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160 · Marketing · Generative Engine Optimization

GEO Query Finder

Develops a research plan for AI-search and generative-engine queries.

geogenerative-engine-optimizationai-searchquery-researchki-suche

GEO Query Finder · Original SKILL.md

---
name: geo-query-finder
description: >
  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. 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]".
---

# 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

When to use

Develops a research plan for AI-search and generative-engine queries.

What you get

The complete original guide with source attribution and explicit delivery limits.

How it works

Clarify evidence and scope, apply the method as planning guidance, then separate recommendations from any authorized external action.

Requirements

Task context, user-supplied or authorized evidence, constraints and an accountable owner for any external work.

Delivery and review notes

Original documentation is published under the repository MIT license at the pinned revision. It is reference guidance only. Referenced API keys, OAuth tokens, environment files, CLIs, scripts, runtimes and third-party services are not available, read or executed. Loading this source does not authorize credential use, external requests, account changes, content publication, messages, payment, deployment, installation or script execution.

Starting prompt

Use GEO Query Finder for [TASK]. Establish the evidence, permissions and constraints first. Return a bounded, reviewable plan with assumptions and open questions. Do not use credentials, call services or perform external actions.

Not for

Reading credentials, issuing API calls, installing tools, publishing content or making account changes.

What M11 added

M11 added German routing, source integrity and runtime boundaries. Original documentation is published under the repository MIT license at the pinned revision. It is reference guidance only. Referenced API keys, OAuth tokens, environment files, CLIs, scripts, runtimes and third-party services are not available, read or executed. Loading this source does not authorize credential use, external requests, account changes, content publication, messages, payment, deployment, installation or script execution.

Original authorship remains with OpenClaudia/openclaudia-skills · Original source ↗

Original license & copyright

MIT License

Copyright (c) 2026 OpenClaudia

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Source SHA-256: ecf746382569c50b950999ba97032763bbee06a8293e284c51c7300cdcf97352
Snapshot checked: 2026-09-28T18:31:39.085Z

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