Amazon Product Research Framework

Problem It Solves

Amazon sellers often have many product ideas but lack a repeatable way to compare them. Looking only at sales, price, or review count can miss seasonality, market concentration, FBA fees, certification requirements, and startup investment. This skill breaks selection into demand, competition, profit, market entry, and risk, then produces comparable scores and recommendations.

How It Works

It typically gathers public market data with web_search and web_fetch, then follows a structured workflow:
- Demand validation: search trends, Amazon autocomplete, community discussion, and seasonality.
- Competition assessment: result density, top brands, price bands, review distribution, and market fragmentation.
- Profit estimation: selling price, supplier cost, FBA fees, referral fees, marketing, and return cost.
- Entry and risk: MOQ, investment, certification, time to launch, cash flow, and regulatory exposure.
- Multi-marketplace coverage: supports US, UK, DE, JP, AU, and other listed sites.

It can conclude with a 7-10 pursue, 4-6 conditional, or 1-3 avoid recommendation, and can compare multiple products in a table.

Limitations

The skill relies on public data. Sales volume and competitor share are estimates, while pricing, inventory, and supplier details need verification. It is best used for early filtering and structured evaluation, not as a replacement for real-time marketplace data, seller central data, or legal and compliance review.