| name | aeo-prompt-frequency-analyzer |
| description | Analyze what search queries Gemini uses when answering a prompt, by running it multiple times with Google Search grounding and reporting frequency distribution. Use when investigating AEO query patterns, understanding how AI models search the web for a topic, or studying the probabilistic nature of AI-triggered search queries. |
Prompt Frequency Analyzer
Run a prompt N times against Gemini with Google Search grounding enabled. Collect and report the frequency of search queries Gemini generates across all runs.
Why Gemini 3 Flash? This is the model that powers Google Search AI Mode and AI Overviews โ the most important answer engine for AEO. Running prompts through Gemini 3 Flash with grounding simulates what Google's AI actually does when users ask questions. 20 samples provides reliable frequency distribution for directional insights.
The Retrieval Framework: Influence happens at retrieval, not inside the model. You can't edit a model's training data โ but you can enter the "candidate set" the model selects from when it searches the web. Gemini is search-first: it fires real Google Search queries before nearly every answer, making it more influenceable than GPT. This tool reveals the recurring retrieval set โ the queries, sources, and themes Gemini consistently draws from. Understanding query frequency is the first step to entering that set.
Usage
GEMINI_API_KEY="$GEMINI_API_KEY" python3 scripts/analyze.py "your prompt here" [--runs 20] [--model gemini-3-flash-preview] [--concurrency 5] [--output text|json]
Run from the skill directory. Resolve scripts/analyze.py relative to this SKILL.md.
Options
--runs N โ Number of times to run the prompt (default: 20; 20 samples gives good directional signal)--model NAME โ Gemini model to use (default: gemini-3-flash-preview โ the model powering Google AI Overviews)--concurrency N โ Max parallel API calls (default: 5; keep โค5 to avoid rate limits)--output text|json โ Output format (default: text)
Output
Reports for each unique search query:
- Frequency percentage (how many runs used that query)
- Raw count
- Intent classification โ each query is classified as
informational, commercial, navigational, or transactional
- Intent distribution summary โ breakdown of query intents across all unique queries
- Top web sources referenced
Intent Classification
Every search query is automatically classified by intent:
- informational โ knowledge-seeking queries ("what is X", "how does X work", "X explained")
- commercial โ evaluation/comparison queries ("best X", "X vs Y", "top X for", "X review")
- navigational โ brand/site-specific queries (contains domain names, "X login", "X website")
- transactional โ purchase/action queries ("buy X", "X discount", "X free trial", "download X")
Example output:
Search Query Frequency:
85% (17/20) [commercial] โ best seo tools 2026
60% (12/20) [informational] โ how seo tools work
40% (8/20) [navigational] โ semrush.com features
20% (4/20) [transactional] โ seo tools free trial
Intent Distribution:
45% informational, 30% commercial, 15% navigational, 10% transactional
Use intent data to understand what kind of content enters the recurring retrieval set. Each intent type maps to a content format the model searches for:
- informational โ explanatory/educational content (guides, explainers)
- commercial โ comparison/review content (vs pages, best-of lists)
- navigational โ brand/product pages (homepages, feature pages)
- transactional โ conversion pages (pricing, free trial, download)
If 60% of queries are commercial, the model is searching for comparison content โ and that's the content type you need to create to enter the candidate set.
Further Reading
Notes
- Gemini API key must be in
GEMINI_API_KEY env var (stored in macOS Keychain under google-api-key)
- Each run is independent โ Gemini may use different search queries each time
- Retries failed requests up to 3 times with exponential backoff
- Use
--output json for programmatic consumption
