SkillHub

aeo-analytics-free

v1.0.0

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...

Sourced from ClawHub, Authored by psyduckler

Installation

Please help me install the skill `aeo-analytics-free` from SkillHub official store. npx skills add 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.

  1. Send prompt to Gemini API with googleSearch tool enabled
  2. From the response, extract:
  3. Response text — the AI's answer
  4. Grounding chunks — the web sources cited (URLs + titles)
  5. Web search queries — what the AI searched for

  6. Analyze the response:

  7. Mentioned? — Search response text for brand names (case-insensitive, word-boundary match)
  8. Mention excerpt — Extract the sentence(s) containing the brand name
  9. Cited? — Check if brand's domain appears in any grounding chunk URI
  10. Cited URLs — List the specific brand URLs cited
  11. Sentiment — Classify the mention context as positive/neutral/negative
  12. 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