AEO Analytics Free

Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks mention/citation rates over time, detects trends, and identifies opportunities. Uses Gemini API free tier (with grounding) as primary method, web search as fallback. Use when a user wants to: check if AI models mention their brand, track AI citation changes over time, measure AEO content effectiveness, monitor competitor AI visibility, or audit their brand's presence in AI-generated answers. Pairs with aeo-prompt-research-free (identifies prompts) and aeo-content-free (creates/refreshes content). This skill closes the loop by measuring results.

psyduckler@psyduckler

Install

openclaw skills install @psyduckler/aeo-analytics-free

AEO Analytics (Free)

Source: github.com/psyduckler/aeo-skills Part of: AEO Skills Suite — Prompt Research → Content → Analytics

Track whether AI assistants mention and cite your brand — and how that changes over time.

Requirements

Primary: Gemini API key (free from aistudio.google.com) — enables grounding with source data
Fallback: web_search only — weaker signal but zero API keys needed
web_fetch — optional, for deeper analysis of cited pages

Input

Domain (required) — the brand's website (e.g., tabiji.ai)
Brand names (required) — names to search for in responses (e.g., ["tabiji", "tabiji.ai"])
Prompts (required for first scan) — list of target prompts to track. Can come from aeo-prompt-research-free output.
Data file path (optional) — where to store scan history. Default: aeo-analytics/<domain>.json

Commands

The skill supports three commands:

`scan` — Run a new visibility scan

Execute all tracked prompts against the AI model and record results.

`report` — Generate a visibility report

Analyze accumulated scan data and produce a formatted report.

`add-prompts` / `remove-prompts` — Manage tracked prompts

Add or remove prompts from the tracking list.

Scan Workflow

Step 1: Load or Initialize Data

Check if a data file exists for this domain. If yes, load it. If no, create a new one. See references/data-schema.md for the full JSON schema.

Step 2: Run Prompts

For each tracked prompt:

Method A — Gemini API with grounding (preferred): See references/gemini-grounding.md for API details.

Send prompt to Gemini API with googleSearch tool enabled
From the response, extract:
- Response text — the AI's answer
- Grounding chunks — the web sources cited (URLs + titles)
- Web search queries — what the AI searched for
Analyze the response:
- Mentioned? — Search response text for brand names (case-insensitive, word-boundary match)
- Mention excerpt — Extract the sentence(s) containing the brand name
- Cited? — Check if brand's domain appears in any grounding chunk URI
- Cited URLs — List the specific brand URLs cited
- Sentiment — Classify the mention context as positive/neutral/negative
- Competitors — Extract other brand names and domains from response + citations

Method B — Web search fallback (if no Gemini API key):

web_search the exact prompt text
Check if brand's domain appears in search results
Record as "web-proxy" method (less direct than grounding)

Step 3: Save Results

Append the scan results to the data file. Never overwrite previous scans — history is the whole point.

Step 4: Quick Summary

After scanning, output a brief summary:

Prompts scanned
Current mention rate and citation rate
Change vs. last scan (if applicable)
Any notable changes (new mentions, lost citations)

Report Workflow

Per-Prompt Detail

For each tracked prompt, show:

text

1. "[prompt text]"
   Scans: [total] (since [first scan date])
   Mentioned: [count]/[total] ([%]) — [trend arrow] [trend description]
   Cited: [count]/[total] ([%])
   Latest: [✅/❌ Mentioned] + [✅/❌ Cited]
   Sentiment: [positive/neutral/negative]
   Competitors mentioned: [list]

If mentioned in latest scan, include the mention excerpt. If not mentioned, note which sources were cited instead and rate the opportunity (HIGH/MEDIUM/LOW).

Summary Section