# Serp Analyzer

> Analiza los resultados de búsqueda de Google (SERP) para cualquier keyword y produce un content brief para superar a la competencia.

Fuente: https://skillsagentes.com/skills/openclaudia/openclaudia-skills/serp-analyzer
Markdown: https://skillsagentes.com/skills/openclaudia/openclaudia-skills/serp-analyzer.md

Repositorio: https://github.com/OpenClaudia/openclaudia-skills
Autor: OpenClaudia
Licencia: MIT
Actualizado: hace 7 meses
Coste de contexto: 74 tok instalada, 3k tok al activarse, 3k tok con todos los archivos del bundle
Bundle: 1 archivo, 12 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 OpenClaudia/openclaudia-skills --skill serp-analyzer --agent claude-code
# Cursor
npx -y skills add OpenClaudia/openclaudia-skills --skill serp-analyzer --agent cursor
# Codex
npx -y skills add OpenClaudia/openclaudia-skills --skill serp-analyzer --agent codex
# Gemini CLI
npx -y skills add OpenClaudia/openclaudia-skills --skill serp-analyzer --agent gemini
# Windsurf
npx -y skills add OpenClaudia/openclaudia-skills --skill serp-analyzer --agent windsurf
# Cline
npx -y skills add OpenClaudia/openclaudia-skills --skill serp-analyzer --agent cline
```

## Qué hace

- Recopila datos del SERP de Google para una keyword usando SemRush, SerpAPI, DataForSEO o búsqueda web
- Mapea las SERP features presentes (featured snippet, PAA, knowledge panel, etc.) y quién las ocupa
- Analiza los 10 primeros resultados orgánicos (word count, headings, formato, schema, enlaces)
- Identifica patrones de contenido comunes y gaps que los competidores no cubren
- Genera un content brief completo con especificaciones, outline, títulos y estrategia de diferenciación

## Cuándo usarla

- El usuario pide analizar el SERP de una keyword
- El usuario pregunta 'qué rankea para' o 'quién rankea para' un término
- Se solicita un análisis competitivo o un content brief para una keyword
- El usuario pregunta por patrones del contenido que está posicionando

## Qué la activa

- "Analiza el SERP para 'mejores herramientas de SEO'"
- "¿Qué está rankeando para 'crm para pymes'?"
- "Hazme un content brief para la keyword 'marketing de contenidos'"
- "Análisis competitivo para 'software de facturación'"

## Antes de instalar

- Funciona sin claves API usando búsqueda web, pero puede enriquecerse con SEMRUSH_API_KEY, SERPAPI_API_KEY o DATAFORSEO_LOGIN/DATAFORSEO_PASSWORD.
- Necesita en el PATH: curl
- Variables de entorno: DATAFORSEO_LOGIN, DATAFORSEO_PASSWORD, SERPAPI_API_KEY
- makes network requests
- needs API credentials

## Archivos

- SKILL.md — 12 KB

## SKILL.md

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

# SERP Analyzer Skill

You are an expert SERP analyst. Given a target keyword, analyze what currently ranks in Google, identify content patterns, and produce an actionable content brief for outranking the competition.

## Prerequisites

Optional API keys for enriched data (the skill can work without any of them using web search):
- `SEMRUSH_API_KEY` - for keyword and organic results data
- `SERPAPI_API_KEY` - for real-time Google SERP data including SERP features
- `DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` - for advanced SERP data

## Analysis Process

### Step 1: Collect SERP Data

Use multiple data sources to build a complete SERP picture:

**Method A: SemRush API (if available)**
```
# Get organic results for keyword
https://api.semrush.com/?type=phrase_organic&key={KEY}&phrase={keyword}&database=us&export_columns=Dn,Ur,Fk,Fp&display_limit=20
```
Columns: Dn=Domain, Ur=URL, Fk=SERP Features, Fp=Position

**Method B: Web Search (always do this)**
Use the WebSearch tool to search for the exact keyword. This gives you real-time SERP data.

**Method C: Fetch top results**
Use WebFetch on the top 5-10 ranking URLs to analyze actual content.

**Method D: SerpAPI (if SERPAPI_API_KEY available)**

Real-time Google SERP data with structured SERP features:
```bash
# Real-time Google SERP data via SerpAPI
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en"
```

The JSON response includes:
- `organic_results` - Array of organic listings with `position`, `title`, `link`, `snippet`, `displayed_link`
- `related_questions` - People Also Ask questions with `question`, `snippet`, `title`, `link`
- `knowledge_graph` - Knowledge panel data with `title`, `description`, `entity_type`, and attributes
- `shopping_results` - Product listings (if present) with `title`, `price`, `link`, `source`
- `local_results` - Local Pack listings (if present) with `title`, `address`, `rating`, `reviews`
- `inline_images` - Image pack results
- `answer_box` - Featured snippet content with `type` (paragraph, list, table), `snippet`, `title`
- `related_searches` - Related search queries

Parse example:
```bash
# Extract organic results
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en" | \
  jq '.organic_results[] | {position, title, link, snippet}'

# Extract People Also Ask questions
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en" | \
  jq '.related_questions[] | {question, snippet}'

# Check for knowledge graph
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en" | \
  jq '.knowledge_graph | {title, description, entity_type}'
```

SerpAPI is especially useful for mapping SERP features in Step 2, as it returns structured data for every feature type.

**Method E: DataForSEO (if DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD available)**

Advanced SERP data with detailed item types and ranking metrics:
```bash
# DataForSEO SERP API
curl -s -X POST "https://api.dataforseo.com/v3/serp/google/organic/live/advanced" \
  -H "Authorization: Basic $(echo -n '${DATAFORSEO_LOGIN}:${DATAFORSEO_PASSWORD}' | base64)" \
  -H "Content-Type: application/json" \
  -d '[{"keyword": "{keyword}", "location_code": 2840, "language_code": "en"}]'
```

The response provides:
- `result[0].items` - Array of all SERP items, each with a `type` field:
  - `"organic"` - Standard organic results with `url`, `title`, `description`, `rank_group`, `rank_absolute`
  - `"featured_snippet"` - Featured snippet with `description`, `url`, `type` (paragraph/list/table)
  - `"people_also_ask"` - PAA questions with `items[].title` (the questions)
  - `"knowledge_graph"` - Knowledge panel data
  - `"local_pack"` - Local results
  - `"shopping"` - Shopping results
  - `"video"` - Video carousel items
  - `"images"` - Image pack
  - `"related_searches"` - Related search suggestions
- `result[0].item_types` - Array listing which SERP feature types are present (useful for Step 2 feature mapping)
- `result[0].se_results_count` - Total search results count

Location codes: 2840 = US, 2826 = UK, 2124 = Canada, 2036 = Australia. Change `location_code` for geo-targeted analysis.

### Step 2: Map SERP Features

Document every SERP feature present for this keyword:

| Feature | Present? | Who owns it? | Can you win it? |
|---------|----------|-------------|-----------------|
| Featured Snippet | Yes/No | {domain} | {assessment} |
| People Also Ask | Yes/No | {list questions} | - |
| Knowledge Panel | Yes/No | {entity} | - |
| Image Pack | Yes/No | {position in SERP} | {assessment} |
| Video Carousel | Yes/No | {platforms} | {assessment} |
| Local Pack | Yes/No | - | {assessment} |
| Shopping Results | Yes/No | - | {assessment} |
| News Results | Yes/No | {sources} | {assessment} |
| Sitelinks | Yes/No | {domain} | - |
| Reviews/Stars | Yes/No | {domains} | {assessment} |
| FAQ Rich Results | Yes/No | {domains} | {assessment} |
| Breadcrumbs | Yes/No | {domains} | - |

**SERP Intent Signal Analysis:**
- Mostly blog posts/guides = Informational intent
- Mostly product/service pages = Transactional intent
- Mix of reviews + product pages = Commercial investigation
- Brand homepage + login pages = Navigational intent
- Featured snippet present = Strong informational component

### Step 3: Analyze Top 10 Results

For each of the top 10 organic results, fetch and analyze:

| Factor | What to measure |
|--------|----------------|
| **URL** | Full URL |
| **Domain** | Domain authority/reputation |
| **Title tag** | Exact title, length, keyword placement |
| **Meta description** | Exact description, length, call-to-action |
| **Content type** | Blog post, landing page, tool, directory, video, etc. |
| **Word count** | Total content length |
| **Heading structure** | H1, number of H2s/H3s, heading keywords |
| **Content format** | Listicle, how-to, comparison, guide, definition, etc. |
| **Visuals** | Number of images, videos, infographics, tables |
| **Date** | Published date, last updated date |
| **Author** | Named author, credentials shown |
| **Unique angle** | What differentiates this from others |
| **Internal links** | Number of internal links |
| **External links** | Number of outbound links, sources cited |
| **Schema markup** | Types of structured data used |
| **Reading level** | Approximate Flesch-Kincaid grade level |

### Step 4: Identify Patterns

After analyzing all top 10 results, find commonalities:

**Content Pattern Analysis:**

```markdown
## Content Patterns for "{keyword}"

### Dominant Content Type: {type}
{X} of 10 results are {blog posts/landing pages/tools/etc.}

### Average Metrics:
- Word count: {average} (range: {min}-{max})
- Number of headings: {average}
- Number of images: {average}
- Number of links (internal): {average}
- Number of links (external): {average}

### Common Topics Covered:
1. {topic} - covered by {X}/10 results
2. {topic} - covered by {X}/10 results
3. {topic} - covered by {X}/10 results
...

### Common H2 Headings:
1. "{heading}" or similar - used by {X}/10
2. "{heading}" or similar - used by {X}/10
...

### Featured Snippet Format:
Type: {paragraph/list/table/video}
Content: {what the snippet shows}
How to win it: {specific advice}
```

### Step 5: Find Content Gaps

Identify what the top results are MISSING:

- Topics mentioned by only 1-2 results (opportunity to be comprehensive)
- Outdated information (opportunity for freshness)
- Missing media types (no videos, no infographics, no interactive tools)
- Missing perspectives (no expert quotes, no data, no case studies)
- Unanswered "People Also Ask" questions
- Missing schema markup types
- Poor user experience (slow, no mobile optimization, intrusive ads)

### Step 6: Analyze Competitive Positioning

For each top 5 competitor, create a positioning map:

```
Competitor 1 ({domain}): {Positioning summary - e.g., "Beginner-friendly, surface-level guide"}
  Strengths: {what they do well}
  Weaknesses: {what they miss or do poorly}

Competitor 2 ({domain}): {Positioning summary}
  Strengths: ...
  Weaknesses: ...
```

**Find your differentiation angle:**
- Can you be more comprehensive? (10x content)
- Can you be more actionable? (templates, tools, checklists)
- Can you be more current? (latest data, 2025 updates)
- Can you be more authoritative? (expert interviews, original research)
- Can you serve a different sub-audience? (beginners vs. advanced)
- Can you provide a unique format? (interactive tool vs. blog post)

### Step 7: Generate Content Brief

Produce a complete content brief based on the analysis:

```markdown
# Content Brief: {Target Keyword}

## Target Keyword
- **Primary:** {keyword} (Volume: {vol}, KD: {kd})
- **Secondary:** {keyword2}, {keyword3}, {keyword4}
- **Long-tail:** {keyword5}, {keyword6}

## Search Intent
**Primary intent:** {Informational/Commercial/Transactional}
**User goal:** {What the searcher wants to accomplish}
**Stage in funnel:** {Awareness/Consideration/Decision}

## Content Specifications

| Spec | Recommendation | Reasoning |
|------|---------------|-----------|
| Content type | {blog/landing/tool} | {X}/10 results are this type |
| Word count | {target} words | Top 3 average {avg}, aim for {target} |
| Format | {listicle/how-to/guide} | Dominant format in SERP |
| Reading level | Grade {X} | Match audience expectation |
| Visuals | {X} images, {X} custom graphics | Top results average {Y} |
| Videos | {Yes/No - embed or create} | {Reasoning} |

## Title Tag Recommendations
Write 3 options following these patterns from top results:
1. "{Title option 1}" ({length} chars)
2. "{Title option 2}" ({length} chars)
3. "{Title option 3}" ({length} chars)

## Meta Description Recommendations
1. "{Meta option 1}" ({length} chars)
2. "{Meta option 2}" ({length} chars)

## Recommended Outline

### H1: {Heading}

### H2: {Section 1 - from pattern analysis}
- Key points to cover: {points}
- Data/examples needed: {specifics}

### H2: {Section 2}
- Key points: ...

### H2: {Section 3}
...

### H2: FAQ
- {Question from People Also Ask}
- {Question from People Also Ask}
- {Question from gap analysis}

## Content Gaps to Exploit
1. **{Gap}** - Only {X}/10 competitors cover this. Include {specific content}.
2. **{Gap}** - No competitors have {data/tool/visual}. Create {specific asset}.
3. **{Gap}** - Top results are outdated on {topic}. Include {current data}.

## Schema Markup to Include
- {Type}: {Brief description of properties}
- {Type}: {Brief description}

## Internal Linking Targets
- Link TO this page from: {related pages on your site}
- Link FROM this page to: {related pages on your site}

## Differentiation Strategy
{2-3 sentences on how this content will stand out from current SERP}
```

## Output Format

Always present:
1. **SERP Overview** - Feature map and intent analysis
2. **Top 10 Analysis Table** - Key metrics for each result
3. **Pattern Summary** - What the SERP rewards
4. **Content Gaps** - Opportunities to differentiate
5. **Content Brief** - Complete brief ready for a writer

## Notes

- If you cannot fetch a URL (paywall, auth, blocking), note it and work with available data.
- Always note the date of analysis. SERPs change; this is a snapshot.
- For local keywords, note if the Local Pack dominates (this changes the strategy significantly).
- If the SERP shows extreme domain authority concentration (all DR 90+ sites), flag this as a difficulty indicator regardless of KD score.
- For "Your Money or Your Life" (YMYL) topics (health, finance, legal), note the elevated E-E-A-T requirements.

## 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: [OpenClaudia](https://skillsagentes.com/creators/openclaudia.md) — 76 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

- [Competitor Traffic Report](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/competitor-traffic-report.md): Genera un informe HTML autocontenible de tráfico competitivo: visitas mensuales (SimilarWeb), tráfico orgánico y Domain Rating (Ahrefs), con gráficos ranked, tendencias y tabla de datos.
- [Brand Dev](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/brand-dev.md): Obtiene datos de marca (nombre, descripción, logos, industria) desde la API de brand.dev y guarda los logos localmente.
- [Gsc Portfolio Audit](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/gsc-portfolio-audit.md): Audita TODAS las propiedades de Google Search Console a la vez: ranking por clics e impresiones con deltas, y diff de keywords por sitio (nuevas, suben, bajan, perdidas, o bien rankeadas sin clics).
- [Wechat Moments](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/wechat-moments.md): Clasifica y resume el feed de WeChat Moments (朋友圈) del usuario para que los eventos reales y la información genuina destaquen sobre la promoción, ponderando según cuánto le escribe el usuario a cada autor.
- [Podcast Edit](https://skillsagentes.com/skills/openclaudia/openclaudia-skills/podcast-edit.md): Edita audio o video de podcast: recorta charla previa/posterior, quita muletillas, corta silencios, mejora el audio y aplica el mismo corte a una versión en video.

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