Seo Geo

> 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

• DataForSEO 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 DataForSEO).

Process

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

• Confirm URL is fetchable before continuing.
• DataForSEO bills per call; this run issues ~10–20 calls (URL keyword footprint, AIO presence + leaderboard for top 5 keywords). Use the documented limit/ceiling params to cap.
• 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.

1. URL keyword footprint mcp__dataforseo__dataforseo_labs_google_relevant_pages and mcp__dataforseo__dataforseo_labs_google_ranked_keywords (with target = URL)

• 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).

1. AIO presence per keyword mcp__dataforseo__serp_organic_live_advanced (read the AIO block)

• For each keyword, query the SERP and read the AI Overview block for 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.

1. AIO leaderboard per keyword mcp__dataforseo__serp_organic_live_advanced (AIO citation list) + mcp__dataforseo__ai_opt_llm_ment_top_domains

• From the SERP AIO block, build the full ranked list of cited sources per AIO query; corroborate domain-level citation frequency with mcp__dataforseo__ai_opt_llm_ment_top_domains.
• Identify patterns: domain-level (which sites consistently cited?), passage-level (what structure?).

1. 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.

1. 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.

1. 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 ` 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.

1. 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).

1. 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., "Add a 60-word TL;DR after the H1 that directly answers '{primary keyword}' — current page buries the answer below 800 words of preamble"}
2. {Specific change}
3. {Specific change}
4. {Specific change}
5. {Specific change}

## Recommended next step
Re-run `seo-geo` on this URL in 30 days after applying the recommendations. AIO indexes update on a monthly cadence — citation changes show up there first.

Tips

• DataForSEO allows up to 2,000 calls/min, 30 concurrent; this skill paces sequentially. ~5 keywords × 2 SERP/AIO calls = ~10 calls; plus 2–3 WebFetch on cited sources. Easy.
• Cost: DataForSEO bills per call (~10–20 calls typical), plus ~3 Firecrawl credits when the extension is installed (1 for target-URL JSON-LD, 2 for AI-protocol files). The skill degrades gracefully without Firecrawl — the schema and AI-protocol sections emit explicit "skipped" notes rather than silently dropping.
• Citation isn't ranking. A page can rank well organically and still not be cited in AIO. The opposite happens too — cited pages often rank below their citation rate.
• The biggest GEO levers are usually:

1. Definitive answer in the first 200 words.
2. Specific stats with dates and sources.
3. Schema with author + dates.
4. Passage-level structure (each H2 is a question; first paragraph after H2 is the answer).

• Pair with seo-ai-search-share-of-voice for domain-level brand-vs-brand visibility (this skill is page-level).
• Pair with seo-content-audit to apply the CITE rubric to the page (which has more citation-readiness items).
• Pair with seo-schema to fix schema issues identified in step 7.
• Don't optimize for AIO at the expense of human readability. The two reinforce each other when done right.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: amirjahfar1
• Source: amirjahfar1/automate-seo-with-claude
• License: MIT
• Homepage: https://nextbrainsolutions.com/

Install and usage instructions live in the source repository linked above.