# Seo Geo

> Análisis de Generative Engine Optimization (GEO) a nivel de URL: identifica qué consultas AIO citan la página y cuáles deberían, y recomienda cambios para mejorar su citabilidad en LLMs.

Fuente: https://skillsagentes.com/skills/seranking/seo-skills/seo-geo
Markdown: https://skillsagentes.com/skills/seranking/seo-skills/seo-geo.md

Repositorio: https://github.com/seranking/seo-skills
Autor: seranking
Licencia: MIT
Actualizado: hace 4 meses
Coste de contexto: 135 tok instalada, 2.6k tok al activarse, 2.6k tok con todos los archivos del bundle
Bundle: 1 archivo, 10 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add seranking/seo-skills --skill seo-geo --agent claude-code
# Cursor
npx -y skills add seranking/seo-skills --skill seo-geo --agent cursor
# Codex
npx -y skills add seranking/seo-skills --skill seo-geo --agent codex
# Gemini CLI
npx -y skills add seranking/seo-skills --skill seo-geo --agent gemini
# Windsurf
npx -y skills add seranking/seo-skills --skill seo-geo --agent windsurf
# Cline
npx -y skills add seranking/seo-skills --skill seo-geo --agent cline
```

## Qué hace

- Analiza el footprint de citación en AI Overview de una URL específica, keyword por keyword
- Compara los pasajes de la página candidata contra fuentes que sí son citadas para detectar brechas
- Audita el schema JSON-LD y los archivos de protocolo AI (llms.txt, RSL) del dominio
- Genera GEO.md con tabla de citación, pasajes con puntuación de citabilidad y top 5 recomendaciones

## Cuándo usarla

- El usuario pide "GEO para esta página" o "análisis de citación AIO"
- Se quiere saber "por qué esta página no está citada" en AI Overview
- Se busca mejorar la citabilidad de una URL en motores como Perplexity o ChatGPT

## Cuándo no

- Se necesita comparar marca vs marca a nivel de dominio (usar seo-ai-search-share-of-voice en su lugar)

## Qué la activa

- "Haz un análisis GEO de esta URL"
- "¿Por qué esta página no aparece citada en AI Overview?"
- "Analiza la citabilidad AIO de este artículo y dame recomendaciones"

## Antes de instalar

- Requiere el servidor MCP de SE Ranking conectado y la herramienta WebFetch de Claude; Firecrawl es opcional para el chequeo de schema y archivos AI-protocol.

## Archivos

- SKILL.md — 10 KB

## SKILL.md

Reproducido tal cual desde seranking/seo-skills bajo MIT. Esta sección es el documento original y está en inglés.

> 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)
├── 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:

```markdown
# 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

- Respect rate limit. ~5 keywords × 2 AIO calls = ~10 calls; plus 2–3 WebFetch on cited sources. Easy.
- Cost: ~10–20 SE Ranking credits 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.

## Dónde encaja

- Categoría: [SEO y GEO](https://skillsagentes.com/categorias/seo-geo.md) — Keywords, auditorías on-page, datos estructurados y visibilidad en respuestas de IA.
- Creador: [seranking](https://skillsagentes.com/creators/seranking.md) — 32 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Client Onboarding Proposal](https://skillsagentes.com/skills/seranking/seo-skills/client-onboarding-proposal.md): Construye una propuesta de onboarding de cliente basada en datos, combinando un audit SEO + búsqueda IA de SE Ranking con un análisis en Planable del rendimiento social actual del cliente.
- [Local Gmb Visibility](https://skillsagentes.com/skills/seranking/seo-skills/local-gmb-visibility.md): Combina el seguimiento de rankings por ciudad de SE Ranking con contenido social local en Planable, incluyendo publicaciones de Google Business Profile.
- [Ai Search Gaps To Social Campaign](https://skillsagentes.com/skills/seranking/seo-skills/ai-search-gaps-to-social-campaign.md): Encuentra los prompts y temas de búsqueda con IA donde una marca es invisible o pierde frente a competidores en SE Ranking, y convierte esos vacíos en una campaña social en Planable con seguimiento antes/después.
- [Site Audit To Social Distribution](https://skillsagentes.com/skills/seranking/seo-skills/site-audit-to-social-distribution.md): Detecta páginas débiles y brechas de contenido en SE Ranking, y coordina la corrección en Planable: redacta el copy reescrito para revisión y aprobación, y programa la distribución social.
- [Seo Ai Social Report](https://skillsagentes.com/skills/seranking/seo-skills/seo-ai-social-report.md): Genera un único reporte de rendimiento que une rankings SEO, visibilidad en búsquedas IA (SE Ranking) y engagement social (Planable): resumen en el chat más un informe HTML interactivo autónomo.

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