples
No clear structureUse Feature → How → Outcome
Missing schemaAdd Article + FAQ schema
No author attributionAdd author name, bio, credentials
Generic contentAdd original data, examples, POV
Orphan pagesLink into content cluster
Fence-sitting ("it depends")Take a clear position
No external citationsAdd 1-2 credible sources per section

---

AEO vs Traditional SEO

AspectTraditional SEOAEO
GoalRank on page 1Get cited in AI answers
Success metricClick-through rateShare of answer
Content focusKeywordsEntities + facts
StructureHeaders for scanningTriples for extraction
LinksBacklinks for authorityCitations for consensus
UpdatesPeriodic refreshContinuous 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)

Related skills

FAQ

What signals do AI engines use to pick answers?

The skill names three signals: consensus across credible sources, information gain from net-new insight, and clear entities with tidy structure.

What is a semantic triple?

A compact fact in Subject-verb-object form, such as HubSpot CRM syncs contact and company data, that AI engines and humans cannot misread.

Marketing & SEOseocontent

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