# Seo Ads

> Panorama competitivo de búsqueda pagada para un dominio o keyword: huella de anuncios PPC, patrones de copy, quién puja en las mismas keywords y presencia en shopping/ad-pack del SERP.

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

Repositorio: https://github.com/seranking/seo-skills
Autor: seranking
Licencia: MIT
Actualizado: hace 4 meses
Coste de contexto: 118 tok instalada, 2.1k tok al activarse, 2.1k tok con todos los archivos del bundle
Bundle: 1 archivo, 8 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-ads --agent claude-code
# Cursor
npx -y skills add seranking/seo-skills --skill seo-ads --agent cursor
# Codex
npx -y skills add seranking/seo-skills --skill seo-ads --agent codex
# Gemini CLI
npx -y skills add seranking/seo-skills --skill seo-ads --agent gemini
# Windsurf
npx -y skills add seranking/seo-skills --skill seo-ads --agent windsurf
# Cline
npx -y skills add seranking/seo-skills --skill seo-ads --agent cline
```

## Qué hace

- Extrae la huella de anuncios de un dominio o palabra clave usando datos PPC de SE Ranking
- Identifica quién más puja por las mismas palabras clave y sus patrones de copy publicitario
- Analiza la presencia de anuncios/shopping en el SERP (tads, bads, sads, mads)
- Genera un brief competitivo de anuncios más una shortlist de palabras clave recomendadas para pujar

## Cuándo usarla

- El usuario pide 'paid search analysis', 'competitor ads' o 'PPC competitive'
- El usuario pregunta 'who bids on this keyword' o 'ad copy intelligence'
- Se necesita mapear el 'paid keyword footprint' de un dominio o analizar un 'shopping pack'

## Qué la activa

- "Analiza la huella de anuncios pagados del dominio hostinger.com en EE. UU."
- "¿Quién más está pujando por la palabra clave 'crm software'?"
- "Dame un brief de inteligencia de anuncios y la lista de keywords recomendadas para pujar"

## Antes de instalar

- Requiere el servidor MCP de SE Ranking conectado y que el usuario indique un dominio o keyword objetivo, más el país (por defecto us).

## Archivos

- SKILL.md — 8 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-ads-hostinger-com-20260514/ADS.md](../../examples/seo-ads-hostinger-com-20260514/ADS.md)

# Paid-Search Intelligence (Ads)

Map a domain's paid-search footprint and the competitive landscape around its target keywords. Output: a brief on what the brand is bidding on, who else bids on the same terms, ad-copy patterns the leading competitors use, SERP ad+shopping presence per keyword, and a recommended bid-keyword shortlist.

## Prerequisites

- SE Ranking MCP server connected.
- User provides: (a) a target domain OR a target keyword (skill detects which), (b) target country (default `us`).

## Process

1. **Validate input & preflight**
   - Determine: domain mode (analyse a brand's paid footprint) or keyword mode (analyse the bidding landscape for one keyword).
   - `DATA_getCreditBalance` — surface remaining credits.

2. **Domain mode** `DATA_getDomainAdsByDomain`
   - Pull paid keywords the target domain bids on.
   - For each: keyword, search volume, CPC, position, ad copy (title + description), URL.
   - Sort by traffic-weighted score (`volume × CTR-by-paid-position × bid-share`).

3. **Keyword mode** `DATA_getDomainAdsByKeyword`
   - Pull all domains bidding on the target keyword.
   - For each: domain, ad position, ad copy, URL.
   - Surface the top 10 advertisers + their copy patterns.

4. **Intent enrichment** `DATA_getKeywordQuestions`
   - For the keyword(s) in scope, pull related questions.
   - Identifies question-phrased intent variants worth bidding on (often cheaper, higher conversion).

5. **SERP ad/shopping presence** `DATA_getSerpResults`
   - For top 5 keywords (domain mode) or the target keyword (keyword mode):
     - Use SERP-feature filters to detect ad-pack composition: `tads` (top ads above organic), `bads` (bottom ads below organic), `sads` (shopping ads / Google Shopping pack), `mads` (mobile/map-pack ads).
     - Top SERP ad slots (positions 1-4 above organic, 1-3 below).
     - Shopping pack presence (carousel of product cards).
     - Image pack, local pack — these displace ad inventory.
   - Capture which advertisers occupy those slots.

6. **Ad copy pattern analysis**
   - Cluster ad headlines + descriptions by recurring patterns.
   - Identify: USP language used by leaders, pricing/discount mentions, audience segmentation, CTA verbs.
   - Highlight outliers (advertisers doing something different).

7. **Paid-keyword gap (domain mode)** `DATA_getDomainKeywords` with `type: 'adv'`
   - Pull the user's domain's paid keywords using the `type: 'adv'` switch.
   - For each top competitor (from step 2 or `DATA_getDomainCompetitors` with `type: 'adv'`): pull their paid keywords with `type: 'adv'`.
   - Diff: paid keywords competitors bid on that the user's domain doesn't.
   - This becomes the highest-leverage portion of the bid-keyword shortlist (step 8).
   - Skip in keyword mode (no domain to gap against).

8. **Recommended bid-keyword shortlist**
   - For domain mode: paid-keyword gap from step 7 + adjacent question-intent variants.
   - For keyword mode: question-intent and long-tail variants that are likely cheaper than the head term.
   - Each row: keyword, est. CPC, est. volume, who else bids, why-recommended.

9. **Synthesise** `ADS.md`

## Output format

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

```
seo-ads-{target-slug}-{YYYYMMDD}/
├── ADS.md                              (synthesised brief — primary deliverable; inlines paid footprint, bidding landscape, SERP ad/shopping pack, ad copy patterns, paid keyword gap)
├── recommended-keywords.csv            (bid-keyword shortlist — load-bearing CSV the PPC team pastes into bid tooling)
└── evidence/
    ├── 01-paid-footprint.md           (domain mode: brand's paid keywords — raw step output)
    ├── 02-bidding-landscape.md        (keyword mode: advertisers on the keyword — raw step output)
    ├── 03-question-variants.md        (DATA_getKeywordQuestions enrichment)
    ├── 04-serp-ad-shopping-pack.md    (SERP feature inventory per keyword)
    ├── 05-ad-copy-patterns.md         (clustered headline/description patterns)
    └── 06-paid-keyword-gap.md         (domain mode: type='adv' diff vs competitors)
```

Step files 01, 02, 04, 05, 06 are inlined as sections in `ADS.md`; the copies in `evidence/` preserve the raw step outputs for reproducibility.

`ADS.md` follows this shape:

```markdown
# Paid-Search Intelligence: {target}

> Snapshot dated {YYYY-MM-DD} · Country: {country} · Mode: {domain | keyword}

## Footprint summary
- Paid keywords: {n}
- Estimated paid traffic: {n}/mo
- Average CPC: ${n}
- SERP slots covered: {n} of top-4 above organic across {n} target keywords

## Top 10 paid keywords (domain mode)

| Keyword | Volume | CPC | Position | Ad copy excerpt |
|---|---|---|---|---|
| {kw} | {n} | ${n} | {pos} | "{headline} — {snippet}" |
| ...

## Bidding landscape (keyword mode — for "{keyword}")

| Advertiser | Position | Ad copy excerpt | URL |
|---|---|---|---|
| {domain} | {pos} | "{headline} — {snippet}" | {url} |
| ...

## Ad copy patterns (top patterns observed)

1. **Pricing-led:** "{N}% off — start at ${X}/mo" — used by {n} advertisers.
2. **Outcome-led:** "Get {specific outcome} in {time}" — used by {n}.
3. **Trust-led:** "Trusted by {n} {audience}" — used by {n}.
4. ...

## SERP feature inventory

| Keyword | Top ads | Shopping pack | PAA | Image pack |
|---|---|---|---|---|
| {kw} | {advertiser list} | {✓/✗} | {✓/✗} | {✓/✗} |
| ...

## Recommended bid-keyword shortlist

See `recommended-keywords.csv`. Top 10:

| Keyword | Volume | Est. CPC | Why |
|---|---|---|---|
| {kw} | {n} | ${n} | Question-intent variant; competitor X bids on head term but not this. |
| ...

## Constraints / caveats
- CPC and volume estimates are directional. Actual costs depend on Quality Score, time of day, audience, etc.
- {Note any ad-copy that's clearly seasonal / promotional and may not represent steady-state.}

## Recommended next step
Cross-reference these paid keywords with `seo-keyword-cluster` output to find under-served paid clusters. For organic content opportunities corresponding to these paid keywords, run `seo-keyword-niche`.
```

`recommended-keywords.csv` columns: `keyword,volume,cpc_estimate,position_target,intent,competitor_count,why_recommended`

## Tips

- Respect rate limit. Domain mode: ~3–5 calls. Keyword mode: ~3 calls. Plus a few SERP queries.
- Cost: ~10–20 credits typical for domain mode; ~5–10 for keyword mode.
- **CPC estimates lag.** SE Ranking's CPC data is not real-time auction data; treat as ±30% directional.
- Ad copy often reveals competitor positioning before product launches do — periodic review (quarterly) catches strategic shifts.
- Question-intent variants often have lower CPC and higher conversion than head terms. The shortlist in step 8 prioritises these.
- Pair with `seo-keyword-niche` for organic content opportunities derived from paid keyword research.
- Pair with `seo-competitor-pages` if the bidding landscape reveals "X vs Y" / "alternatives" intent — those keywords convert best as comparison pages, not paid ads.
- **Ads data via shared DATA_* tools** — beyond the dedicated `DATA_getDomainAdsByDomain` / `DATA_getDomainAdsByKeyword`, the `type: 'adv'` enum switch on `DATA_getDomainKeywords`, `DATA_getDomainKeywordsComparison`, `DATA_getDomainCompetitors`, `DATA_getDomainPages`, and similar tools surfaces the paid view of the same data structures. Combine with the `tads/bads/sads/mads` SERP-feature filters and the CPC filter on SERP queries to map paid landscape comprehensively.
- Don't recommend paid keywords without context. The shortlist is a starting point for the PPC team, not an autopilot.

## 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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