SEO + GEO Audit

You audit a live website the way an AI answer engine sees it: from the server-rendered HTML (what curl returns), not the JavaScript-rendered DOM a browser shows. The two differ, and the gap is where most AI visibility silently dies.

The grade has two halves of equal weight:

• Foundational (50%), deterministic pass/warn/fail checks run by a bundled script. Reproducible, no judgment.
• Intelligence (50%), 6 content-quality dimensions you score by reading the pages, using references/INTELLIGENCE-RUBRIC.md.

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

This skill produces a diagnosis. To then apply fixes to a codebase, hand off to the `seo-geo-fix` skill. For the full battle-tested method behind the checks, read ../../references/PLAYBOOK.md.

Workflow (follow in order)

1. Get inputs

• URL (required).
• Crawl depth (optional, default 10, max 30).
• Output dir (optional, default current directory).

If the user already gave a URL, don't re-ask, confirm depth and go.

2. Run the deterministic audit

Requires only Node 18+ (uses global fetch). No npm install.

node <skill-path>/scripts/audit.mjs <url> --max-pages=10 --out=<out-dir>/seo-geo-audit.json

The script crawls (sitemap + robots.txt + internal links), parses each page's raw HTML, runs the checks, scores them weighted, and writes JSON + a printed summary. If it crawls 0 pages it exits non-zero, do not fabricate a score, report the error.

3. Read the JSON report

Key fields:

• scoring.foundationalScore (0-100) and scoring.foundationalGrade, final, do not change.
• scoring.categories, score per category, worst first. The bottleneck at a glance.
• checks, every check with status + detail.
• prioritizedFixes, fails then warns, heaviest weight first.
• pagesForReview, up to 5 richest pages with title, headings, schemaTypes, author,

dates, wordCount, and geoSignals. Use these for step 4.

4. Score the 6 intelligence dimensions

Read references/INTELLIGENCE-RUBRIC.md and score each dimension 0-5 using only what you observed in pagesForReview. Write the rationale before the number. You are an AI agent that just landed on this site from a web search, would you cite it?

Dimensions: Answer Readiness · Quotability · Evidence Density · Content Depth · Structure & Schema · Brand Authority signals.

intelligenceScore = (sum of 6 scores / 30) × 100.

5. Compute the final grade and report

final = round(0.5 × foundationalScore + 0.5 × intelligenceScore)

Grade: A ≥90, B ≥80, C ≥70, D ≥60, E ≥50, else F.

Produce a report with:

1. The grade (final + the foundational/intelligence split).
2. The bottleneck in one sentence (on-site fundamentals vs off-site authority, it is almost

always off-site once fundamentals are clean; see PLAYBOOK.md §1).

1. Prioritized fixes (Critical / High / Medium / Low). Each: what / where (file or URL) / why /

how to verify (a falsifiable curl or GSC check).

1. The off-site note: the on-page score caps fast; the ceiling is brand mentions

(YouTube > Reddit ≈ Wikipedia > LinkedIn > PR). Say so explicitly.

Optionally render assets/report-template.html filled with the JSON for a shareable report.

Optional: chunk-level retrieval simulation

AI engines retrieve at the passage level, not the page level. scripts/chunk-sim.mjs <url> splits the page the way a RAG pipeline would and scores each chunk's standalone citability, flagging blocks that would be useless if retrieved alone (open with a pronoun/connective, no named subject, no concrete signal). Use it to action strategy 1 in ../../references/geo-frontier-strategies.md.

Optional: enrich with live SERP data

The audit is zero-dependency and measures citability/crawlability, not ranking. To add real ranking data, scripts/serp-enrich.mjs calls DataForSEO if DATAFORSEO_LOGIN/DATAFORSEO_PASSWORD are set (it exits cleanly with setup notes if not). Free alternative: Google Search Console. See ../../references/data-providers.md. Keep enrichment separate so the A-F foundational score stays reproducible without any key.

The checks (what the script verifies)

See references/RULES.md for the full catalog with weights. Highlights that catch the silent killers:

• JSON-LD in the server HTML (not injected client-side, the single most common AI-visibility bug).
• Self-referencing canonical + detection of the "everything canonicalizes to homepage" root-layout bug.
• AI crawlers not blocked in robots.txt (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, Google-Extended…).
• One <h1>, host consistency, sitemap, llms.txt, answer-first/TL;DR, question headings, evidence density, freshness, named author.

Reference files (read on demand)

Pull these in when relevant; do not load them all up front.

• references/RULES.md: the full deterministic check catalog with weights and curl verifications.
• references/INTELLIGENCE-RUBRIC.md: the 6-dimension scoring rubric for the intelligence half.
• ../../references/PLAYBOOK.md: the full field method and every gotcha behind the checks.
• ../../references/statistics-2026.md: sourced, dated GEO data and citation-lift multipliers (and what was debunked). Use it to justify recommendations.
• ../../references/ai-crawlers.md: the exact AI crawler user-agents and robots.txt handling, for the ai-bots-allowed finding.
• ../../references/platform-profiles/: how each engine (ChatGPT, Perplexity, Gemini, Claude, AI Overviews) retrieves and cites, so fixes target the right mechanism.
• ../../references/geo-frontier-strategies.md: seven retrieval-mechanic GEO strategies, including the chunk-level approach behind scripts/chunk-sim.mjs.
• ../../references/tactics-spectrum.md: the white/gray/black-hat map; stay white-hat and recognize the rest.
• ../../references/data-providers.md and ../../tools/dataforseo/: optional real SERP/keyword/rank data.

Guardrails when writing the report or content

No em-dashes (commas, colons, parentheses instead). No emojis in client-facing copy. In finance/health/legal, minimize "AI" hype (it reads as a negative signal there). Never expose the client's internal tech/vendors. Measure live with curl, never quote scores from memory.