aeo-prompt-frequency-analyzer
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.
Informations de source
- Dépôt
- modbender/skill-library-mcp
- Dernière activité de la source
- 6 mars 2026 à 18:33
- Langue détectée de SKILL.md
- anglais
- Étoiles
- 12
- Forks
- 3
Options d'installation
Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.
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Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.
Affichage de SKILL.md
SKILL.md
Instructions source · Aperçu en lecture seule- 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.
## Usage
```bash
GEMINI_API_KEY=$(security find-generic-password -s "nano-banana-pro" -w) \
python3 scripts/analyze.py "your prompt here" [--runs 10] [--model gemini-2.5-pro] [--concurrency 5] [--output text|json]
```
Run from the skill directory. Resolve `scripts/analyze.py` relative to this SKILL.md.
## Options
- `--runs N` — Number of times to run the prompt (default: 10)
- `--model NAME` — Gemini model to use (default: gemini-2.5-pro)
- `--concurrency N` — Max parallel API calls (default: 5; keep ≤5 to avoid rate limits)
- `--output text|json` — Output format (default: text)
## Output
Reports for each unique search query:
- Frequency percentage (how many runs used that query)
- Raw count
- Top web sources referenced
## Notes
- Gemini API key must be in `GEMINI_API_KEY` env var (stored in macOS Keychain under `nano-banana-pro`)
- Each run is independent — Gemini may use different search queries each time
- Retries failed requests up to 3 times with exponential backoff
- Use `--output json` for programmatic consumption
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