amazon-sorftime-research-category-skill_skill

Overview

This skill automates Amazon category selection and market research using a five-dimension scoring model to generate a Markdown analysis report. It runs a data-driven workflow (Top N by default = 20) to surface market scale, growth potential, competition, entry barriers, and profitability. Trigger via /category-selection or natural language requests like “analyze X category”.

How this skill works

The workflow searches for a category nodeId, fetches a category report (Top products and statistics), and retrieves product details, keywords, and historical trends as needed. Data is cleaned (control-character escape, encoding fixes), standardized, and analyzed against the five-dimensional scoring model. The skill outputs a detailed Markdown report plus JSON/Excel artifacts and an interactive HTML dashboard for visualization.

When to use it

• When evaluating whether to enter a specific Amazon category (US or other supported sites).
• Before sourcing or launching a new product to validate market size and competition.
• When you need Top-N product benchmarking and pricing or trend insights.
• To generate a reproducible research package (report.md, data.json, scores.json, dashboard).
• When you want automated category scoring using a consistent five-dimension model.

Best practices

• Prefer using a specific child NodeID when possible to avoid ambiguous category search.
• Run the workflow with a sensible Top N (20–50) to balance detail and API costs.
• Inspect execution.log and raw SSE response if parsing issues appear.
• Limit concurrent API calls to 3–5 to avoid throttling and improve stability.
• Validate .mcp.json or environment API key before running to prevent authentication errors.

Example use cases

• Quickly assess whether "Sofas" in US is worth entering and get Top20 product benchmarks.
• Compare growth and seasonality for "Kitchen" across 25-month trends before planning inventory.
• Identify categories with low review counts and favorable entry barriers for private label testing.
• Produce an investor-ready category analysis report with scores, KPI cards, and an HTML dashboard.
• Export Top-N product data and scores to Excel for internal team review.