# Geo Query Finder

> Encuentra qué consultas de búsqueda en ChatGPT mencionan una marca dada, probando queries long-tail contra el modelo de ChatGPT con búsqueda web activada.

Fuente: https://skillsagentes.com/skills/openclaudia/openclaudia-skills/geo-query-finder
Markdown: https://skillsagentes.com/skills/openclaudia/openclaudia-skills/geo-query-finder.md

Repositorio: https://github.com/OpenClaudia/openclaudia-skills
Autor: OpenClaudia
Licencia: MIT
Actualizado: hace 3 meses
Coste de contexto: 89 tok instalada, 1.6k tok al activarse, 1.6k tok con todos los archivos del bundle
Bundle: 1 archivo, 6 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 geo-query-finder --agent claude-code
# Cursor
npx -y skills add OpenClaudia/openclaudia-skills --skill geo-query-finder --agent cursor
# Codex
npx -y skills add OpenClaudia/openclaudia-skills --skill geo-query-finder --agent codex
# Gemini CLI
npx -y skills add OpenClaudia/openclaudia-skills --skill geo-query-finder --agent gemini
# Windsurf
npx -y skills add OpenClaudia/openclaudia-skills --skill geo-query-finder --agent windsurf
# Cline
npx -y skills add OpenClaudia/openclaudia-skills --skill geo-query-finder --agent cline
```

## Qué hace

- Comprueba mentiones ya indexadas de la marca en LLMs vía DataForSEO (Google AI Overview y ChatGPT) antes de gastar en pruebas especulativas
- Genera 15-20 consultas long-tail (features, B2B, problemas, comparación, casos de uso) sobre una marca
- Envía cada consulta al modelo gpt-4o-search-preview de OpenAI con búsqueda web activada
- Detecta si la marca aparece en la respuesta, en qué posición y con qué contexto
- Genera una tabla de resultados con recomendaciones de contenido según menciones y huecos detectados

## Cuándo usarla

- El usuario pide 'find queries for [brand]'
- El usuario pregunta 'check GEO visibility'
- El usuario pregunta 'which queries mention [brand]'
- El usuario pide 'geo query finder', 'find AI mentions' o 'test ChatGPT queries for [brand]'

## Qué la activa

- "Encuentra qué consultas de ChatGPT mencionan a Acme Corp"
- "Revisa la visibilidad GEO de mi marca en ChatGPT"
- "¿Qué queries mencionan a mi empresa en las respuestas de IA?"
- "Prueba estas consultas en ChatGPT para ver si aparece mi marca"

## Antes de instalar

- Requiere las variables de entorno DATAFORSEO_LOGIN/DATAFORSEO_PASSWORD y OPENAI_API_KEY, y cada consulta cuesta ~$0.01 vía la API de OpenAI.
- Necesita en el PATH: curl
- Variables de entorno: AUTH, DATAFORSEO_LOGIN, DATAFORSEO_PASSWORD, OPENAI_API_KEY
- makes network requests
- needs API credentials

## Archivos

- SKILL.md — 6 KB

## SKILL.md

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

# GEO Query Finder

Find which ChatGPT search queries mention a given brand. Tests long-tail queries against ChatGPT's web-search-enabled model and reports which ones surface the brand.

## Trigger

Use when the user asks to "find queries for [brand]", "check GEO visibility", "which queries mention [brand]", "geo query finder", "find AI mentions", or "test ChatGPT queries for [brand]".

## Usage

```
/geo-query-finder <brand_name> [--industry <industry>] [--features <feature1,feature2,...>] [--queries <custom_query1;custom_query2;...>]
```

**Examples:**
- `/geo-query-finder "Acme Corp"` — auto-researches the brand and generates queries
- `/geo-query-finder "Acme Corp" --industry "smart TV OS" --features "white-label,voice-control,OEM licensing"`
- `/geo-query-finder "Acme Corp" --queries "best regulatory AI;eCTD validation tool;pharma compliance software"`

## How It Works

### Step 0: Pull pre-indexed LLM mentions (DataForSEO) — do this FIRST

Before generating speculative queries, check if DataForSEO already has indexed mentions for the brand's domain. If it does, you get ground-truth queries with search volume in one call instead of burning OpenAI dollars guessing.

Auth via `DATAFORSEO_LOGIN` / `DATAFORSEO_PASSWORD` environment variables.

```bash
AUTH=$(printf '%s' "$DATAFORSEO_LOGIN:$DATAFORSEO_PASSWORD" | base64)
# Google AI Overview citations
curl -s -X POST "https://api.dataforseo.com/v3/ai_optimization/llm_mentions/search/live" \
  -H "Authorization: Basic $AUTH" -H "Content-Type: application/json" \
  -d '[{"target":[{"domain":"<DOMAIN>","search_filter":"include","include_subdomains":true}],"platform":"google","limit":700}]'
# ChatGPT citations (substitute "platform":"chat_gpt")
```

**Critical flags:**
- `"include_subdomains": true` — without it, apex domains return 0 results (www.X treated as a different domain).
- Omit `location_code` to get global results; add `"location_code": 2840` only to scope to US.
- `platform` options: `"google"` (AI Overview), `"chat_gpt"`. Perplexity is NOT supported via this dataset.

**Extract from each `items[]`:**
- `question` — the real search query where the brand was cited
- `ai_search_volume` — monthly AI search volume (use to prioritize)
- `sources[]` — entries with `domain` matching the brand have the exact cited URL
- `location_code`, `language_code`, `model_name` — for geo/locale breakdown
- `answer` — the LLM answer text (for context)

**Decision rule:**
- If ≥20 queries returned → skip Steps 1–4 entirely; report these as ground-truth mentions and focus Step 5 on gap analysis (sort by volume, find URL-section winners like `/guides/` vs `/tools/`).
- If <20 queries → use them as seed input for Step 2 (generate variations of the query themes DataForSEO already confirmed), then run Steps 3–4 only on the gaps.
- If 0 queries → the domain has no AI citations; proceed with the original Steps 1–5 (speculative testing) as fallback.

### Step 1: Research the Brand
If no `--industry` or `--features` provided, use web search to understand:
- What the brand does / what industry it's in
- Key differentiators vs competitors
- Unique features that competitors DON'T have

### Step 2: Generate Long-Tail Queries
Generate 15-20 long-tail queries across these categories:
1. **Feature-specific** (unique capabilities only this brand has)
2. **B2B/decision-maker** (queries from buyers, not consumers)
3. **Problem-solving** ("how to X without Y")
4. **Comparison/alternative** ("alternative to [dominant player]")
5. **Use-case specific** (niche scenarios where the brand excels)

Avoid generic queries where dominant players will always win.

### Step 3: Query ChatGPT via OpenAI Search API

Use OpenAI's `gpt-4o-search-preview` model with web search enabled:

```bash
OPENAI_API_KEY from environment variable
```

```python
import json, os, urllib.request, ssl

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]

data = json.dumps({
    "model": "gpt-4o-search-preview",
    "web_search_options": {"search_context_size": "medium"},
    "messages": [{"role": "user", "content": "<query>"}],
    "max_tokens": 1000
}).encode()

req = urllib.request.Request(
    "https://api.openai.com/v1/chat/completions",
    data=data,
    headers={
        "Authorization": f"Bearer {OPENAI_API_KEY}",
        "Content-Type": "application/json"
    }
)

resp = urllib.request.urlopen(req, context=ssl.create_default_context(), timeout=45)
result = json.loads(resp.read())
answer = result["choices"][0]["message"]["content"]
```

### Step 4: Check Mentions

For each query, check if the brand name (or known aliases) appears in ChatGPT's response:
- Check case-insensitive match
- Check variations (with/without spaces, dots, hyphens)
- If mentioned, extract the surrounding context (200 chars around the mention)
- Note the position (is it #1 recommended? listed among many? mentioned in passing?)

### Step 5: Report Results

Output a summary table:

```
## GEO Query Finder Results: [Brand Name]

### Mentioned (X/N queries)
| Query | Position | Context |
|-------|----------|---------|
| ... | #1 | "Brand is the leading..." |

### Not Mentioned (Y/N queries)
| Query | What ChatGPT Recommended Instead |
|-------|----------------------------------|
| ... | Competitor A, Competitor B |

### Recommendations
- Queries where brand is ALREADY mentioned: create more authoritative content to maintain/improve position
- Queries where brand is NOT mentioned but SHOULD be: these are content gaps — create targeted pages
- Queries to AVOID: too generic, dominated by big players, not worth the effort
```

## Rate Limiting

- Run queries sequentially with 1-2 second delays to avoid rate limits
- Each query costs ~$0.01 via OpenAI API
- Default: 15-20 queries per run (~$0.15-0.20 per run)

## Notes

- Results reflect ChatGPT with web search enabled (grounded in real-time web results)
- Results may vary slightly between runs due to search freshness
- This tests ChatGPT specifically — Gemini and Copilot may give different results
- For ongoing monitoring, consider scheduling periodic runs to track visibility changes over time

## 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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