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

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)
├── 05-cited-source-comparison.md     (gap vs cited sources — load-bearing reference)
└── evidence/
    ├── 01-url-keyword-footprint.md   (URL overview + top keywords — raw step output)
    ├── 02-aio-by-keyword.md          (AIO presence + citation per keyword)
    ├── 03-leaderboards.md            (full leaderboards per keyword)
    ├── 06-schema-check.md            (JSON-LD audit for GEO-relevant types — requires Firecrawl)
    └── 07-ai-protocol-files.md       (llms.txt + RSL status and content — requires Firecrawl)

Top-level: GEO.md + 04-page-passages.md + 05-cited-source-comparison.md. The other step files preserve raw API/scrape outputs in evidence/ for reproducibility — editors and writers don't open them in the normal flow.

GEO.md follows this shape:

# GEO Analysis: {URL}

> Snapshot dated {YYYY-MM-DD} · Country: {country} · Keywords analysed: {n}

## Citation footprint

| Keyword | AIO present | Candidate cited | Citers |
|---|---|---|---|
| {keyword 1} | ✓ | ✗ | {3 cited sources} |
| {keyword 2} | ✓ | ✓ | {includes candidate + 2 others} |
| ... |

**Citation rate: {n}/{checked} ({%}) of AIOs where candidate could appear actually cite it.**

## Where the candidate IS cited
- {keyword X} — passage cited: "{passage text}"
- ...

## Where the candidate is NOT cited (and AIO is present)
- {keyword Y} — cited sources tend to share these patterns:
  - {pattern 1: short definitive answer in first 100 words}
  - {pattern 2: numbered stat with date}
  - {pattern 3: schema-marked Article with author bio}
- The candidate is missing: {specific gap}.

## Page passage-level audit

Top-scoring passages on the candidate (by citability score):
1. {passage at H2 "X" — score 8/10. Strong: definitive sentence, named stat. Weak: no date.}
2. ...

Lowest-scoring passages (refresh candidates):
1. {passage at H2 "Y" — score 3/10. Weak: vague generalities, no specific data.}
2. ...

## Schema check
- `Article` (or sub-type) present and valid: {✓/✗ | skipped — Firecrawl required}
- `author` populated with `@type: Person` and `url`: {✓/✗}
- `datePublished` + `dateModified` ISO 8601: {✓/✗}
- `FAQPage` for visible Q&A: {✓/✗/N-A}
- `BreadcrumbList`: {✓/✗}

## AI-protocol files
- `/llms.txt` present: {✓ status 200 / ✗ status {n} / skipped — Firecrawl required}
- `/.well-known/rsl.json` (or `/RSL.txt`) present: {✓ / ✗ / skipped}
- Stance summary: {permissive / restrictive / mixed / unknown — based on declared categories and allow/deny scope}

## Recommendations (top 5 to improve citability)

1. {Specific change — e.g., "A