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review-analysis

Categorize 1-3 star Amazon reviews into pain-point types and generate improvement recommendations.

685|104|Updated Mar 2, 2026
Search Tags:#mcp#amazon#pain-points#review-analysis#customer-service#llm-agent
One-click install
npx skills add https://github.com/liangdabiao/amazon-sorftime-research-MCP-skill --skill review-analysis-liangdabiao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-analysis
Source: https://github.com/liangdabiao/amazon-sorftime-research-MCP-skill/tree/main/.claude/skills/review-analysis
Command: npx skills add https://github.com/liangdabiao/amazon-sorftime-research-MCP-skill --skill review-analysis-liangdabiao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

An Amazon product review analysis tool that identifies key pain points, explains why products fail to meet customer expectations, and generates actionable improvement plans plus customer service templates.

Core Features & Use Cases

  • Automatically aggregates and analyzes 1-3 star reviews to categorize issues into defined pain-point types (Structure/assembly, Electronics, Design, Appearance, Description, and Service/Logistics).
  • Produces a prioritized pain-point matrix, root-cause analysis, concrete product and supply-chain improvement suggestions, and ready-to-send customer-service reply templates.
  • Outputs structured data and reports for downstream dashboards and product decisions, including Markdown reports and JSON artifacts for archiving.

Quick Start

Analyze reviews for a given ASIN to generate a pain-point report and ready-to-use customer-service templates.

Frequently Asked Questions about review-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract customer pain points from Amazon product reviews?▼

To extract customer pain points from Amazon product reviews, you can analyze 1-3 star feedback for a specific ASIN to categorize issues into types like Structure, Electronics, or Service. This generates a pain-point matrix and root-cause analysis for product improvements.

How do I generate customer service templates for negative Amazon feedback?▼

Generating customer service templates for negative Amazon feedback involves analyzing 1-3 star reviews to identify specific pain points. The analysis produces ready-to-send reply templates and prioritized product-improvement recommendations based on the root causes.

Can I categorize Amazon review issues by product structure and logistics using an LLM agent?▼

Yes, you can categorize Amazon review issues by structure, assembly, electronics, design, and logistics using an LLM agent. It processes low-star reviews via the Sorftime MCP API to output a structured JSON pain-point matrix and actionable recommendations.

What is the best way to analyze Amazon ASIN reviews for product improvement?▼

The best way to analyze Amazon ASIN reviews for product improvement is to aggregate 1-3 star ratings to identify defined pain-point categories. This yields a prioritized matrix, root-cause analysis, and supply-chain suggestions for data-driven product decisions.

Does Amazon review analysis work with structured JSON and Markdown report outputs?▼

Amazon review analysis works with structured JSON and Markdown report outputs by processing review data through an LLM agent. It archives the pain-point matrix and recommended actions as JSON artifacts and generates Markdown reports for downstream dashboards.

What types of Amazon review pain points can I identify for a specific ASIN?▼

For a specific ASIN, you can identify Amazon review pain points categorized into Structure/assembly, Electronics, Design, Appearance, Description, and Service/Logistics. This categorization applies to 1-3 star feedback to pinpoint why products fail customer expectations.

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