aeo-optimization

from alinaqi/claude-bootstrap

Opinionated project initialization for Claude Code. Security-first, spec-driven, AI-native.

448 stars37 forksUpdated Jan 20, 2026
npx skills add https://github.com/alinaqi/claude-bootstrap --skill aeo-optimization

SKILL.md

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 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.


...
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