--- name: review-summarizer-10a4fa7d2868 description: 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. --- # Review Summarizer ## 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 ```python # 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 ```python # 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 ```python # 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:** ```bash 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:** ```bash 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:** ```bash 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:** ```bash 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:** ```bash python3 scripts/export_data.py \ --input product_summary.json \ --format csv \ --output reviews_data.csv ``` ## Output Format ### Markdown Summary Structure ```markdown # 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: ```bash # 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: ```bash # 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 ```bash # 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 ```bash # 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" user@example.com 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.** --- Source attribution: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/michael-laffin/review-summarizer/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/michael-laffin/review-summarizer/SKILL.md Attribution basis: Source repository record Fields of work: Marketing Roles: Content Marketer, Influencer Marketer, Marketing Analyst, Product Marketer, SEO Specialist Specialties: API Tools: Amazon, Google, Tripadvisor, Yelp Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c91f10e456b7b64febfb6ac6b8adf7a96b5cf4162dbf370fc8b6f78f75b4b9fa