Claude-skill-registry aeo-audit
Answer Engine Optimization (AEO) audit methodology for LLM visibility. Use when auditing brands for ChatGPT/Gemini mentions, checking LLM citations, analyzing AI search visibility, or when user mentions "AEO", "LLM visibility", "ChatGPT mentions", "Gemini citations", or "AI search optimization".
git clone https://github.com/majiayu000/claude-skill-registry
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/aeo-audit" ~/.claude/skills/majiayu000-claude-skill-registry-aeo-audit && rm -rf "$T"
skills/data/aeo-audit/SKILL.mdAEO Audit Methodology
This skill provides the complete Answer Engine Optimization protocol for auditing and optimizing brand visibility in LLM-powered search (ChatGPT, Gemini, Perplexity, etc.).
CRITICAL: Read Protocol First
BEFORE running ANY audit, you MUST read the AEO Protocol SOP:
Read aeo-protocol-sop.md (key sections): - Lines 1-200: Core methodology - Lines 850-900: First 50 Words Audit (CRITICAL) - Lines 1200-1300: Content gap analysis - Lines 2800-2900: Audit checklist - Lines 3400-3500: Final checklist
Do NOT skip this step. The protocol is the source of truth.
Core Concepts
What is AEO?
Answer Engine Optimization ensures brands appear in LLM-generated answers, not just traditional search results. LLMs cite sources differently than Google - they need:
- Facts repeated across 3+ authoritative sources (triangulation)
- Structured, extractable content
- Clear entity establishment
- Technical accessibility (SSR, proper robots.txt)
The Three Search Backends
| Engine | Backend | How It Works |
|---|---|---|
| ChatGPT | Bing + Memory | 3-layer cache (parametric → memory → live search) |
| Gemini | Google Grounding | Real-time Google Search verification |
| Google AI Overview | Google SERP | Aggregates top organic results |
Audit Process
Step 1: Run Brand Audit
Use
run_brand_audit MCP tool with:
- Brand name
- Product category (be specific: "hair transplant clinic" not "medical")
- Primary competitor (optional)
Step 2: Discovery Query Testing
Test queries people use BEFORE knowing the brand:
- "Best [category] in [location]"
- "Best [category] for [use case]"
- "Top [category] [year]"
- "[problem] solution"
Step 2.5: CRITICAL - Run Key Queries 10 Times Each
LLM responses are non-deterministic. Single tests are unreliable.
For top 2-3 discovery queries, run each 10 times per LLM and calculate consistency:
| Score | Interpretation |
|---|---|
| 9-10/10 | Strong (locked in) |
| 7-8/10 | Good (consistent) |
| 5-6/10 | Weak (inconsistent) |
| 1-4/10 | Poor (rarely mentioned) |
| 0/10 | Invisible (critical) |
A brand at 60% consistency is NOT reliably visible.
Step 2.6: Custom Client Queries
Beyond standard queries, test client-specific "dream queries":
| Query Type | Example |
|---|---|
| Outcome-focused | "[category] if money doesn't matter" |
| Problem-aware | "fix bad [category]" |
| Fear-based | "safest [category]" |
| Lifestyle | "[category] for executives" |
| Attribute-specific | "[category] no scars" |
Ask during intake: "What 3-5 queries do you WANT to own?"
For 0% visibility queries → create dedicated landing page.
Step 3: Competitive Analysis
- Check which competitors appear in LLM responses
- Identify citation sources (what sites are LLMs pulling from?)
- Map competitive tier (don't compare premium to budget)
Step 4: Gap Analysis
For each query where brand is missing:
- What sources ARE being cited?
- Is brand mentioned on those sources?
- What facts are LLMs extracting?
- What content needs to be created?
Step 5: First 50 Words Audit (CRITICAL)
For every key page:
- Fetch page content
- Extract first 50 words of visible body text
- Check for presence of:
- WHO: Brand/entity name, credentials
- WHAT: Core offering/service
- WHERE: Location
- PRICE: Pricing tier or specific numbers
- Score: Pass (3-4) / Partial (2) / Fail (0-1)
- Document specific rewrites needed
Why this matters: LLMs weight early content heavily. Facts not in first 50 words often aren't extracted.
Scoring Framework
| Metric | Weight | Measurement |
|---|---|---|
| ChatGPT Mentions | 30% | Brand appears in X/8 queries |
| Gemini Mentions | 30% | Brand appears in X/8 queries |
| Google AI Overview | 20% | Brand in AI Overview snippets |
| Citation Quality | 20% | Authoritative sources citing brand |
Key Audit Queries (Template)
- Basic recognitionWhat is [brand]?
- DiscoveryBest [category] in [location]
- Comparison[Brand] vs [competitor]
- Reputation[Brand] reviews
- Commercial intent[Brand] pricing
- Use-case discoveryBest [category] for [use case]
- Problem-aware discovery[Problem] specialist [location]
- List inclusionTop [category] [year]
Red Flags in Audits
- ❌ Brand not mentioned in discovery queries (acquisition problem)
- ❌ Competitor mentioned but brand isn't (content gap)
- ❌ Incorrect facts in LLM responses (reputation risk)
- ❌ No citations to brand's own website (authority problem)
- ❌ Only mentioned with competitor comparisons (positioning issue)
Quick Reference
For detailed methodology, see:
- aeo-protocol-sop.md - Full protocol
- fuegenix-aeo-audit.md - Example audit