Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mattgierhart/PRD-driven-context-engineering --skill prd-v09-aeo-auditgit clone --depth 1 https://github.com/mattgierhart/PRD-driven-context-engineeringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mattgierhart/prd-driven-context-engineering/prd-v09-aeo-audit)<a href="https://agentmods.dev/skills/mattgierhart/prd-driven-context-engineering/prd-v09-aeo-audit"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v09-aeo-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mattgierhart/prd-driven-context-engineering/prd-v09-aeo-audit"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v09-aeo-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00118 | $0.02472 |
| Opus 5 | $0.00059 | $0.01236 |
| Sonnet 5 | $0.00024 | $0.00494 |
| Haiku 4.5 | $0.00012 | $0.00247 |
Grade A, and why
prd-v09-aeo-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- 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").
- 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.
- 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)
- 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
- 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 204 lines · 118 tokens per session scan A 163de10bd6ba
prd-v09-aeo-audit is a skill published in the GitHub repository mattgierhart/PRD-driven-context-engineering (182 stars, last pushed 9d ago), licensed MIT. It adds 118 tokens to every session and 2,472 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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