| name | audit-website-aeo |
| description | Audits a live website for AI-engine discoverability (AEO/GEO). Crawls the site, runs 16 deterministic checks plus a 6-dimension content evaluation, and produces a scored report (A-F) with prioritized fixes. Use to get a baseline before improve-aeo-geo, or to measure progress after changes. |
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:
- Website URL (required) — the live site to audit.
- Crawl depth (optional) — how many pages to crawl. Default 10, max 30.
- 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.
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
- Intelligence score =
average(6 dimension scores) × 20 → rounds each 0-5 to 0-100.
- Final score =
round(0.5 × foundationalScore + 0.5 × intelligenceScore).
- 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
# 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) |
| 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 |
