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/install amazon-review-checker
Description
Amazon review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes time clustering detection, content similarit...
README (SKILL.md)
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.
Usage Guidance
This package appears coherent for analyzing Amazon reviews, but take these precautions before running it: 1) Inspect the included Python files locally (they are bundled) before execution — they appear to be plain parsing/analysis code with no network calls. 2) Run scripts in a controlled environment (virtualenv or sandbox) especially if you plan to supply large or unknown inputs. 3) Be careful pasting real review text that contains reviewer names, emails or other PII — the tool will process whatever you provide. 4) If you choose to run the npx command from SKILL.md, remember npx will fetch code from npm (network) — verify the package/source and prefer running the bundled scripts directly if you trust the bundled files. 5) The HTML report loads Chart.js from a CDN when opened in a browser; if you need offline or air-gapped operation, change or remove that reference. 6) If you want higher assurance, run static scans on the code or execute it in a restricted environment and observe network activity before providing any sensitive data.
Capability Analysis
Type: OpenClaw Skill
Name: amazon-review-checker
Version: 0.1.0
The amazon-review-checker skill bundle is a legitimate tool designed to analyze the authenticity of Amazon reviews. The core logic in scripts/analyzer.py and scripts/parser.py uses standard heuristics like N-gram similarity, time-burst detection, and rating distribution analysis to identify suspicious patterns. The code relies on standard Python libraries (json, csv, re) and does not perform any network requests, file exfiltration, or unauthorized command execution. The SKILL.md file provides clear instructions for the AI agent that are strictly aligned with the stated purpose of review analysis.
Capability Assessment
Purpose & Capability
The bundled Python scripts (parser, analyzer, HTML report generator) implement review parsing, multiple detection dimensions, and report output that match the skill description. There are no unexpected credentials, binaries, or unrelated dependencies declared.
Instruction Scope
SKILL.md keeps runtime instructions scoped to parsing pasted or JSON reviews and running the local scripts. It does recommend an npx-based installer command (which would fetch a package from the network) and hints at additional 'Account Profile Analysis' if more reviewer data is provided — those are optional and not implemented in the included code. The skill will process any review text you paste, which may include reviewer names or other PII provided by you.
Install Mechanism
There is no formal install spec in the package (instruction-only), so nothing is automatically written to disk by the registry. The README suggests using an npx command to add the skill from an external package (npx will fetch code from npm). The included code itself does not perform network calls; only the generated HTML references Chart.js via a CDN when viewed in a browser.
Credentials
The skill requires no environment variables, credentials, or config paths. The Python code does not access environment variables or external services. The only external resource is a Chart.js CDN link embedded in the generated HTML (client-side).
Persistence & Privilege
The skill is not marked always:true and does not request persistent system privileges. It does not modify other skills or system-wide agent configuration. It generates a local HTML file if instructed, which is normal for reporting tools.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install amazon-review-checker - After installation, invoke the skill by name or use
/amazon-review-checker - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Amazon Review Checker v1.0.0 — Initial release
- Detects fake reviews, suspicious patterns, and rating manipulation on Amazon products.
- Supports progressive analysis from basic (content-only) to complete (with rating, date, and verified purchase).
- Calculates an authenticity score (0–100) with risk level classification.
- Flags high-risk reviews and provides explanations.
- Multiple input methods supported: pasted text, JSON, demo mode.
- No API key required; works offline.
Metadata
Frequently Asked Questions
What is Amazon Review Checker?
Amazon review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes time clustering detection, content similarit... It is an AI Agent Skill for Claude Code / OpenClaw, with 157 downloads so far.
How do I install Amazon Review Checker?
Run "/install amazon-review-checker" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Amazon Review Checker free?
Yes, Amazon Review Checker is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Amazon Review Checker support?
Amazon Review Checker is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Amazon Review Checker?
It is built and maintained by Henk Nie (@phheng); the current version is v0.1.0.
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