ECommerce-Skills amazon-review-checker

Amazon review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes time clustering detection, content similarity analysis, rating distribution checks, and verified purchase validation. Progressive analysis with L1-L4 depth levels. No API key required.

install

source · Clone the upstream repo

git clone https://github.com/nexscope-ai/eCommerce-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/nexscope-ai/eCommerce-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/review-checker/amazon-review-checker" ~/.claude/skills/nexscope-ai-ecommerce-skills-amazon-review-checker && rm -rf "$T"

manifest: review-checker/amazon-review-checker/SKILL.md

source content

Amazon Review Checker 🔍

Review authenticity analyzer — detect fake reviews, suspicious patterns, and rating manipulation.

Installation

npx skills add nexscope-ai/eCommerce-Skills --skill amazon-review-checker -g

Features

Authenticity Score — 0-100 comprehensive rating
Suspicious Pattern Detection — Time clustering, content similarity, rating anomalies
Fake Review Flagging — Mark high-risk reviews with explanations
Progressive Analysis — More data = deeper insights

Progressive Analysis Levels

Level	Required Data	Unlocked Analysis
L1 Basic	Review content	Similarity, length, keywords
L2 Advanced	+ Review date	Time clustering detection
L3 Deep	+ Star rating	Rating distribution analysis
L4 Complete	+ VP status	Verified purchase validation

Detection Dimensions

Dimension	Weight	Method
Time Clustering	25%	Sliding window + burst detection
Content Similarity	20%	N-gram + Jaccard similarity
Rating Distribution	20%	Chi-square test vs natural distribution
VP Ratio	15%	Compare to category benchmark
Review Length	5%	Entropy analysis
Suspicious Keywords	5%	Keyword pattern matching

Risk Levels

Score	Level	Description
70-100	✅ Low Risk	Reviews appear authentic
50-69	⚠️ Medium Risk	Some concerns found
30-49	🔴 High Risk	Multiple red flags
0-29	💀 Critical	Likely mass fake reviews

Usage

Method 1: Paste Reviews

Paste reviews directly in conversation:

Check these reviews:

5 stars - Great product! Works perfectly.
5 stars - Amazing! Best purchase ever.
1 star - Not as described.

Method 2: JSON Input

python3 scripts/analyzer.py '[
  {"content": "Great product!", "rating": 5, "date": "2024-01-15", "verified_purchase": true},
  {"content": "Amazing!", "rating": 5, "date": "2024-01-15", "verified_purchase": false}
]'

Method 3: Demo Mode

python3 scripts/analyzer.py --demo

Output Example

📊 Review Authenticity Report

ASIN: B08XXXXX
Reviews: 10
Analysis Level: L4

━━━━━━━━━━━━━━━━━━━━━━━━

Authenticity Score: 66/100 ⚠️

Medium Risk - Some concerns found.

━━━━━━━━━━━━━━━━━━━━━━━━

Detection Dimensions

🔴 Time Clustering: 70/100
   Max 6 reviews within 48h

✅ Content Similarity: 24/100
   Found 0 highly similar review groups

━━━━━━━━━━━━━━━━━━━━━━━━

High-Risk Reviews (Top 3)

1. Risk 75% - "Perfect!"
   Reason: Too short, non-VP, templated 5-star

🔍 Want more accurate analysis? Add:
• Reviewer info → Unlock "Account Profile Analysis"

Interaction Flow

User Input (any format)
        ↓
Smart field detection
        ↓
Analyze with available data
        ↓
Results + depth suggestions
        ↓
User continues or ends

Part of Nexscope AI — AI tools for e-commerce sellers.