prd-v09-aeo-audit

name	prd-v09-aeo-audit
description	Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes. Triggers on requests to audit AI search visibility, improve AEO/GEO, check ChatGPT/Perplexity coverage, or when user asks "do we show up in AI search?", "AEO audit", "generative engine optimization", "AI discoverability", "how does ChatGPT describe us?", "Perplexity ranking". Outputs GTM-AEO-* entries and a Coverage Matrix.
context	fork
allowed-tools	["Read","Write","Edit","Glob","Grep","WebSearch","WebFetch"]
execution_modes	{"default":"standard","supports":["quick","standard","deep"]}

AEO Audit (AI Search Discoverability)

Position in workflow: v0.9 Launch Channels (ORB) → v0.9 AEO Audit → v0.9 Alternatives Pages, Launch Metrics

Execution Mode

Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.

Mode	What this skill produces
quick	5 target queries × 2 AI surfaces (ChatGPT + Perplexity); top 3 gaps with fixes
standard	10–15 queries × 3–4 AI surfaces; full Coverage Matrix; ranked fix backlog
deep	20–30 queries × all major surfaces; per-surface citation analysis; structured-data audit; before/after re-test plan

What This Does

Tests whether AI search engines surface, recommend, and accurately describe the product when a target customer asks a relevant question. AEO (answer-engine optimization) and GEO (generative-engine optimization) are the post-SEO distribution layer — when ChatGPT/Perplexity/AI Overviews answer a buyer's question, the product either is in the answer or isn't.

This is a diagnostic skill. It produces a gap map and a ranked fix backlog. The fixes are executed by prd-v09-alternatives-pages, content updates, and structured-data work — not by this skill.

How It Works

1. Build a query set — From the Positioning best-fit characteristics (jobs to be done, triggers, search intent), generate target queries an actual best-fit buyer would type. Mix high-intent ("best X for Y"), comparison ("X vs Y"), and category ("what is X").
2. Run each query against AI surfaces — At minimum: ChatGPT (free tier — what the median buyer sees), Perplexity, Google AI Overviews. Deep mode adds Claude, Brave Search, Kagi. Save raw responses with timestamps.
3. Score each result on five dimensions:
   • Mentioned? (yes / no)
   • Position in recommendation list (1st, 2nd, not listed)
   • Description accuracy (matches positioning vs. miscategorized vs. wrong)
   • Competitive frame (which alternatives are listed alongside)
   • Citation sources (which URLs/domains the AI cited to build the answer)
4. Identify the gap pattern:
   • Absence gaps — product not mentioned at all
   • Category gaps — mentioned in the wrong category (positioning failure)
   • Citation gaps — answer is built from sources the product doesn't appear in (need to be on those sources)
   • Comparison gaps — competitor wins the comparison query because comparison content doesn't exist on your side
5. Propose ranked fixes — Each fix maps to a specific gap type:
   • Absence → content on best-fit query intent, JSON-LD structured data, citation-source targets
   • Category → positioning content (handoff to Positioning skill)
   • Citation → outreach/contribution to high-citation sources (G2, Reddit, blog posts on cited domains)
   • Comparison → handoff to prd-v09-alternatives-pages

Example

A best-fit buyer types "best CRO tool for early-stage SaaS founders" into ChatGPT. Result:

Surface	Mentioned?	Position	Accuracy	Citations
ChatGPT	No	n/a	n/a	5 sources, none ours
Perplexity	Yes	4th of 5	"an analytics tool" (wrong category)	Sources include our pricing page only
AI Overviews	No	n/a	n/a	G2, Reddit r/SaaS, two competitor blog posts

Gaps identified:

• Absence in ChatGPT — no high-intent landing for this query
• Category miscoding in Perplexity — we're being summarized as "analytics", not "CRO"
• Citation gap — we don't appear in G2 or r/SaaS threads about CRO

Ranked fixes:

1. Publish CRO-anchored guide (prd-v09-alternatives-pages handles competitor variants)
2. Rewrite product schema (JSON-LD) with the Dunford category claim
3. Outreach to G2 (claim profile, request reviews) and post a substantive thread in r/SaaS

What You Get Back

• GTM-AEO-* entries — one per query × surface gap, with the proposed fix and ranking
• Coverage Matrix (single GTM-* with Type=Audit) — full query × surface × status table
• Fix backlog — ranked list with handoff target skills

When to Use It

Trigger	Mode
Pre-launch sanity check (before paid channels activate)	quick
Standard launch wave audit	standard
Quarterly retention/competitive intelligence review	deep
After major positioning change (re-test)	standard
When organic signups stall and paid CAC rises	deep

Do not run before Positioning is complete — without a sharpened category claim, every "miscategorized" result is unfixable.

Consumes

• GTM-* positioning statement + category claim (from v0.9 Positioning) — Defines what "accurate description" looks like; without this, scoring is opinion
• CFD-* competitive alternatives (from v0.2) — Source for comparison-intent queries
• PER-* best-fit characteristics (sharpened by Positioning) — Source for query intent
• GTM-* channel mix (from v0.9 Launch Channels) — AEO is a channel; surfaces tested should match best-fit channel use

Produces

• GTM-AEO-* entries with Type=AEO-Recommendation, one per gap-fix pair
• GTM-* with Type=Audit — the Coverage Matrix
• Fix backlog — handoff list referencing prd-v09-alternatives-pages, Positioning re-run, content production tickets

Confidence guidance (P4): AEO scoring is 3/5 minimum because it's based on observed AI responses, not opinion. Quick mode may produce 2/5 outputs (limited sampling) and must tag them.

Output Template

GTM-AEO-XXX: [Gap Title]
Type: AEO-Recommendation
Status: Open
Priority: [High | Medium | Low]
Owner: [Person / role]

Query: "[the exact query]"
Surface: [ChatGPT | Perplexity | AI Overviews | Claude | Brave | Kagi]
Date observed: [YYYY-MM-DD]

Result summary:
  Mentioned? [yes | no]
  Position: [#]
  Accuracy: [matches positioning | miscategorized | wrong]
  Competitive frame: [list of alternatives shown]
  Citation sources: [URLs/domains used]

Gap type: [Absence | Category | Citation | Comparison]

Proposed fix:
  - [Specific action 1]
  - [Specific action 2]

Handoff: [Target skill or owner — e.g., prd-v09-alternatives-pages, content team]

Re-test: [Date to verify fix]

Linked IDs: GTM-YYY (positioning), CFD-ZZZ (competitor), PER-AAA (best-fit)

GTM-XXX: AEO Coverage Matrix
Type: Audit
Status: Snapshot — [YYYY-MM-DD]

| Query | ChatGPT | Perplexity | AI Overviews | Gap Type |
|-------|---------|------------|--------------|----------|
| ... | ✓ #2 | ✗ | ✗ | Absence (2 surfaces) |
| ... | ✗ | ✓ #4 (miscat) | ✗ | Category + Absence |

Total queries: X
Coverage rate: Y% (mentioned in any surface)
Accurate-description rate: Z% (mentioned AND correctly described)

Linked IDs: All GTM-AEO-* entries above

Anti-Patterns

Pattern	Signal	Fix
Vanity queries	Auditing "best [exact product name]" — you'll always win that one	Use buyer-intent queries the buyer would actually type
No timestamp / no re-test	Results saved but never re-tested	AI surfaces change; re-test every fix within 2 weeks
Surface monoculture	Only testing ChatGPT	Each surface has different model/data; minimum 3 surfaces
Fix all gaps equally	20 gaps, parallel work, no ranking	Rank by query buyer-intent strength × surface adoption
Treating absence as failure	"We're not in any results — game over"	Absence is often the easiest fix (publish high-intent content); category miscoding is the harder one
Skipping citation analysis	Knowing you're absent but not why	Citation sources reveal where you need to appear

Quality Gates

Before proceeding to fix execution:

• At least 5 queries tested (quick) / 10–15 (standard) / 20+ (deep)
• At least 3 AI surfaces sampled (standard+)
• Every gap has a typed classification (Absence / Category / Citation / Comparison)
• Every gap has a proposed fix with a handoff target
• Coverage Matrix exists and is dated
• Fix backlog is ranked

Downstream Connections

Consumer	What it uses	Example
Alternatives Pages	Comparison-gap fixes become alternatives-page targets	"X vs us" comparison gap → SCR-ALT- page
Positioning (re-run)	Category-coding gaps signal positioning weakness	Recurring miscategorization → re-run prd-v09-positioning-dunford
Launch Metrics	Coverage Matrix becomes a KPI baseline	KPI-AEO-coverage%
v1.0 Continuous Discovery	Recurring gap patterns inform discovery questions	"Users keep finding competitor X — why?"

Detailed References

• Sanity team's seo-aeo-best-practices skill (VoltAgent index)
• Princeton AI Search benchmark studies
• (No bundled references/ — AI surfaces change too quickly to canonize)