---
name: "Audit website AEO/GEO skill"
repo: https://github.com/onvoyage-ai/gtm-engineer-skills
source: https://github.com/onvoyage-ai/gtm-engineer-skills/blob/HEAD/audit-website-aeo/SKILL.md
license: "MIT"
install: "npx skills add onvoyage-ai/gtm-engineer-skills --skill audit-website-aeo"
page: https://100xlab.com/resources/agent-skills/onvoyage-ai-gtm-engineer-skills--audit-website-aeo--10y37kx
---

# Audit Website AEO/GEO Skill

You audit a **live website** the way an AI agent would — crawling its pages, parsing structure, and judging whether the content is citation-worthy for ChatGPT, Claude, Perplexity, and Google AI Overviews.

The audit has two halves:

- **Foundational (50%)** — 16 deterministic pass/fail checks run by a script. Reproducible, no judgment.
- **Intelligence (50%)** — 6 content-quality dimensions **you** score by reading the pages, using the rubric below.

Final score = `0.5 × foundational + 0.5 × intelligence`, mapped to an A-F grade.

This skill produces a **diagnosis**. To then fix a codebase, hand off to the **`improve-aeo-geo`** skill.

---

## Workflow

Follow this sequence exactly.

### Step 1: Get inputs

Ask the user for:

1. **Website URL** (required) — the live site to audit.
2. **Crawl depth** (optional) — how many pages to crawl. Default 10, max 30.
3. **Output location** (optional) — where to save the report. Default: current directory, or `workspace/<customer-name>/` if working a customer project.

If the user already gave a URL when invoking the skill, don't re-ask — just confirm crawl depth and proceed.

### Step 2: Run the deterministic audit

Run the bundled script from this skill's `scripts/` directory. It requires only Node 18+ — no `npm install`.

```bash
node <skill-path>/scripts/aeo-audit.mjs <url> --max-pages=10 --out=<output-dir>/aeo-audit.json
```

The script crawls (sitemap + robots.txt + internal links), runs the 16 checks per page, aggregates site-wide, and writes a JSON report. It also prints a human-readable summary. Tell the user the foundational score and the failed checks.

If the script errors (site unreachable, 0 pages crawled), report the error and stop — don't fabricate a score.

### Step 3: Read the JSON report

Read the `aeo-audit.json` file. The key fields:

- `scoring.foundationalScore` — the deterministic score (0-100). This is **final** — do not change it.
- `checks` — the 16 site-wide checks with pass/fail and details.
- `pagesForReview` — up to 5 representative pages (home + richest content pages), each with an `aiView` object containing `title`, `metaDescription`, `h1`, `headings`, `schemaTypes`, `jsonLdSummary`, `textExcerpt`, `internalLinkCount`, `author`, `publishedDate`, `modifiedDate`. **Use these for Step 4.**
- `prioritizedFixes`, `worstPages`, `coverage`, `heuristicIntelligenceSignals` — supporting context. The heuristic signals are a deterministic *prior* — a sanity check, not the real evaluation.

### Step 4: Score the 6 intelligence dimensions

You are an AI agent that just found this site via web search. A user asked you a question and you landed here. **Decide: would you cite this site in your answer?**

Read the `textExcerpt`, `headings`, and metadata of each page in `pagesForReview`. Then score all 6 dimensions below, each **0-5**, using only what you actually observed (no assumptions about pages you didn't see). Write the rationale **before** the score.

#### Rubric (0-5 each)

**Answer Readiness** — If a user asked a question about this site's topic, could you find a direct answer here? The #1 factor — content answering questions in the first paragraph gets 4.8x more citations.
- 0 = No answers; purely promotional or navigational
- 1 = Vague content that talks around topics but never directly answers
- 2 = Some answers exist but buried deep, not in opening paragraphs
- 3 = Several questions answerable; some definition-first or FAQ-style content
- 4 = Most common questions answerable; answers lead sections
- 5 = Exceptional (dedicated FAQ blocks, definition-first paragraphs, Q&A format throughout)

**Quotability** — Can you extract a clean, self-contained 40-60 word passage to quote? Comparison tables get 2.8x citations; FAQ blocks +156%.
- 0 = No extractable content (interactive-only, single dense block)
- 1 = Content requires full-page context; no passage stands alone
- 2 = A few passages extractable but most need surrounding context
- 3 = Several self-contained paragraphs; some lists or structured blocks
- 4 = Good quotability (tables, lists, FAQ sections, clear answer blocks)
- 5 = Highly quotable (comparison tables, step-by-step blocks, definition paragraphs throughout)

**Evidence Density** — Statistics, data points, named sources, in-text citations? Adding in-text citations = +115% visibility; statistics = +40% citation rate.
- 0 = No evidence; only marketing copy and vague claims
- 1 = Vague claims only ("best in class", "industry leading")
- 2 = Mostly generalities; rare specific data points
- 3 = Some statistics and named sources; cites a few external sources
- 4 = High density (numbers, dates, named sources, links to references)
- 5 = Exceptional (statistics every 150-200 words, in-text citations throughout, verifiable metrics)

**Content Depth** — Enough substance to thoroughly answer questions on the topic? Long-form (2000+ words) gets 3x more citations.
- 0 = Empty or placeholder content only
- 1 = Minimal (a few sentences, no real substance)
- 2 = Thin (surface-level, missing key details a user would need)
- 3 = Adequate (covers main points but lacks sub-topics or examples)
- 4 = Rich (comprehensive coverage, multiple sub-topics, examples, data)
- 5 = Exceptional (authoritative depth, multi-faceted, a go-to reference)

**Freshness** — Current enough to cite confidently? 76% of ChatGPT's most-cited pages were updated in the last 30 days.
- 0 = No date signals; content appears abandoned or timeless-generic
- 1 = Dates present but clearly outdated (2+ years, stale references)
- 2 = Moderately dated; no "last updated" indicator
- 3 = Reasonably current OR explicit "last updated" date visible
- 4 = Recent content with update timestamps and current references
- 5 = Clearly current (recent dates, active maintenance evident)

**Structural Clarity** — Does the HTML parse cleanly into readable text? A prerequisite — clean heading hierarchy = 3.2x more citations.
- 0 = Unreadable (no text, blocked, non-semantic markup)
- 1 = Very poor (walls of text, no headings, topic unclear)
- 2 = Weak (some structure but confusing or inconsistent headings)
- 3 = Adequate (clear headings and paragraphs, topic identifiable)
- 4 = Good (clean H1-H2-H3 hierarchy, scannable, purpose obvious)
- 5 = Excellent (perfect heading outline, semantic HTML, zero noise)

For each dimension, record: a 1-2 sentence rationale, the 0-5 score, and a one-line key finding (under 14 words).

### Step 5: Compute the final score

1. **Intelligence score** = `average(6 dimension scores) × 20` → rounds each 0-5 to 0-100.
2. **Final score** = `round(0.5 × foundationalScore + 0.5 × intelligenceScore)`.
3. **Grade** from the final score:

| Grade | Range | Grade | Range | Grade | Range |
|---|---|---|---|---|---|
| A+ | 95-100 | B+ | 80-84 | C | 60-64 |
| A | 90-94 | B | 75-79 | C- | 55-59 |
| A- | 85-89 | B- | 70-74 | D | 40-54 |
| | | C+ | 65-69 | F | below 40 |

Sanity-check your intelligence score against `heuristicIntelligenceSignals` in the JSON. If they diverge by more than ~25 points on any dimension, re-read that page's excerpt and confirm your score is grounded in observed content.

### Step 6: Write the audit report

Write a Markdown report to `<output-dir>/aeo_audit_report.md` using the format in **Report Format** below. Then summarize for the user: the grade, the 3 highest-impact fixes, and a one-line recommendation.

### Step 7: Hand off

If the user wants to act on the findings:
- **To fix a codebase** → recommend the **`improve-aeo-geo`** skill, passing this report as input.
- **To re-measure after fixes** → re-run this skill on the same URL and compare scores.

---

## The 16 deterministic checks

Run by the script. For reference (id — what it verifies — points):

| Check | Verifies | Pts |
|---|---|---|
| `title` | `<title>` present, 10+ chars | 10 |
| `meta-description` | Meta description present, 50+ chars | 10 |
| `canonical` | `<link rel="canonical">` present | 8 |
| `h1` | Exactly one `<h1>` | 8 |
| `schema` | At least 1 JSON-LD block | 8 |
| `schema-types` | A recognized schema.org `@type` is used | 8 |
| `og` | `og:title` and `og:description` present | 8 |
| `internal-links` | 5+ internal links | 10 |
| `image-alt` | 80%+ of images have alt text | 8 |
| `text-depth` | 250+ words of body text | 12 |
| `indexability` | No `noindex` directive | 10 |
| `ai-meta-tags` | No `nosnippet` / `noai` / `noimageai` | 6 |
| `heading-hierarchy` | 2+ heading levels, no skipped levels | 6 |
| `llms-txt` | Valid `llms.txt` (heading + links + 100+ chars) | 10 |
| `ai-bot-access` | robots.txt does not block 9 major AI crawlers | 12 |
| `rss-feed` | RSS or Atom feed discoverable | 8 |

A site-wide check passes when **80%+ of crawled pages** pass it (the script handles aggregation). Foundational score = earned points ÷ 142 × 100.

---

## Report Format

```markdown
# AEO/GEO Audit — [domain]

**Audited:** [date] · **Pages crawled:** [N]

## Score

| | Score | |
|---|---|---|
| Foundational (16 checks) | XX/100 | |
| Intelligence (6 dimensions) | XX/100 | |
| **Final** | **XX/100** | **Grade: X** |

[One-sentence verdict on AI-citation readiness.]

## Foundational Checks

[Table of the 16 checks: ✓/✗, label, detail. Group failures at the top.]

## Intelligence Evaluation

For each of the 6 dimensions: score (X/5 → XX/100), rationale, key finding.

## Prioritized Fixes

Numbered list, highest impact first. For each: what to change, why it matters,
impact/effort. Pull from `prioritizedFixes` and your dimension findings.

## Weakest Pages

[From `worstPages` — URL and per-page %.]

## Recommendation

[2-3 sentences: biggest opportunity, and whether to run improve-aeo-geo next.]
```

---

## Rules

- **Never fabricate the crawl.** Always run the script. If it fails, report the failure — don't invent pages or scores.
- **The foundational score is the script's output.** Don't recompute or adjust it.
- **Score intelligence only from observed content.** Base every dimension score on `textExcerpt` / `headings` / metadata in `pagesForReview`. No assumptions about unseen pages.
- **Rationale before score.** Write why, then the number — for every dimension.
- **One report file**, saved to the output directory. Don't scatter partial outputs.
- This skill **diagnoses**; it does not edit code. Code fixes are the job of `improve-aeo-geo`.

---

## Research References

All statistics above are from verifiable primary research:

| Claim | Source |
|---|---|
| Quotations = +41% visibility; Statistics = +33%; Cite Sources = +28%; in-text citations = +115% for lower-ranked sites | Aggarwal et al., "GEO: Generative Engine Optimization," KDD 2024 ([arXiv](https://arxiv.org/abs/2311.09735)) |
| 44.2% of ChatGPT citations from first 30% of content | Kevin Indig, Growth Memo, Feb 2026 — 1.2M AI answers |
| Comparison tables 2.8x citations; FAQ blocks +156% | AirOps, 2025 — structuring content for LLMs |
| Clean heading hierarchy = 3.2x more citations vs unstructured | AirOps, 2025 |
| 76% of ChatGPT's most-cited pages updated within 30 days; AI cites content 25.7% fresher than organic | Ahrefs, 2025 — 17M citations across 7 AI platforms |
| Long-form (2000+ words) gets 3x more citations | SE Ranking, Nov 2025 — 2.3M pages, 295K domains |

---

## Supporting file: examples/anthropic-com-audit.md

# AEO/GEO Audit — anthropic.com

**Audited:** 2026-05-16 · **Pages crawled:** 6

> Example output produced by `audit-website-aeo`. Real crawl data; the
> intelligence scores below were assigned by reading the page excerpts.

## Score

| | Score | |
|---|---|---|
| Foundational (16 checks) | 72/100 | |
| Intelligence (6 dimensions) | 63/100 | |
| **Final** | **68/100** | **Grade: C+** |

The site is technically clean and well-written, but missing structured-data
and freshness signals keep it from being an easy, confident citation source.

## Foundational Checks

| | Check | Detail |
|---|---|---|
| ✗ | Structured data present | 2/6 pages passed |
| ✗ | Schema types identified | 2/6 pages passed |
| ✗ | Content structure | 2/6 pages passed (heading levels skipped/single-level) |
| ✗ | llms.txt valid | No llms.txt found |
| ✗ | RSS/Atom feed | No RSS/Atom feed found |
| ✓ | Clear page title | 6/6 |
| ✓ | Meta description | 6/6 |
| ✓ | Canonical URL | 6/6 |
| ✓ | Single H1 heading | 6/6 |
| ✓ | Open Graph basics | 6/6 |
| ✓ | Internal linking | 6/6 |
| ✓ | Image alt coverage | 5/6 |
| ✓ | Readable content depth | 6/6 |
| ✓ | Indexable for discovery | 6/6 |
| ✓ | AI-accessible meta tags | 6/6 |
| ✓ | AI bot access | No AI bots blocked in robots.txt |

## Intelligence Evaluation

**Answer Readiness — 3/5 (60/100).** Product and guidance pages lead with clean
definitions ("Anthropic Interviewer is a research tool, powered by Claude,
that…"); the homepage is promotional and navigational. *Several questions
answerable, but no FAQ-first structure across the site.*

**Quotability — 4/5 (80/100).** Self-contained passages and concrete metrics
("Claude Haiku 4.5 scores 73.3% on SWE-bench Verified") are easy to extract;
two pages carry FAQPage structure. *Strong — clear answer blocks and lists.*

**Evidence Density — 3/5 (60/100).** Product pages cite hard numbers and
sample sizes ("1,250 professionals", "81,000 people"); value/careers pages run
on qualitative copy. No author attribution anywhere. *Uneven — strong on
product pages, thin elsewhere.*

**Content Depth — 3/5 (60/100).** Every page clears the depth threshold, but
the crawl surfaced only home + miscellaneous pages — no docs or blog corpus.
*Adequate; lacks a deep knowledge section.*

**Freshness — 3/5 (60/100).** Some pages show visible recent dates ("Last
updated Jul 10, 2025"), but these are inline text, not machine-readable
`article:modified_time` meta tags — and there is no RSS feed. *Current content,
weak machine-readable freshness signals.*

**Structural Clarity — 3/5 (60/100).** Single H1 and clean titles throughout,
but heading hierarchy passes only 2/6 pages and the homepage extract is noisy
with repeated nav text. *Readable, but the outline does not chunk cleanly.*

## Prioritized Fixes

1. **Add JSON-LD structured data to all key templates** *(High impact / Medium
   effort)* — only 2/6 pages carry any schema. Add `Organization` + `WebSite`
   site-wide, `Article` to content pages, `Product`/`SoftwareApplication` to
   model pages. Agents rely on this to classify entities.
2. **Publish a valid `llms.txt`** *(Medium / Low)* — none exists. A heading +
   curated links gives AI systems a trusted index of the site.
3. **Emit machine-readable date meta tags** *(Medium / Low)* — convert visible
   "Last updated" dates into `article:published_time` /
   `article:modified_time`. Freshness is a top citation driver.
4. **Fix heading hierarchy** *(Medium / Low)* — 4/6 pages skip levels or use
   one. Enforce H1 → H2 → H3 so passages chunk cleanly.
5. **Add an RSS/Atom feed** *(Medium / Low)* — helps AI systems discover new
   announcements and releases.

## Weakest Pages

| Score | URL |
|---|---|
| 73% | https://www.anthropic.com/ |
| 80% | https://www.anthropic.com/about-anthropic-interviewer |
| 86% | https://www.anthropic.com/candidate-ai-guidance |

## Recommendation

The biggest, lowest-effort win is structured data — it fails on two-thirds of
pages and is a hard prerequisite for AI classification. Combined with `llms.txt`
and machine-readable dates, these fixes would lift the foundational score into
the 80s. Run `improve-aeo-geo` against the site's codebase to apply them, then
re-run this audit to confirm the delta.

---

## Supporting file: README.md

# Audit Website AEO/GEO

An agent skill that audits a **live website** for AI-engine discoverability — the way ChatGPT, Claude, Perplexity, and Google AI Overviews actually see it.

It crawls the site, runs **16 deterministic checks**, then evaluates **6 content-quality dimensions** and produces a single A-F score with prioritized fixes.

This skill *diagnoses*. To then fix a codebase, hand the report to [`improve-aeo-geo`](../improve-aeo-geo/).

---

## Install

Clone the repo, then symlink or copy `audit-website-aeo/` into `~/.codex/skills/` or `~/.claude/skills/`. See the root [README](../README.md) for examples.

Requires **Node 18+** for the crawler script (uses the built-in `fetch`). No `npm install` — the script is zero-dependency.

## Usage

```
/audit-website-aeo
```

Or:
> "Audit my website for AEO"
> "Check if AI engines can cite https://example.com"
> "Run an AEO/GEO audit on [URL]"

The skill asks for a URL and crawl depth, runs the audit, and writes a report.

---

## How it works

1. **Crawl** — Discovers pages via `sitemap.xml`, `robots.txt`, and internal links (up to 30 pages), respecting `robots.txt`.
2. **Deterministic audit** — A Node script runs 16 binary checks per page and aggregates site-wide (a check passes when 80%+ of pages pass it).
3. **Intelligence evaluation** — The agent reads the richest pages and scores 6 dimensions 0-5 using a research-backed rubric.
4. **Scoring** — `Final = 50% foundational + 50% intelligence`, mapped to an A-F grade.
5. **Report** — A Markdown report with the score, failed checks, dimension findings, prioritized fixes, and weakest pages.

### The 16 deterministic checks

Title · meta description · canonical URL · single H1 · structured data (JSON-LD) · recognized schema types · Open Graph · internal linking · image alt coverage · text depth · indexability · AI-accessible meta tags · heading hierarchy · `llms.txt` · AI-bot access in `robots.txt` · RSS/Atom feed.

### The 6 intelligence dimensions

| Dimension | Question it answers |
|---|---|
| Answer Readiness | Can an agent find a direct answer in the opening paragraphs? |
| Quotability | Can a clean 40-60 word passage be extracted to quote? |
| Evidence Density | Are there statistics, data points, and named sources? |
| Content Depth | Is there enough substance to thoroughly answer questions? |
| Freshness | Does the content appear current enough to cite? |
| Structural Clarity | Does the HTML parse cleanly into readable text? |

---

## The crawler script

`scripts/aeo-audit.mjs` is a standalone, zero-dependency Node crawler and checker. The skill runs it for you, but you can also run it directly:

```bash
node scripts/aeo-audit.mjs https://example.com --max-pages=20 --out=aeo-audit.json
```

| Flag | Default | Description |
|---|---|---|
| `--max-pages=N` | `10` | Pages to crawl (max 30) |
| `--out=PATH` | `aeo-audit-report.json` | JSON report path |
| `--no-out` | — | Skip writing the JSON file |
| `--json` | — | Print full JSON to stdout instead of a summary |

It prints a human-readable summary and writes a JSON report containing the foundational score, all 16 checks, prioritized fixes, weakest pages, and `pagesForReview` (the pages the agent reads for the intelligence evaluation).

> **Parsing note:** the script parses HTML with regex/string ops, not a full DOM, so it has no dependencies but is slightly less precise than a browser. Checks measure presence and structure, which tolerates this well.

---

## Where it fits in the workflow

```
audit-website-aeo   →   improve-aeo-geo   →   audit-website-aeo (re-run)
   diagnose              fix the codebase        measure the delta
```

Run it before `improve-aeo-geo` to get a baseline, and again afterward to confirm the score improved.

## License

MIT — see the root [LICENSE](../LICENSE).