SEO Geo

Example output: examples/seo-geo-notion-share-pages-20260514/GEO.md

Page-Level GEO (Generative Engine Optimization)

For one URL, surface its AI-search citation footprint and recommend the page-level changes that would improve citability across AI Overview, Perplexity, ChatGPT, and other LLM-powered search engines. Different from the domain-level brand-vs-brand share-of-voice — this is page-level diagnosis.

Prerequisites

• SE Ranking MCP server connected.
• Claude's WebFetch tool available.
• User provides: a target URL. Optional: target country (default us), specific keywords to focus on (defaults: the URL's top-5 traffic-weighted keywords from SE Ranking).

Process

1. Validate target & preflight. See skills/seo-firecrawl/references/preflight.md for the canonical 3-stage preflight (credit balance, Firecrawl availability, Google APIs). Skill-specific notes:

   • Confirm URL is fetchable before continuing.
   • Estimated SE Ranking cost for this skill: ~10–20 credits typical (URL keyword footprint, AIO presence + leaderboard for top 5 keywords).
   • Firecrawl: optional, ~3 Firecrawl credits if available. When available, the JSON-LD parse in step 7 and the AI-protocol-files step 8 use it. Without it, those steps emit (skipped — Firecrawl not installed; install via extensions/firecrawl/install.sh) notes in GEO.md rather than failing the run. Pass --no-firecrawl to skip Firecrawl even when available (saves credits).
   • Google APIs: not used.

2. URL keyword footprint DATA_getUrlOverviewWorldwide and DATA_getDomainKeywords (URL-filtered)

   • Pull URL's overview (keywords, traffic).
   • Pull all keywords the URL ranks for. Sort by traffic-weighted score.
   • Take the top 5 as the GEO investigation set (or use user-supplied keywords).

3. AIO presence per keyword DATA_getAiOverview

   • For each keyword, query AIO presence + citation list.
   • Flag: AIO present? Is the candidate URL cited?
   • Capture the AIO answer text — it tells you what passage shape Google's models prefer.

4. AIO leaderboard per keyword DATA_getAiOverviewLeaderboard

   • Full ranked list of cited sources per AIO query.
   • Identify patterns: domain-level (which sites consistently cited?), passage-level (what structure?).

5. Page passage-level audit WebFetch

   • Pull the page HTML.
   • Identify "passages" — paragraphs that could be extracted standalone (TL;DR boxes, definition paragraphs, summary sentences after H2s).
   • For each passage, score citability:
     • Has it a complete thought in 1–3 sentences?
     • Does it answer a specific question (i.e., the question its parent H2 implies)?
     • Has it a stat / number / named entity?
     • Has it a clear timestamp or freshness signal?
   • This is the citability layer.

6. Compare candidate to cited sources

   • For each AIO query where candidate is NOT cited, identify the cited sources.
   • WebFetch 2–3 of them.
   • Extract the cited passage (often a snippet from the AIO answer).
   • Compare passage shape: candidate vs cited. Surface specific structural / content / freshness gaps.

7. Schema check mcp__firecrawl-mcp__firecrawl_scrape

   • WebFetch in step 5 returned markdown — JSON-LD blocks were stripped before parsing. The schema check requires Firecrawl to recover them.
   • If Firecrawl available: scrape the target URL once (1 Firecrawl credit), parse the returned html for every <script type="application/ld+json"> block. Specifically check for: Article/BlogPosting with valid author + datePublished + dateModified; FAQPage if Q&A blocks present; BreadcrumbList; mainEntityOfPage self-canonical.
   • If Firecrawl unavailable: write Schema check: skipped — Firecrawl required to parse JSON-LD blocks (WebFetch returns markdown only). into evidence/06-schema-check.md, mirror the same line in the GEO.md "Schema check" section. Don't infer from markdown — that's the bug this section closes.
   • Schema isn't a direct citation signal but it correlates strongly with citation rates in Google's AIO.

8. AI-protocol files mcp__firecrawl-mcp__firecrawl_scrape

   • If Firecrawl available: scrape https://{domain}/llms.txt and https://{domain}/.well-known/rsl.json (and the legacy /RSL.txt location as a fallback). Cost: 2 Firecrawl credits (one per file).
   • For each file: capture HTTP status (200 / 404 / other), full body if present, and a parsed summary (declared content categories, allow/deny scope, attribution requirements).
   • Surface in evidence/07-ai-protocol-files.md and in GEO.md as a new "AI-protocol files" section. These signal the domain's stance on LLM training and citation — present-and-permissive correlates with higher AIO citation rates.
   • If Firecrawl unavailable: write AI-protocol files: skipped — Firecrawl not installed. Don't fall back to WebFetch (it would work for plain text but the integration stays uniform; runtime savings are negligible).

9. Synthesise GEO.md