Geo Query Finder

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  [--industry ] [--features ] [--queries ]

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.

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":"","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 "}],

"max_tokens": 1000 }).encode()

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

resp = urllib.request.urlopen(req, context=ssl.createdefaultcontext(), 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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [OpenClaudia](https://github.com/OpenClaudia)
- **Source:** [OpenClaudia/openclaudia-skills](https://github.com/OpenClaudia/openclaudia-skills)
- **License:** MIT
- **Homepage:** https://openclaudia.com

Install and usage instructions live in the source repository linked above.