Agent skill

Geo Query Finder

by OpenClaudia in OpenClaudia/openclaudia-skills

Find which ChatGPT search queries mention a given brand. An agent skill from OpenClaudia/openclaudia-skills.

MITAuto-check passedProductivity & Automation

Install Geo Query Finder

skills CLI

$ npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install OpenClaudia/openclaudia-skills geo-query-finder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/geo-query-finder .claude/skills/geo-query-finder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "geo-query-finder" agent skill from https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder into .claude/skills/geo-query-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-query-finder", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install OpenClaudia/openclaudia-skills geo-query-finder --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/geo-query-finder .agents/skills/geo-query-finder && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "geo-query-finder" agent skill from https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder into .agents/skills/geo-query-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-query-finder", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install OpenClaudia/openclaudia-skills geo-query-finder --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/geo-query-finder .cursor/skills/geo-query-finder && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "geo-query-finder" agent skill from https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder into .cursor/skills/geo-query-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-query-finder", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/OpenClaudia/openclaudia-skills.git --path skills/geo-query-finder

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install OpenClaudia/openclaudia-skills geo-query-finder --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/geo-query-finder .gemini/skills/geo-query-finder && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "geo-query-finder" agent skill from https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder into .gemini/skills/geo-query-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-query-finder", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install OpenClaudia/openclaudia-skills geo-query-finder

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/geo-query-finder .github/skills/geo-query-finder && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "geo-query-finder" agent skill from https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder into .github/skills/geo-query-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-query-finder", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install OpenClaudia/openclaudia-skills geo-query-finder --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/geo-query-finder .opencode/skills/geo-query-finder && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "geo-query-finder" agent skill from https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/geo-query-finder into .opencode/skills/geo-query-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-query-finder", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: geo-query-finder
GitHub stars: 713
Token cost: ~1.6k tokens
SKILL.md length: 554 words
Files: 1
Skills in repo: 74
Repo updated: Sep 18, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Find which ChatGPT search queries mention a given brand. An agent skill from OpenClaudia/openclaudia-skills.

Works in 6 steps: Pull pre-indexed LLM mentions… → Research the Brand → Generate Long-Tail Queries → …
The user asks to find queries for [brand]
SKILL.md covers Trigger, Usage, How It Works and Rate Limiting, plus 1 more section
Calls curl; reaches api.dataforseo.com and api.openai.com; needs OPENAI_API_KEY and DATAFORSEO_PASSWORD

What it does

Geo Query Finder is an agent skill from OpenClaudia/openclaudia-skills. 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]".

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering AI search optimization and Web search. It works with OpenAI. The repository describes itself as: 77 open-source marketing skills for Claude Code, Codex, and other AI coding agents. SEO, content, email, ads, analytics, and growth. The licence is MIT.

When your agent uses it

The user asks to find queries for [brand]
Check GEO visibility
Which queries mention [brand]
Geo query finder

Example prompts

“find queries for [brand]”
“check GEO visibility”
“which queries mention [brand]”
“/geo-query-finder”

Requirements

Python 3
A credential in OPENAI_API_KEY

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Pull pre-indexed LLM mentions (DataForSEO) — do this FIRST
Research the Brand
Generate Long-Tail Queries
Query ChatGPT via OpenAI Search API
Check Mentions
Report Results

What it can do on your machine

Read from SKILL.md and the folder at commit 28bf209. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- api.dataforseo.com
- api.openai.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- OPENAI_API_KEY
- DATAFORSEO_PASSWORD
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Geo Query Finder loads about 1.6k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 554 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~93
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from OpenClaudia/openclaudia-skills at commit 28bf209, republished under its MIT licence (© OpenClaudia). 554 words, ~1,631 tokens.

Download SKILL.mdSave it as .claude/skills/geo-query-finder/SKILL.md (or your agent's skills folder).

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

Show full SKILL.md (209 more words)Show less

Step 2: Generate Long-Tail Queries

Generate 15-20 long-tail queries across these categories:

Feature-specific (unique capabilities only this brand has)
B2B/decision-maker (queries from buyers, not consumers)
Problem-solving ("how to X without Y")
Comparison/alternative ("alternative to [dominant player]")
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

Files

Just SKILL.md in skills/geo-query-finder of OpenClaudia/openclaudia-skills.

Open the folder on GitHubat commit 28bf209

Compare with similar skills

Geo Query Finder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Geo Query Finder compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Geo Query Finder this skillOpenClaudia/openclaudia-skills	713	—	~1.6k	Automated safety check: Pass	MIT	23 days ago
Geo Fundamentalswasp-lang/wasp	19k	9 repos	~861	Automated safety check: Pass	MIT	today
Marketing OsYuzzyuk/marketing-os	540	—	~2.5k	Automated safety check: Pass	MIT	1 mo ago
Geoliangdabiao/GEO-Content-Optimizer-Skill	205	1 repos	~2.3k	Automated safety check: Notes	MIT	2 mo ago
Geo Optimizerliangdabiao/GEO-Content-Optimizer-Skill	205	—	~1.1k	Automated safety check: Pass	None	2 mo ago
SEO Auditshadcn-labs/agentcn	490	—	~598	Automated safety check: Pass	MIT	4 days ago

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Works with

OpenAI

Questions about Geo Query Finder

What does Geo Query Finder do?

Find which ChatGPT search queries mention a given brand. An agent skill from OpenClaudia/openclaudia-skills. Geo Query Finder is an agent skill from OpenClaudia/openclaudia-skills. Find which ChatGPT search queries mention a given brand.

When should I use Geo Query Finder?

Geo Query Finder fits situations like: the user asks to find queries for [brand]; check GEO visibility; which queries mention [brand]; geo query finder.

How do I install Geo Query Finder in Claude Code?

Run `npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a claude-code`. Or copy the skill folder (skills/geo-query-finder in OpenClaudia/openclaudia-skills) into .claude/skills/geo-query-finder in your project. Claude Code loads it when a task matches its description.

How do I install Geo Query Finder in Codex?

Run `npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a codex`. Or copy the skill folder (skills/geo-query-finder in OpenClaudia/openclaudia-skills) into .agents/skills/geo-query-finder in your project. Codex loads it when a task matches its description.

Can I use Geo Query Finder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OpenClaudia/openclaudia-skills --skill geo-query-finder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/geo-query-finder, .gemini/skills/geo-query-finder, .github/skills/geo-query-finder and .opencode/skills/geo-query-finder in your project.

What does Geo Query Finder need to run?

Going by SKILL.md and its folder, Geo Query Finder needs the command-line tools its instructions call (curl) and credentials named OPENAI_API_KEY and DATAFORSEO_PASSWORD. Our summary lists: Python 3; A credential in OPENAI_API_KEY.

Does Geo Query Finder access the network?

SKILL.md names 2 domains. In commands or code: api.dataforseo.com and api.openai.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Geo Query Finder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Geo Query Finder use?

Geo Query Finder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Geo Query Finder use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Geo Query Finder?

Skills that share tags, products or a category with Geo Query Finder: Geo Fundamentals (wasp-lang/wasp, 19k stars), Marketing Os (Yuzzyuk/marketing-os, 540 stars), Geo (liangdabiao/GEO-Content-Optimizer-Skill, 205 stars) and Geo Optimizer (liangdabiao/GEO-Content-Optimizer-Skill, 205 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Geo Query Finder?

OpenClaudia (a GitHub organization) maintains it in OpenClaudia/openclaudia-skills, which has 713 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on September 18, 2026.

Source: OpenClaudia/openclaudia-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Geo Query Finder

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

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SKILL.md

GEO Query Finder

Trigger

Usage

How It Works

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

Step 1: Research the Brand

Step 2: Generate Long-Tail Queries

Step 3: Query ChatGPT via OpenAI Search API

Step 4: Check Mentions

Step 5: Report Results

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Notes

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