ClawHub

AEO Analytics Free

Prompts

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.

Install

openclaw skills install 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.

  1. Send prompt to Gemini API with googleSearch tool enabled

  2. 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

  3. 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):

  1. web_search the exact prompt text
  2. Check if brand's domain appears in search results
  3. 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:

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

VISIBILITY SCORE
  Brand mentioned: [X]/[total] prompts ([%]) in latest scan
  Brand cited: [X]/[total] prompts ([%]) in latest scan

TRENDS (last [N] days, [N] scans)
  Mention rate: [%] → [trend]
  Citation rate: [%] → [trend]
  Most improved: [prompt] ([old rate] → [new rate])
  Most volatile: [prompt] (mentioned [X]/[N] scans)
  Consistently absent: [list of prompts never mentioned]

COMPETITOR SHARE OF VOICE
  [Competitor 1] — mentioned in [X]/[total] prompts
  [Competitor 2] — mentioned in [X]/[total] prompts
  [Brand] — mentioned in [X]/[total] prompts

NEXT ACTIONS
  → [Prioritized recommendations based on gaps and trends]

Recommendations Logic

  • High opportunity: Prompt has 0% mention rate + no strong owner in citations → create content
  • Close to winning: Prompt has mentions but no citations → refresh content for citation-worthiness
  • Volatile: Mention rate between 20-60% → content exists but needs strengthening
  • Won: Mention rate >80% + citation rate >50% → maintain, monitor for decay

Data Management

  • Data file location: aeo-analytics/<domain>.json
  • Schema: see references/data-schema.md
  • Each scan appends to the scans array — never delete history
  • Prompts can be added/removed without affecting historical data
  • When adding new prompts, they start with 0 scans (no backfill)

Tips

  • Run scans at consistent intervals (weekly or biweekly) for meaningful trend data
  • After publishing new AEO content, wait 2-4 weeks for indexing before expecting changes
  • Gemini's grounding results can vary run-to-run — that's normal. Aggregate data over multiple scans is more reliable than any single result
  • Track 10-20 prompts max for a focused view. Too many dilutes the signal
  • This skill completes the AEO loop: Research (aeo-prompt-research-free) → Create/Refresh (aeo-content-free) → Measure (this skill) → repeat
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