/seo-geo

name: seo-geo
description: URL-level Generative Engine Optimization (GEO) analysis. For a specific URL, pulls AI Overview citation data scoped to the URL's primary keywords, identifies which AIO queries cite the URL vs which don't but should, and recommends page-level changes that improve LLM citability. Distinct from `seo-ai-search-share-of-voice` (domain-level, brand vs brand) — this is one URL, deeper. Use when the user asks "GEO for this page", "AIO citation analysis", "AI search readiness for URL", "why isn't this page cited", or "improve LLM citations".

> Example output: [examples/seo-geo-notion-share-pages-20260514/GEO.md](../../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`

Output format

Create a folder `seo-geo-{target-slug}-{YYYYMMDD}/` with:

seo-geo-{target-slug}-{YYYYMMDD}/
├── GEO.md                            (synthesised report + recommendations — primary deliverable)
├── 04-page-passages.md               (extracted passages + citability scores — load-bearing reference editors consult)