# Seo Competitor Pages

> Genera páginas de aterrizaje SEO de comparación 'X vs Y' y 'alternativas a X' con matriz de funcionalidades, schema y bloques de conversión, distinta de `seo-agency-landing-page`.

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

Repositorio: https://github.com/seranking/seo-skills
Autor: seranking
Licencia: MIT
Actualizado: hace 4 meses
Coste de contexto: 126 tok instalada, 2.4k tok al activarse, 2.4k tok con todos los archivos del bundle
Bundle: 1 archivo, 9 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-competitor-pages --agent claude-code
# Cursor
npx -y skills add seranking/seo-skills --skill seo-competitor-pages --agent cursor
# Codex
npx -y skills add seranking/seo-skills --skill seo-competitor-pages --agent codex
# Gemini CLI
npx -y skills add seranking/seo-skills --skill seo-competitor-pages --agent gemini
# Windsurf
npx -y skills add seranking/seo-skills --skill seo-competitor-pages --agent windsurf
# Cline
npx -y skills add seranking/seo-skills --skill seo-competitor-pages --agent cline
```

## Qué hace

- Genera páginas de comparación 'X vs Y' y 'alternativas a X' orientadas a keywords de intención comparativa.
- Extrae datos de competidores, SERPs comparativas y páginas de comparación existentes vía SE Ranking MCP y WebFetch/Firecrawl.
- Produce un borrador de página con matriz de funcionalidades, FAQ basada en PAA, schema JSON-LD y bloques de conversión.
- Crea una carpeta con COMPARISON.md, feature-matrix, schema.jsonld y evidencia cruda de cada paso.

## Cuándo usarla

- El usuario pide una 'comparison page', 'vs page', 'alternatives page', 'X vs Y', 'alternative to X' o 'competitor comparison page'.

## Cuándo no

- Para páginas de demanda top-of-funnel (usar `seo-agency-landing-page` en su lugar).

## Qué la activa

- "Crea una página comparativa Linear vs Jira"
- "Genera una página de alternativas a Notion"
- "Necesito una landing 'best CRM for startups'"
- "Haz un vs page para mi producto contra 3 competidores"

## Antes de instalar

- Requiere el servidor MCP de SE Ranking conectado y la herramienta WebFetch de Claude disponible; Firecrawl es opcional.

## Archivos

- SKILL.md — 9 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-competitor-pages-linear-vs-jira-20260514/COMPARISON.md](../../examples/seo-competitor-pages-linear-vs-jira-20260514/COMPARISON.md)

# Competitor Comparison & Alternatives Pages

Produce conversion-tuned landing pages targeting comparative-intent keywords ("X vs Y", "alternatives to X", "best X for Y"). The deliverable is a paste-ready page draft with feature matrix, balanced verdict, schema markup, and a CTA flow that converts comparison-stage traffic.

## Page types this skill produces

1. **"X vs Y" head-to-head** — direct comparison between two products/services. Target keyword: `[Product A] vs [Product B]`.
2. **"Alternatives to X"** — listicle-format page positioning the user's product as one of N alternatives to a category leader. Target keyword: `[Competitor] alternatives` or `alternatives to [Competitor]`.
3. **"Best X for Y"** — segmented best-of page targeting a use case or audience. Target keyword: `best [category] for [use case]`.

## Prerequisites

- SE Ranking MCP server connected.
- Claude's `WebFetch` tool available.
- User provides: (a) the user's brand/product (the page's hero), (b) target competitor(s) — at least one, optionally up to 5 for an alternatives page, (c) page type (auto-detected from the keyword if user doesn't specify), (d) target country (default `us`).

## Process

1. **Validate & determine page type**
   - From the user's input, detect: vs / alternatives / best-of.
   - If page type unclear, ask the user. Don't guess silently.

2. **Pull competitor context** `DATA_getDomainCompetitors`
   - For the user's domain, list top organic competitors by `common_keywords` overlap.
   - Validate that the user's named competitor is in the list (or close).

3. **Pull keyword data per brand** `DATA_getDomainKeywords`
   - For the user's domain and each named competitor, pull top 100 organic keywords.
   - Identify: keywords each brand owns exclusively, keywords both rank for, gaps.

4. **Pull comparative SERPs** `DATA_getSerpResults` and `DATA_getKeywordQuestions`
   - For "X vs Y" / "alternatives to X" / "best X for Y" target keyword(s):
     - Top 10 organic results — who else ranks for this comparative keyword?
     - PAA questions (these become FAQ section content).
     - Featured snippet (if present, capture the answer pattern).

5. **Fetch existing comparison pages** `WebFetch` (always) + `mcp__firecrawl-mcp__firecrawl_scrape` (when available)
   - **WebFetch first** (free): pull the top 3 SERP winners' markdown. Extract H2 outline, feature-matrix dimensions, verdict pattern, CTA placement.
   - **Firecrawl second** (3 Firecrawl credits — 1 per winner) — recovers what WebFetch markdown can't show:
     - Schema types from `<script type="application/ld+json">` blocks (`Product` ×N, `BreadcrumbList`, `FAQPage`, `Review`, `AggregateRating`).
     - `og:title` / `og:description` / `og:image` / `twitter:card` from `metadata`.
     - `<title>` and meta description lengths from real HTML.
   - **If Firecrawl unavailable:** WebFetch portion runs unchanged. The "schema types" line in `evidence/04-existing-pages-teardown.md` reads `(skipped — Firecrawl required for JSON-LD)`. Schema generation in step 8 falls back to a default `Product + BreadcrumbList + FAQPage` template instead of mirroring whatever the winners use.
   - This anchors the draft in observed-rewarded-pattern.

5b. **Bulk competitor scrape** `mcp__firecrawl-mcp__firecrawl_scrape` (optional, opt-in)
   - When user passes `--bulk-scrape <urls>` or supplies a list of competitor URLs to compare directly (beyond the SERP top-3), Firecrawl-scrape each URL.
   - For each URL, extract: `<title>`, `og:*`, `twitter:*`, JSON-LD `@type`s, hero-image presence, pricing-block detection (regex on prose for `$N/mo`, `€N/mo`, etc.), CTA count, comparison-table presence, free-tier-mention boolean.
   - Output `competitor-elements.csv` — one row per competitor URL × these signals.
   - Cost: 1 Firecrawl credit per URL. Surface estimate before running; refuse >50 URLs without `--confirm-cost`.

6. **Pull keyword comparison data** `DATA_getDomainKeywordsComparison` (if available for the brands)
   - Side-by-side keyword overlap.

7. **Build feature matrix**
   - Dimensions inferred from the top SERP winners (e.g., "Pricing", "Free tier", "Integrations", "Support tiers", "Best for").
   - Cells: ✓ / ✗ / partial / "TBD — confirm with PM" placeholders for fields you can't auto-infer.
   - Where SE Ranking data informs a cell (e.g., "ranks for X enterprise keywords"), pull the number.

8. **Synthesise** `COMPARISON.md`
   - Hero (target keyword in H1, balanced positioning).
   - TL;DR / verdict box in first 200 words.
   - Feature matrix.
   - Section per major dimension (each H2 = one dimension).
   - PAA-derived FAQ (top 3–5 questions from step 4).
   - Verdict / recommendation.
   - CTA flow.
   - Schema-ready JSON-LD: `Product` (×N) + `BreadcrumbList` + `FAQPage` (if real Q&A).

## Output format

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

```
seo-competitor-pages-{target-slug}-{YYYYMMDD}/
├── COMPARISON.md                     (the page draft — primary deliverable)
├── 05-feature-matrix.md              (inferred dimensions × brands — load-bearing reference for PMs/writers)
├── schema.jsonld                     (paste-ready Product + Breadcrumb + FAQ — load-bearing artefact for engineering)
├── 05b-competitor-elements.csv       (only if --bulk-scrape ran: competitor URL × on-page-element grid)
└── evidence/
    ├── 01-competitor-context.md      (DATA_getDomainCompetitors — raw step output)
    ├── 02-keyword-overlap.md         (DATA_getDomainKeywords for each brand — raw step output)
    ├── 03-comparative-serp.md        (top 10 + PAA for the target keyword — raw step output)
    └── 04-existing-pages-teardown.md (top-3 SERP winners' structure + schema/og — Firecrawl-recovered)
```

Top-level: `COMPARISON.md` + `05-feature-matrix.md` + `schema.jsonld`. The 01–04 step files preserve raw API/scrape outputs in `evidence/`. `05b-competitor-elements.csv` only appears when `--bulk-scrape` was passed.

`COMPARISON.md` for an "X vs Y" page follows this shape:

```markdown
# {User's Brand} vs {Competitor}: 2026 Comparison

> Updated {YYYY-MM-DD}. Compare {Brand A} and {Brand B} on pricing, features, integrations, and best-fit use case.

## TL;DR
{One paragraph balanced verdict — when to choose A, when to choose B}

## At a glance

| Dimension | {Brand A} | {Brand B} |
|---|---|---|
| Starting price | {$X/mo} | {$Y/mo} |
| Free tier | {✓/✗} | {✓/✗} |
| Best for | {use case} | {use case} |
| Integrations | {n} | {n} |
| Support | {tier} | {tier} |
| ... | | |

## {Dimension 1 header — e.g., Pricing}
{Side-by-side detail, balanced. Avoid hyperbole.}

## {Dimension 2 header — e.g., Features}
{...}

## {Dimension 3 header — e.g., Integrations}
{...}

## {Dimension 4 header — e.g., Support}
{...}

## When to choose {Brand A}
- {scenario 1}
- {scenario 2}
- {scenario 3}

## When to choose {Brand B}
- {scenario 1}
- {scenario 2}
- {scenario 3}

## FAQ
**{PAA question 1}**
{Answer}

**{PAA question 2}**
{Answer}

**{PAA question 3}**
{Answer}

## Get started
{Brand A} CTA — {link}
{Brand B} CTA — {link if balanced; otherwise drop}

## Schema
See `schema.jsonld` — paste into `<head>`.
```

## Tips

- **Balance is conversion.** Pages that pretend the user's product is always better lose trust and rankings. Honest assessments outperform partisan ones.
- Respect rate limit. Step 5 (fetching top 3 SERP winners) takes 3 WebFetch calls + earlier MCP queries.
- Cost: ~15–25 SE Ranking credits typical, +3 Firecrawl credits for the schema/og benchmark in step 5, +1 Firecrawl credit per URL in step 5b (opt-in only). Pass `--no-firecrawl` to skip both Firecrawl steps.
- **Schema:** use `Product` for both products in a vs page, plus `BreadcrumbList`, plus `FAQPage` if the FAQ section is real Q&A (not a manufactured one).
- For "alternatives to X" pages, position the user's product as one of N (typically 5–10), not as #1. Numbered listicles convert better than self-promotional alternatives pages.
- For "best X for Y" pages, segment by use case explicitly — "best for solo developers" vs "best for enterprise teams" — this lets you win multiple long-tail variants.
- Run `seo-page` on the published page after 30 days to track ranking trajectory.
- Pair with `seo-content-audit` to E-E-A-T-check the draft before publishing.
- The PAA-derived FAQ in step 4 is gold — those are the questions users are actually searching, and answering them in-page raises citation-readiness for AIO.
- Don't auto-publish. Hand the draft to a writer/PM for fact-checking and brand-voice tuning.

## 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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