Skill: phheng/amazon-review-checker (version 0.1.0)

--- 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 ```bash 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 ```bash 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 ```bash 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](https://www.nexscope.ai/) — AI tools for e-commerce sellers.**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)