/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`](../../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](../prd-v09-alternatives-pages/SKILL.md), 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](../prd-v09-alternatives-pages/SKILL.md)

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](../prd-v09-alternatives-pages/SKILL.md) 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=AE