Agent skill

Aeo Optimization

by alinaqi in alinaqi/maggy

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

MITAuto-check passedMarketing & SEO

Install Aeo Optimization

skills CLI
$ npx skills add alinaqi/maggy --skill aeo-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alinaqi/maggy aeo-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/aeo-optimization .claude/skills/aeo-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
aeo-optimization
GitHub stars
707
Token cost
~3.7k tokens
SKILL.md length
817 words
Files
1
Skills in repo
71
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 3 steps: Consensus → Information Gain → Entities & Structure
  • Tasks that involve AI search optimization
  • SKILL.md covers Why AEO Matters Now, How AI Engines Choose Answers, Semantic Triples (Critical for… and Paragraph Pattern (Feature →…, plus 6 more sections
  • Reaches schema.org

What it does

Aeo Optimization is an agent skill from alinaqi/maggy. AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering AI search optimization. It works with OpenAI. The repository describes itself as: What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center. The licence is MIT.

When your agent uses it

  • Tasks that involve AI search optimization

Example prompts

  • “/aeo-optimization”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Consensus
  2. Information Gain
  3. Entities & Structure

What it can do on your machine

Read from SKILL.md and the folder at commit 72a456e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and html).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • schema.org

    Also links to:

    • hubspot.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Aeo Optimization loads about 3.7k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 817 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~27
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alinaqi/maggy at commit 72a456e, republished under its MIT licence (© alinaqi). 817 words, ~3,677 tokens.

Download SKILL.mdSave it as .claude/skills/aeo-optimization/SKILL.md (or your agent's skills folder).
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 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
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
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
Show full SKILL.md (317 more words)Show less
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)

© alinaqi, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/aeo-optimization of alinaqi/maggy.

Open the folder on GitHubat commit 72a456e

Compare with similar skills

Aeo Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Aeo Optimization compared with similar skills
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SEO DataforseoAgriciDaniel/codex-seo7912 repos~4.6kAutomated safety check: PassMIT
Fire Your SEO Agencyleopard627/fire-your-seo-agency707—~1.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Aeo Optimization

What does Aeo Optimization do?

AI Engine Optimization - semantic triples, page templates, content clusters for AI citations. Aeo Optimization is an agent skill from alinaqi/maggy.

When should I use Aeo Optimization?

Aeo Optimization fits situations like: tasks that involve AI search optimization.

How do I install Aeo Optimization in Claude Code?

Run `npx skills add alinaqi/maggy --skill aeo-optimization -a claude-code`. Or copy the skill folder (skills/aeo-optimization in alinaqi/maggy) into .claude/skills/aeo-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Aeo Optimization in Codex?

Run `npx skills add alinaqi/maggy --skill aeo-optimization -a codex`. Or copy the skill folder (skills/aeo-optimization in alinaqi/maggy) into .agents/skills/aeo-optimization in your project. Codex loads it when a task matches its description.

Can I use Aeo Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alinaqi/maggy --skill aeo-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aeo-optimization, .gemini/skills/aeo-optimization, .github/skills/aeo-optimization and .opencode/skills/aeo-optimization in your project.

What does Aeo Optimization need to run?

SKILL.md names no scripts, command-line tools or credentials: Aeo Optimization is instructions for the agent only.

Does Aeo Optimization access the network?

SKILL.md names 2 domains. In commands or code: schema.org; the agent is likely to contact it when it follows the instructions. As links in the text: hubspot.com. This is read from the text; nothing was executed.

Is Aeo Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Aeo Optimization use?

Aeo Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Aeo Optimization use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Aeo Optimization?

Skills that share tags, products or a category with Aeo Optimization: Geo Fundamentals (wasp-lang/wasp, 19k stars), SEO Geo (ReScienceLab/opc-skills, 1.8k stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars) and SEO Dataforseo (AgriciDaniel/codex-seo, 791 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aeo Optimization?

alinaqi (a GitHub user) maintains it in alinaqi/maggy, which has 707 GitHub stars. The repository holds 71 skills in this directory. The repository was last updated on September 24, 2026.

Source: alinaqi/maggy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.