ai shopping visibility

Your Amazon listings were written for keywords, but buyers are now asking Rufus, ChatGPT, and Perplexity for recommendations. If your listing is missing structured attributes or fails to answer common review-mined questions, these assistants will skip your product or recommend a competitor. This skill audits your readiness and rewrites your copy to win the AI share of voice.

What it does

• Compliance auditing checks your listing against Amazon's latest rules, including the July 2026 75-character title limit.
• Review mining extracts unanswered buyer questions and pain points from 1-3 star reviews to identify content gaps.
• Technical SEO check verifies if your brand site allows AI crawlers like OAI-SearchBot and PerplexityBot to see your product data.
• Listing rewriting generates titles, Item Highlights, bullets, and backend terms mapped directly to buyer intent.
• Visibility probing creates a structured manual testing plan to measure real-world performance in Rufus and Google AI Mode.
• Professional reporting assembles audits and before/after comparisons into white-label PDF reports and client proposals.

How it works

1. Ingest listing data via ASIN, URL, or saved HTML to capture current attributes and backend terms.
2. Process buyer feedback by analyzing a provided reviews.txt to find the specific questions assistants need to answer.
3. Run the audit script to generate a 0-100 readiness score across six categories including compliance and attribute coverage.
4. Execute the rewrite following a specific playbook that maps every bullet point to a sourced product fact or buyer question.
5. Measure and report by using the probe plan to gather assistant responses and generating a formatted PDF for stakeholders.

Frameworks & tools

The toolset is built on Python 3 standard library scripts. It uses JSON-LD for site analysis and outputs data in Markdown, HTML, and PDF formats.

Why this beats prompting it yourself

Generic prompts often invent product features or ignore Amazon's strict character limits and "Item Highlights" requirements. This skill enforces hard policy rules and ensures every claim in the rewrite is grounded in your actual product specs or brand site data.

Use cases

• Audit an ASIN to see why Rufus is not recommending your product to buyers.
• Rewrite a catalog of listings to comply with the 75-character title policy.
• Create data-backed sales proposals for Amazon marketing agency clients.
• Monitor monthly visibility trends and share of voice against named competitors.

Known limitations

Rufus cannot be queried via automated scripts; measurement requires manual data entry using the generated probe plan. Captchas may require users to provide saved HTML files for analysis.