Review Summarizer

Scrape, analyze, and summarize product reviews from multiple platforms (Amazon, Google, Yelp, TripAdvisor). Extract key insights, sentiment analysis, pros/cons, and recommendations. Use when researching products for arbitrage, creating affiliate content, or making purchasing decisions.

Security Scan

The skill's description promises live, multi-platform scraping and analysis, but the shipped code uses only local mock data and contains mismatches with the stated capability — this incoherence warrants caution before use.

Purpose & Capability

The README/description claims the skill scrapes Amazon, Google, Yelp, and TripAdvisor and integrates with their APIs, but the actual scraping implementation (scripts/scrape_reviews.py) returns hard-coded MOCK_REVIEWS and contains only comments about 'In production, integrate with:' APIs. There are no network calls or API integrations, no required credentials declared, and no real scraping logic. That is a substantive mismatch between the advertised purpose and the provided capability.

Instruction Scope

SKILL.md instructs the agent/user to run the provided scripts with product URLs as if they will fetch live reviews. The scripts accept URL and platform arguments, but scrape_reviews.py uses local mock data and will not retrieve external reviews. The instructions do not ask for credentials or API keys (even though real integrations would normally require them), which makes the runtime instructions misleading for someone expecting live scraping. The scripts only read/write files relevant to their task and do not reference unrelated system files or env vars.

Install Mechanism

There is no install spec (instruction-only metadata) and the repository contains plain Python scripts. No external installers, downloads, or archive extraction are specified. Risk from installation mechanism is low.

Credentials

The skill declares no required environment variables, credentials, or config paths and none are needed by the included code. This is proportional to the actual (mock) implementation, but inconsistent with the written promise to integrate with third-party APIs — those would normally require credentials.

Persistence & Privilege

The skill does not request permanent presence (always: false) and contains no code that modifies other skills or system-wide agent settings. Scripts write output files as expected for a reporting tool; there is no evidence of elevated persistence or privilege requests.

What to consider before installing

This skill is internally inconsistent: it advertises multi-platform web scraping and API integration but the shipped code only uses local MOCK_REVIEWS and no network/API calls. Before installing or relying on it: 1) If you expect live scraping, inspect and modify scrape_reviews.py — implement proper API clients or HTTP scraping and add secure handling for any API keys (do not hard-code secrets). 2) Verify Terms of Service and legal/privacy implications for scraping each platform. 3) Run the scripts in a sandbox first—they only write local files but you should confirm they won't make network requests you didn't expect. 4) If you see a later version that adds network calls, review those additions carefully (endpoints, credential handling, and any obfuscated or remote-download logic). 5) Be cautious granting credentials: the skill currently asks for none, but a real scraper would need API keys — ensure those are minimal-scope and stored securely.

Overview

Automatically scrape and analyze product reviews from multiple platforms to extract actionable insights. Generate comprehensive summaries with sentiment analysis, pros/cons identification, and data-driven recommendations.

Core Capabilities

1. Multi-Platform Review Scraping

Supported Platforms:

• Amazon (product reviews)
• Google (Google Maps, Google Shopping)
• Yelp (business and product reviews)
• TripAdvisor (hotels, restaurants, attractions)
• Custom platforms (via URL pattern matching)

Scrape Options:

• All reviews or specific time ranges
• Verified purchases only
• Filter by rating (1-5 stars)
• Include images and media
• Max review count limits

2. Sentiment Analysis

Analyzes:

• Overall sentiment score (-1.0 to +1.0)
• Sentiment distribution (positive/neutral/negative)
• Key sentiment drivers (what causes positive/negative reviews)
• Trend analysis (sentiment over time)
• Aspect-based sentiment (battery life, quality, shipping, etc.)

3. Insight Extraction

Automatically identifies:

• Top pros mentioned in reviews
• Common complaints and cons
• Frequently asked questions
• Use cases and applications
• Competitive comparisons mentioned
• Feature-specific feedback

4. Summary Generation

Output formats:

• Executive summary (150-200 words)
• Detailed breakdown by category
• Pros/cons lists with frequency counts
• Statistical summary (avg rating, review count, etc.)
• CSV export for analysis
• Markdown report for documentation

5. Recommendation Engine

Generates recommendations based on:

• Overall sentiment score
• Review quantity and recency
• Verified purchase ratio
• Aspect-based ratings
• Competitive comparison

Quick Start

Summarize Amazon Product Reviews

# Use scripts/scrape_reviews.py
python3 scripts/scrape_reviews.py \
  --url "https://amazon.com/product/dp/B0XXXXX" \
  --platform amazon \
  --max-reviews 100 \
  --output amazon_summary.md

Compare Reviews Across Platforms

# Use scripts/compare_reviews.py
python3 scripts/compare_reviews.py \
  --product "Sony WH-1000XM5" \
  --platforms amazon,google,yelp \
  --output comparison_report.md

Generate Quick Summary

# Use scripts/quick_summary.py
python3 scripts/quick_summary.py \
  --url "https://amazon.com/product/dp/B0XXXXX" \
  --brief \
  --output summary.txt

Scripts

scrape_reviews.py

Scrape and analyze reviews from a single URL.

Parameters:

• --url: Product or business review URL (required)
• --platform: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)
• --max-reviews: Maximum reviews to fetch (default: 100)
• --verified-only: Filter to verified purchases only
• --min-rating: Minimum rating to include (1-5)
• --time-range: Time filter (7d, 30d, 90d, all) (default: all)
• --output: Output file (default: summary.md)
• --format: Output format (markdown, json, csv)

Example:

python3 scripts/scrape_reviews.py \
  --url "https://amazon.com/dp/B0XXXXX" \
  --platform amazon \
  --max-reviews 200 \
  --verified-only \
  --format markdown \
  --output product_summary.md

compare_reviews.py

Compare reviews for a product across multiple platforms.

Parameters:

• --product: Product name or keyword (required)
• --platforms: Comma-separated platforms (default: all)
• --max-reviews: Max reviews per platform (default: 50)
• --output: Output file
• --format: Output format (markdown, json)

Example:

python3 scripts/compare_reviews.py \
  --product "AirPods Pro 2" \
  --platforms amazon,google,yelp \
  --max-reviews 75 \
  --output comparison.md

sentiment_analysis.py

Analyze sentiment of review text.

Parameters:

• --input: Input file or text (required)
• --type: Input type (file, text, url)
• --aspects: Analyze specific aspects (comma-separated)
• --output: Output file

Example:

python3 scripts/sentiment_analysis.py \
  --input reviews.txt \
  --type file \
  --aspects battery,sound,quality \
  --output sentiment_report.md

quick_summary.py

Generate a brief executive summary.

Parameters:

• --url: Review URL (required)
• --brief: Brief summary only (no detailed breakdown)
• --words: Summary word count (default: 150)
• --output: Output file

Example:

python3 scripts/quick_summary.py \
  --url "https://yelp.com/biz/example-business" \
  --brief \
  --words 100 \
  --output summary.txt

export_data.py

Export review data for further analysis.

Parameters:

• --input: Summary file or JSON data (required)
• --format: Export format (csv, json, excel)
• --output: Output file

Example:

python3 scripts/export_data.py \
  --input product_summary.json \
  --format csv \
  --output reviews_data.csv

Output Format

Markdown Summary Structure

# Product Review Summary: [Product Name]

## Overview
- **Platform:** Amazon
- **Reviews Analyzed:** 247
- **Average Rating:** 4.3/5.0
- **Overall Sentiment:** +0.72 (Positive)

## Key Insights

### Top Pros
1. Excellent sound quality (89 reviews)
2. Great battery life (76 reviews)
3. Comfortable fit (65 reviews)

### Top Cons
1. Expensive (34 reviews)
2. Connection issues (22 reviews)
3. Limited color options (18 reviews)

## Sentiment Analysis
- **Positive:** 78% (193 reviews)
- **Neutral:** 15% (37 reviews)
- **Negative:** 7% (17 reviews)

## Recommendation
✅ **Recommended** - Strong positive sentiment with high customer satisfaction.

Best Practices

For Arbitrage Research

1. Compare across platforms - Check Amazon vs eBay seller ratings
2. Look for red flags - High return rates, quality complaints
3. Check authenticity - Verified purchases only
4. Analyze trends - Recent review sentiment vs older reviews

For Affiliate Content

1. Extract real quotes - Use actual customer feedback
2. Identify use cases - How people use the product
3. Find pain points - Problems the product solves
4. Build credibility - Use data from many reviews

For Purchasing Decisions

1. Check recent reviews - Last 30-90 days
2. Look at 1-star reviews - Understand worst-case scenarios
3. Consider your needs - Match features to your use case
4. Compare alternatives - Use compare_reviews.py

Integration Opportunities

With Price Tracker

Use review summaries to validate arbitrage opportunities:

# 1. Find arbitrage opportunity
price-tracker/scripts/compare_prices.py --keyword "Sony WH-1000XM5"

# 2. Validate with reviews
review-summarizer/scripts/scrape_reviews.py --url [amazon_url]
review-summarizer/scripts/scrape_reviews.py --url [ebay_url]

# 3. Make informed decision

With Content Recycler

Generate content from review insights:

# 1. Summarize reviews
review-summarizer/scripts/scrape_reviews.py --url [amazon_url]

# 2. Use insights in article
seo-article-gen --keyword "[product name] review" --use-insights review_summary.json

# 3. Recycle across platforms
content-recycler/scripts/recycle_content.py --input article.md

Automation

Weekly Review Monitoring

# Monitor competitor products
0 9 * * 1 /path/to/review-summarizer/scripts/compare_reviews.py \
  --product "competitor-product" \
  --platforms amazon,google \
  --output /path/to/competitor_analysis.md

Alert on Negative Trends

# Check for sentiment drops below threshold
if [ $(grep -o "Sentiment: -" summary.md | wc -l) -gt 0 ]; then
  echo "Negative sentiment alert" | mail -s "Review Alert" [e-mail]
fi

Data Privacy & Ethics

• Only scrape publicly available reviews
• Respect robots.txt and rate limits
• Don't store PII (personal information)
• Aggregate data, don't expose individual reviewers
• Follow platform terms of service

Limitations

• Rate limiting on some platforms
• Cannot access verified purchase status on all platforms
• Fake reviews may skew analysis
• Language support varies by platform
• Some platforms block scraping

---

Make data-driven decisions. Automate research. Scale intelligence.