Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes.

MITAuto-check passedMarketing & SEO

Install Prd V09 Aeo Audit

skills CLI
$ npx skills add mattgierhart/PRD-driven-context-engineering --skill prd-v09-aeo-audit -a claude-code

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

GitHub CLI
$ gh skill install mattgierhart/PRD-driven-context-engineering prd-v09-aeo-audit --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/mattgierhart/PRD-driven-context-engineering.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/prd-v09-aeo-audit .claude/skills/prd-v09-aeo-audit && 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
prd-v09-aeo-audit
GitHub stars
180
Token cost
~2.4k tokens
SKILL.md length
956 words
Files
1
Skills in repo
45
Repo updated
First seen
Licence
MIT

At a glance

Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes.

  • Works in 5 steps: Build a query set — From the Positioning… → Run each query against AI surfaces — At… → Score each result on five dimensions → …
  • Requests to audit AI search visibility
  • SKILL.md covers Execution Mode, What This Does, How It Works and Example, plus 9 more sections
  • Calls claude

What it does

Prd V09 Aeo Audit is an agent skill from mattgierhart/PRD-driven-context-engineering. 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.

Its SKILL.md is about 2.4k 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 and Perplexity. The repository describes itself as: PRD-Led Context Engineering — Memory as Infrastructure. An ontology layer for product teams building products that solve real problems — with AI agents that remember. Gated PRD… The licence is MIT.

When your agent uses it

  • Requests to audit AI search visibility
  • Improve AEO/GEO
  • Check ChatGPT/Perplexity coverage
  • User asks do we show up in AI search?

Example prompts

  • “do we show up in AI search?”
  • “AEO audit”
  • “generative engine optimization”
  • “/prd-v09-aeo-audit”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, WebSearch, WebFetch

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Build a query set — From the Positioning best-fit characteristics (jobs to be done, triggers, search intent), generate target queries an…
  2. Run each query against AI surfaces — At minimum: ChatGPT (free tier — what the median buyer sees), Perplexity, Google AI Overviews. Deep…
  3. Score each result on five dimensions
  4. Identify the gap pattern
  5. Propose ranked fixes — Each fix maps to a specific gap type

What it can do on your machine

Read from SKILL.md and the folder at commit 30ed1b0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • WebSearch
    • WebFetch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • claude

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

  • Network

    No URLs in SKILL.md.

    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

Prd V09 Aeo Audit loads about 2.4k tokens when it runs. Until then it costs about 120 tokens; SKILL.md has 956 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~120
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

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 mattgierhart/PRD-driven-context-engineering at commit 30ed1b0, republished under its MIT licence (© mattgierhart). 956 words, ~2,444 tokens.

Download SKILL.mdSave it as .claude/skills/prd-v09-aeo-audit/SKILL.md (or your agent's skills folder).
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.
allowed-tools
Read, Write, Edit, Glob, Grep, WebSearch, WebFetch
context
fork
execution_modes.default
standard
execution_modes.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.

ModeWhat this skill produces
quick5 target queries × 2 AI surfaces (ChatGPT + Perplexity); top 3 gaps with fixes
standard10–15 queries × 3–4 AI surfaces; full Coverage Matrix; ranked fix backlog
deep20–30 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:

SurfaceMentioned?PositionAccuracyCitations
ChatGPTNon/an/a5 sources, none ours
PerplexityYes4th of 5"an analytics tool" (wrong category)Sources include our pricing page only
AI OverviewsNon/an/aG2, 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

TriggerMode
Pre-launch sanity check (before paid channels activate)quick
Standard launch wave auditstandard
Quarterly retention/competitive intelligence reviewdeep
After major positioning change (re-test)standard
When organic signups stall and paid CAC risesdeep

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

Show full SKILL.md (362 more words)Show less

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

PatternSignalFix
Vanity queriesAuditing "best [exact product name]" — you'll always win that oneUse buyer-intent queries the buyer would actually type
No timestamp / no re-testResults saved but never re-testedAI surfaces change; re-test every fix within 2 weeks
Surface monocultureOnly testing ChatGPTEach surface has different model/data; minimum 3 surfaces
Fix all gaps equally20 gaps, parallel work, no rankingRank 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 analysisKnowing you're absent but not whyCitation 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

ConsumerWhat it usesExample
Alternatives PagesComparison-gap fixes become alternatives-page targets"X vs us" comparison gap → SCR-ALT- page
Positioning (re-run)Category-coding gaps signal positioning weaknessRecurring miscategorization → re-run prd-v09-positioning-dunford
Launch MetricsCoverage Matrix becomes a KPI baselineKPI-AEO-coverage%
v1.0 Continuous DiscoveryRecurring 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)

© mattgierhart, 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 .claude/skills/prd-v09-aeo-audit of mattgierhart/PRD-driven-context-engineering.

Open the folder on GitHubat commit 30ed1b0

Compare with similar skills

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Categories

Questions about Prd V09 Aeo Audit

What does Prd V09 Aeo Audit do?

Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes. Prd V09 Aeo Audit is an agent skill from mattgierhart/PRD-driven-context-engineering. Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes.

When should I use Prd V09 Aeo Audit?

Prd V09 Aeo Audit fits situations like: requests to audit AI search visibility; improve AEO/GEO; check ChatGPT/Perplexity coverage; user asks do we show up in AI search?.

How do I install Prd V09 Aeo Audit in Claude Code?

Run `npx skills add mattgierhart/PRD-driven-context-engineering --skill prd-v09-aeo-audit -a claude-code`. Or copy the skill folder (.claude/skills/prd-v09-aeo-audit in mattgierhart/PRD-driven-context-engineering) into .claude/skills/prd-v09-aeo-audit in your project. Claude Code loads it when a task matches its description.

How do I install Prd V09 Aeo Audit in Codex?

Run `npx skills add mattgierhart/PRD-driven-context-engineering --skill prd-v09-aeo-audit -a codex`. Or copy the skill folder (.claude/skills/prd-v09-aeo-audit in mattgierhart/PRD-driven-context-engineering) into .agents/skills/prd-v09-aeo-audit in your project. Codex loads it when a task matches its description.

Can I use Prd V09 Aeo Audit 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 mattgierhart/PRD-driven-context-engineering --skill prd-v09-aeo-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prd-v09-aeo-audit, .gemini/skills/prd-v09-aeo-audit, .github/skills/prd-v09-aeo-audit and .opencode/skills/prd-v09-aeo-audit in your project.

What does Prd V09 Aeo Audit need to run?

Going by SKILL.md and its folder, Prd V09 Aeo Audit needs the command-line tools its instructions call (claude). Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, WebSearch, WebFetch.

Does Prd V09 Aeo Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prd V09 Aeo Audit 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 Prd V09 Aeo Audit use?

Prd V09 Aeo Audit 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 Prd V09 Aeo Audit use?

About 2.4k tokens (SKILL.md is roughly 9.8k 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 Prd V09 Aeo Audit?

Skills that share tags, products or a category with Prd V09 Aeo Audit: 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 Qiaomu SEO (joeseesun/qiaomu-seo, 441 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prd V09 Aeo Audit?

mattgierhart (a GitHub user) maintains it in mattgierhart/PRD-driven-context-engineering, which has 180 GitHub stars. The repository holds 45 skills in this directory. The repository was last updated on August 31, 2026.

Source: mattgierhart/PRD-driven-context-engineering on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.