aeo-system

Answer Engine Optimization — get AI assistants to recommend your brand. Run AEO audits, build Answer Intent Maps, track AI recommendation positions, and maintain a 7-layer AEO infrastructure for any brand or product category.

Batsirai Chada@batsirai

Install

openclaw skills install @batsirai/aeo-system

AEO (Answer Engine Optimization) System

Get AI assistants — ChatGPT, Perplexity, Claude, Gemini — to recommend your brand when people ask purchase-intent questions.

What This Is

AEO is the discipline of optimizing for AI-powered answer engines the same way SEO optimizes for search engines. When someone asks Perplexity "what's the best magnesium supplement for sleep?" — AEO determines whether your brand gets named.

This skill gives an OpenClaw agent the ability to:

Audit a brand's current AEO infrastructure across all 7 layers
Map which brands AI platforms recommend (and in what position) for any category
Track position changes week over week
Build the missing infrastructure (Answer Hub, brand-facts.json, schema, citations)
Maintain the system with a weekly 90-minute protocol

When to Load This Skill

User asks to "run an AEO audit" for a brand or URL
User asks "which brands are being recommended by AI for [category]?"
User asks to "build an Answer Intent Map" for a category
User asks to check a brand's Answer Hub, brand-facts.json, or schema markup
User asks to track AI recommendation positions over time
User asks to run the "weekly AEO maintenance protocol"

The 7-Layer AEO Framework

Layer	Name	What It Is	Priority
1	Answer Intent Map	Spreadsheet of all purchase-intent queries + which brands AI recommends	Foundation
2	Answer Hub	A long-form guide page that answers every key question in your category	High
3	Brand-Facts Page	Human-readable brand facts page (neutral, factual, cite-able)	High
4	brand-facts.json	Machine-readable brand data at `/.well-known/brand-facts.json`	Medium
5	Schema Markup	Product, FAQ, and Organization structured data	Medium
6	Citation Network	Getting listed on the sources AI models actually cite	High
7	GPT Shopping	Google Merchant Center + review feed for AI shopping results	High

Prerequisites

Required:

PERPLEXITY_API_KEY — enables direct API queries (get free at perplexity.ai/settings/api)
Node.js v18+ (for the answer-intent-map.js script)

Optional:

OPENAI_API_KEY — enables ChatGPT query automation
BRAVE_API_KEY — enables web searches for infrastructure checks

Without API keys: The skill runs in "manual-assist" mode — generates the queries, provides a blank log template, and analyzes results you paste in.

Core Workflows

Workflow 1: Full AEO Audit

Trigger: "Run an AEO audit for [brand URL]"

Steps:

Fetch and analyze the brand's website for AEO infrastructure:
- Check for Answer Hub page (/guides/ or similar long-form page)
- Check for Brand-Facts page (/brand-facts)
- Check for machine-readable data (/.well-known/brand-facts.json)
- Audit schema markup on product pages (via Rich Results API or web_fetch)
- Check for a Wikidata entry
- Check Google Merchant Center eligibility signals
Score each of the 7 layers (0–3 scale):
- 0 = Doesn't exist
- 1 = Exists but incomplete or outdated
- 2 = Exists, functional, minor gaps
- 3 = Complete, current, optimized
Generate a gap analysis report with:
- Current score per layer
- Priority order for implementation
- Specific action items for each missing layer

Output: Markdown report saved as aeo-audit-[brand]-[date].md

Workflow 2: Answer Intent Map

Trigger: "Build an Answer Intent Map for [category]" or run scripts/answer-intent-map.js

Steps:

Generate query list from four types:
- Category queries: "best [product] for [use case]" (10–15 queries)
- Comparison queries: "[brand] vs [competitor]" (10 queries)
- Brand queries: "is [brand] worth it" (5 queries)
- Educational queries: "does [ingredient] help with [condition]" (10 queries)
For each query, query available platforms:
- Perplexity API (structured JSON response with citations)
- OpenAI API (text response — brand names extracted by parser)
- Browser fallback for Claude and Gemini
Parse responses to extract:
- Brand names mentioned (position 1, 2, 3)
- Source URLs cited
- Key verbatim quotes
Write results to JSON data file + Markdown summary report

Output: answer-intent-map-[category]-[date].json + .md summary

Run the script:

bash

node scripts/answer-intent-map.js \
  --category "magnesium supplements" \
  --brand "MyBrand" \
  --queries 20

# Or with a config file:
node scripts/answer-intent-map.js --config ./aeo-config.json

Workflow 3: Weekly Maintenance Protocol

Trigger: "Run weekly AEO maintenance" or scheduled cron

Steps:

Load the brand's Answer Intent Map (top 15 priority queries)
Query ChatGPT and Perplexity for each priority query in fresh sessions
Compare results against previous week's log (detect position changes)
Generate maintenance report:
- Position changes (up/down/new competitors)
- New sources being cited this week
- Recommended Answer Hub updates
Check brand-facts.json for stale lastUpdated timestamp
Check Google Merchant Center for disapprovals (via browser if needed)

Output: aeo-weekly-report-[date].md

Use the checklist: templates/weekly-maintenance-checklist.md

Workflow 4: Citation Network Analysis

Trigger: "Analyze AEO citations for [category]"

Steps:

Run 20 category queries via Perplexity API (citations returned directly)
Extract all unique source URLs from responses
Group and count by domain
Identify top 10 most-cited external sources in the category
Generate outreach priority list

Output: Citation analysis report with target sites ranked by citation frequency

Workflow 5: Infrastructure Build

Trigger: "Build AEO infrastructure for [brand]" or "Set up brand-facts.json"

Steps:

Ask for brand details (or load from aeo-config.json)
Generate from templates:
- brand-facts.json → templates/brand-facts.json (fill placeholders)
- Answer Hub page → templates/answer-hub-template.md
- Schema markup snippet (JSON-LD for product pages)
Provide implementation instructions per asset

Configuration

Create aeo-config.json in your working directory:

json

{
  "brandName": "Your Brand Name",
  "brandUrl": "https://yourbrand.com",
  "category": "Magnesium Supplements",
  "priorityQueries": [
    "best magnesium supplement for sleep",
    "best magnesium glycinate supplement",
    "magnesium supplement for anxiety"
  ],
  "competitors": [
    "Competitor Brand A",
    "Competitor Brand B",
    "Competitor Brand C"
  ],
  "answerHubUrl": "https://yourbrand.com/guides/best-magnesium-supplements-2026",
  "brandFactsJsonUrl": "https://yourbrand.com/.well-known/brand-facts.json"
}

Output Files

File	Description
`aeo-audit-[brand]-[date].md`	Infrastructure audit report
`answer-intent-map-[category]-[date].json`	Raw AI query results
`answer-intent-map-[category]-[date].md`	Human-readable competitive summary
`aeo-weekly-report-[date].md`	Weekly maintenance report
`citation-analysis-[category]-[date].md`	Citation network analysis

Usage Examples

text

# Full infrastructure audit
"Run an AEO audit for mybrand.com"

# Build competitive intelligence
"Build an Answer Intent Map for the magnesium supplement category"

# Quick position check
"Check if mybrand.com is being recommended by Perplexity for 'best magnesium for sleep'"

# Weekly maintenance
"Run the weekly AEO maintenance protocol for my brand"

# Citation analysis
"Which sources does Perplexity cite most for collagen supplement recommendations?"

# Generate brand-facts.json
"Generate the brand-facts.json template for [brand details]"

# Scoring review
"Score my AEO infrastructure for mybrand.com on all 7 layers"

Platform Limitations

Platform	Access	Notes
Perplexity	API (structured, reliable)	Returns citations directly
ChatGPT	API via OpenAI	Text parsing required for brand extraction
Claude	Browser required	Generate queries + blank log; agent uses browser
Gemini	Browser required	Generate queries + blank log; agent uses browser

For Claude/Gemini: the skill generates the query list and a blank log template; use the browser tool to collect results.

Rate limits: Perplexity free tier ≈ 20 requests/minute. For 50+ queries, add --delay 3000 to the script.

File Structure

text

aeo-system/
├── SKILL.md                              ← This file
├── README.md                             ← Human-readable overview
├── scripts/
│   └── answer-intent-map.js              ← Core query automation script
└── templates/
    ├── answer-hub-template.md            ← Answer Hub page template
    ├── brand-facts.json                  ← Machine-readable brand data template
    └── weekly-maintenance-checklist.md  ← 90-minute weekly protocol

AEO System v1.0 — February 2026 A product by Carson Jarvis (@CarsonJarvisAI)

aeo-system

Install

AEO (Answer Engine Optimization) System

What This Is

When to Load This Skill

The 7-Layer AEO Framework

Prerequisites

Core Workflows

Workflow 1: Full AEO Audit

Workflow 2: Answer Intent Map

Workflow 3: Weekly Maintenance Protocol

Workflow 4: Citation Network Analysis

Workflow 5: Infrastructure Build

Configuration

Output Files

Usage Examples

Platform Limitations

File Structure

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