dealer-ai-sentiment-monitorlisted

Audit how AI search engines (ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Copilot) describe and characterize a car dealership. Use when the user asks to "audit AI brand sentiment for my dealer," "what is ChatGPT saying about my dealership," "audit Perplexity sentiment on my Buick GMC store," "monitor AI tone on my dealership," "is Gemini badmouthing my dealer," "check AI engine reputation," "audit how AI describes my dealer vs competitors," "AI brand sentiment for car dealers," "are AI engines accurate about my dealership," "audit AI hallucinations about my dealer," or any request to assess the tone, accuracy, and competitive framing that AI engines use when answering questions about the dealership. Distinct from dealer-customer-sentiment-analyzer (which reads Google reviews) and dealer-ai-visibility (which tracks citation presence). This skill reads what AI engines SAY about the dealership in natural-language form, classifies sentiment, flags hallucinations, and identifies remediation paths. Authored
arielcoro/dealer-ai-skills · ★ 1 · AI & Automation · score 65

Install: claude install-skill arielcoro/dealer-ai-skills

# Dealer AI Sentiment Monitor Audit how AI search engines describe and characterize a car dealership when shoppers ask about it. This skill complements dealer-ai-visibility (which tracks whether the dealership is cited at all) by reading the natural-language responses AI engines produce and classifying the sentiment, factual accuracy, and competitive framing. Dealer-relevant AI engines covered: - ChatGPT (OpenAI) - Perplexity - Google Gemini and Google AI Mode (AI Overviews) - Claude (Anthropic) - Microsoft Copilot - Grok (xAI) — emerging, optional The output is a structured assessment: per-engine sentiment, accuracy of factual claims, competitive comparisons the engine draws, hallucinations or stale information detected, and a prioritized remediation plan to influence what AI engines will say in the future. The audit framework and scoring rubric are in `REFERENCE.md`. The specific prompt set and per-engine procedures are in `CHECKS.md`. ## What this audits Six dimensions, 100 points total: 1. **Tone** — 18 pts 2. **Factual accuracy** — 22 pts 3. **Strength framing** — 15 pts 4. **Concern framing** — 15 pts 5. **Competitive positioning** — 15 pts 6. **Hallucination and stale-data risk** — 15 pts For each dimension, the audit runs a defined prompt set against each AI engine, captures the response, classifies the output, and scores per the framework in REFERENCE.md. ## When to invoke Invoke this skill when the user wants to: - Diagnose how AI engines currently descr