A

amazon-review-checker

Di:nexscope-ai

Amazon review authenticity — fake reviews, time clustering, verified purchase validation.

Installazione

Invia questo comando al tuo agente AI:

npx skills add https://github.com/nexscope-ai/eCommerce-Skills/tree/main/review-checker/amazon-review-checker --skill amazon-review-checker

Documentazione

---

name: amazon-review-checker

version: 1.0.0

description: "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."

metadata: {"nexscope":{"emoji":"🔍","category":"ecommerce"}}

---

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)

  • 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.

    Collegamenti

    GitHub