aeo-optimization
Skill708 starsChanged 5 months agoMITView on GitHub
AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
2 other projects use a version of this.
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- alinaqi/maggyword for word, first seen Jun 12, 2026
- iltutishrak/claude-skillstheir own version, first seen May 26, 2026
Other versions2 projects use a version of this.
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- alinaqi/maggy★ 711100% same
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Wrote their own version · 1
- iltutishrak/claude-skills★ 0same name
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We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
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- Common
- Distinctive
- Very specific
- Distinctive: line 55
Use same terminology as industry leaders
- Distinctive: line 57
Create internal content clusters that reinforce each other
- Distinctive: line 75
Use semantic triples (Subject → Verb → Object)
- Distinctive: line 113
Every substantive paragraph should follow this structure:
- Distinctive: line 306
Goal: Add information gain and support your content cluster.
- Distinctive: line 555
Use case / Comparison / Blog (supporting)
≈ “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.
Files
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
| 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
<!-- 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
- Category Explainer (top)
- Product/Feature pages (middle)
- Use case / Comparison / Blog (supporting)
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