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  "item": {
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    "name": "Review Summarizer",
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      "scripts/__init__.py",
      "scripts/compare_reviews.py",
      "scripts/export_data.py",
      "scripts/quick_summary.py",
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      "Extract the archive and review SKILL.md first.",
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    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
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      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
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        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
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        "label": "Upgrade existing",
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Overview",
        "body": "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."
      },
      {
        "title": "1. Multi-Platform Review Scraping",
        "body": "Supported Platforms:\n\nAmazon (product reviews)\nGoogle (Google Maps, Google Shopping)\nYelp (business and product reviews)\nTripAdvisor (hotels, restaurants, attractions)\nCustom platforms (via URL pattern matching)\n\nScrape Options:\n\nAll reviews or specific time ranges\nVerified purchases only\nFilter by rating (1-5 stars)\nInclude images and media\nMax review count limits"
      },
      {
        "title": "2. Sentiment Analysis",
        "body": "Analyzes:\n\nOverall sentiment score (-1.0 to +1.0)\nSentiment distribution (positive/neutral/negative)\nKey sentiment drivers (what causes positive/negative reviews)\nTrend analysis (sentiment over time)\nAspect-based sentiment (battery life, quality, shipping, etc.)"
      },
      {
        "title": "3. Insight Extraction",
        "body": "Automatically identifies:\n\nTop pros mentioned in reviews\nCommon complaints and cons\nFrequently asked questions\nUse cases and applications\nCompetitive comparisons mentioned\nFeature-specific feedback"
      },
      {
        "title": "4. Summary Generation",
        "body": "Output formats:\n\nExecutive summary (150-200 words)\nDetailed breakdown by category\nPros/cons lists with frequency counts\nStatistical summary (avg rating, review count, etc.)\nCSV export for analysis\nMarkdown report for documentation"
      },
      {
        "title": "5. Recommendation Engine",
        "body": "Generates recommendations based on:\n\nOverall sentiment score\nReview quantity and recency\nVerified purchase ratio\nAspect-based ratings\nCompetitive comparison"
      },
      {
        "title": "Summarize Amazon Product Reviews",
        "body": "# Use scripts/scrape_reviews.py\npython3 scripts/scrape_reviews.py \\\n  --url \"https://amazon.com/product/dp/B0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 100 \\\n  --output amazon_summary.md"
      },
      {
        "title": "Compare Reviews Across Platforms",
        "body": "# Use scripts/compare_reviews.py\npython3 scripts/compare_reviews.py \\\n  --product \"Sony WH-1000XM5\" \\\n  --platforms amazon,google,yelp \\\n  --output comparison_report.md"
      },
      {
        "title": "Generate Quick Summary",
        "body": "# Use scripts/quick_summary.py\npython3 scripts/quick_summary.py \\\n  --url \"https://amazon.com/product/dp/B0XXXXX\" \\\n  --brief \\\n  --output summary.txt"
      },
      {
        "title": "scrape_reviews.py",
        "body": "Scrape and analyze reviews from a single URL.\n\nParameters:\n\n--url: Product or business review URL (required)\n--platform: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)\n--max-reviews: Maximum reviews to fetch (default: 100)\n--verified-only: Filter to verified purchases only\n--min-rating: Minimum rating to include (1-5)\n--time-range: Time filter (7d, 30d, 90d, all) (default: all)\n--output: Output file (default: summary.md)\n--format: Output format (markdown, json, csv)\n\nExample:\n\npython3 scripts/scrape_reviews.py \\\n  --url \"https://amazon.com/dp/B0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 200 \\\n  --verified-only \\\n  --format markdown \\\n  --output product_summary.md"
      },
      {
        "title": "compare_reviews.py",
        "body": "Compare reviews for a product across multiple platforms.\n\nParameters:\n\n--product: Product name or keyword (required)\n--platforms: Comma-separated platforms (default: all)\n--max-reviews: Max reviews per platform (default: 50)\n--output: Output file\n--format: Output format (markdown, json)\n\nExample:\n\npython3 scripts/compare_reviews.py \\\n  --product \"AirPods Pro 2\" \\\n  --platforms amazon,google,yelp \\\n  --max-reviews 75 \\\n  --output comparison.md"
      },
      {
        "title": "sentiment_analysis.py",
        "body": "Analyze sentiment of review text.\n\nParameters:\n\n--input: Input file or text (required)\n--type: Input type (file, text, url)\n--aspects: Analyze specific aspects (comma-separated)\n--output: Output file\n\nExample:\n\npython3 scripts/sentiment_analysis.py \\\n  --input reviews.txt \\\n  --type file \\\n  --aspects battery,sound,quality \\\n  --output sentiment_report.md"
      },
      {
        "title": "quick_summary.py",
        "body": "Generate a brief executive summary.\n\nParameters:\n\n--url: Review URL (required)\n--brief: Brief summary only (no detailed breakdown)\n--words: Summary word count (default: 150)\n--output: Output file\n\nExample:\n\npython3 scripts/quick_summary.py \\\n  --url \"https://yelp.com/biz/example-business\" \\\n  --brief \\\n  --words 100 \\\n  --output summary.txt"
      },
      {
        "title": "export_data.py",
        "body": "Export review data for further analysis.\n\nParameters:\n\n--input: Summary file or JSON data (required)\n--format: Export format (csv, json, excel)\n--output: Output file\n\nExample:\n\npython3 scripts/export_data.py \\\n  --input product_summary.json \\\n  --format csv \\\n  --output reviews_data.csv"
      },
      {
        "title": "Markdown Summary Structure",
        "body": "# Product Review Summary: [Product Name]\n\n## Overview\n- **Platform:** Amazon\n- **Reviews Analyzed:** 247\n- **Average Rating:** 4.3/5.0\n- **Overall Sentiment:** +0.72 (Positive)\n\n## Key Insights\n\n### Top Pros\n1. Excellent sound quality (89 reviews)\n2. Great battery life (76 reviews)\n3. Comfortable fit (65 reviews)\n\n### Top Cons\n1. Expensive (34 reviews)\n2. Connection issues (22 reviews)\n3. Limited color options (18 reviews)\n\n## Sentiment Analysis\n- **Positive:** 78% (193 reviews)\n- **Neutral:** 15% (37 reviews)\n- **Negative:** 7% (17 reviews)\n\n## Recommendation\n✅ **Recommended** - Strong positive sentiment with high customer satisfaction."
      },
      {
        "title": "For Arbitrage Research",
        "body": "Compare across platforms - Check Amazon vs eBay seller ratings\nLook for red flags - High return rates, quality complaints\nCheck authenticity - Verified purchases only\nAnalyze trends - Recent review sentiment vs older reviews"
      },
      {
        "title": "For Affiliate Content",
        "body": "Extract real quotes - Use actual customer feedback\nIdentify use cases - How people use the product\nFind pain points - Problems the product solves\nBuild credibility - Use data from many reviews"
      },
      {
        "title": "For Purchasing Decisions",
        "body": "Check recent reviews - Last 30-90 days\nLook at 1-star reviews - Understand worst-case scenarios\nConsider your needs - Match features to your use case\nCompare alternatives - Use compare_reviews.py"
      },
      {
        "title": "With Price Tracker",
        "body": "Use review summaries to validate arbitrage opportunities:\n\n# 1. Find arbitrage opportunity\nprice-tracker/scripts/compare_prices.py --keyword \"Sony WH-1000XM5\"\n\n# 2. Validate with reviews\nreview-summarizer/scripts/scrape_reviews.py --url [amazon_url]\nreview-summarizer/scripts/scrape_reviews.py --url [ebay_url]\n\n# 3. Make informed decision"
      },
      {
        "title": "With Content Recycler",
        "body": "Generate content from review insights:\n\n# 1. Summarize reviews\nreview-summarizer/scripts/scrape_reviews.py --url [amazon_url]\n\n# 2. Use insights in article\nseo-article-gen --keyword \"[product name] review\" --use-insights review_summary.json\n\n# 3. Recycle across platforms\ncontent-recycler/scripts/recycle_content.py --input article.md"
      },
      {
        "title": "Weekly Review Monitoring",
        "body": "# Monitor competitor products\n0 9 * * 1 /path/to/review-summarizer/scripts/compare_reviews.py \\\n  --product \"competitor-product\" \\\n  --platforms amazon,google \\\n  --output /path/to/competitor_analysis.md"
      },
      {
        "title": "Alert on Negative Trends",
        "body": "# Check for sentiment drops below threshold\nif [ $(grep -o \"Sentiment: -\" summary.md | wc -l) -gt 0 ]; then\n  echo \"Negative sentiment alert\" | mail -s \"Review Alert\" user@example.com\nfi"
      },
      {
        "title": "Data Privacy & Ethics",
        "body": "Only scrape publicly available reviews\nRespect robots.txt and rate limits\nDon't store PII (personal information)\nAggregate data, don't expose individual reviewers\nFollow platform terms of service"
      },
      {
        "title": "Limitations",
        "body": "Rate limiting on some platforms\nCannot access verified purchase status on all platforms\nFake reviews may skew analysis\nLanguage support varies by platform\nSome platforms block scraping\n\nMake data-driven decisions. Automate research. Scale intelligence."
      }
    ],
    "body": "Review Summarizer\nOverview\n\nAutomatically 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.\n\nCore Capabilities\n1. Multi-Platform Review Scraping\n\nSupported Platforms:\n\nAmazon (product reviews)\nGoogle (Google Maps, Google Shopping)\nYelp (business and product reviews)\nTripAdvisor (hotels, restaurants, attractions)\nCustom platforms (via URL pattern matching)\n\nScrape Options:\n\nAll reviews or specific time ranges\nVerified purchases only\nFilter by rating (1-5 stars)\nInclude images and media\nMax review count limits\n2. Sentiment Analysis\n\nAnalyzes:\n\nOverall sentiment score (-1.0 to +1.0)\nSentiment distribution (positive/neutral/negative)\nKey sentiment drivers (what causes positive/negative reviews)\nTrend analysis (sentiment over time)\nAspect-based sentiment (battery life, quality, shipping, etc.)\n3. Insight Extraction\n\nAutomatically identifies:\n\nTop pros mentioned in reviews\nCommon complaints and cons\nFrequently asked questions\nUse cases and applications\nCompetitive comparisons mentioned\nFeature-specific feedback\n4. Summary Generation\n\nOutput formats:\n\nExecutive summary (150-200 words)\nDetailed breakdown by category\nPros/cons lists with frequency counts\nStatistical summary (avg rating, review count, etc.)\nCSV export for analysis\nMarkdown report for documentation\n5. Recommendation Engine\n\nGenerates recommendations based on:\n\nOverall sentiment score\nReview quantity and recency\nVerified purchase ratio\nAspect-based ratings\nCompetitive comparison\nQuick Start\nSummarize Amazon Product Reviews\n# Use scripts/scrape_reviews.py\npython3 scripts/scrape_reviews.py \\\n  --url \"https://amazon.com/product/dp/B0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 100 \\\n  --output amazon_summary.md\n\nCompare Reviews Across Platforms\n# Use scripts/compare_reviews.py\npython3 scripts/compare_reviews.py \\\n  --product \"Sony WH-1000XM5\" \\\n  --platforms amazon,google,yelp \\\n  --output comparison_report.md\n\nGenerate Quick Summary\n# Use scripts/quick_summary.py\npython3 scripts/quick_summary.py \\\n  --url \"https://amazon.com/product/dp/B0XXXXX\" \\\n  --brief \\\n  --output summary.txt\n\nScripts\nscrape_reviews.py\n\nScrape and analyze reviews from a single URL.\n\nParameters:\n\n--url: Product or business review URL (required)\n--platform: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)\n--max-reviews: Maximum reviews to fetch (default: 100)\n--verified-only: Filter to verified purchases only\n--min-rating: Minimum rating to include (1-5)\n--time-range: Time filter (7d, 30d, 90d, all) (default: all)\n--output: Output file (default: summary.md)\n--format: Output format (markdown, json, csv)\n\nExample:\n\npython3 scripts/scrape_reviews.py \\\n  --url \"https://amazon.com/dp/B0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 200 \\\n  --verified-only \\\n  --format markdown \\\n  --output product_summary.md\n\ncompare_reviews.py\n\nCompare reviews for a product across multiple platforms.\n\nParameters:\n\n--product: Product name or keyword (required)\n--platforms: Comma-separated platforms (default: all)\n--max-reviews: Max reviews per platform (default: 50)\n--output: Output file\n--format: Output format (markdown, json)\n\nExample:\n\npython3 scripts/compare_reviews.py \\\n  --product \"AirPods Pro 2\" \\\n  --platforms amazon,google,yelp \\\n  --max-reviews 75 \\\n  --output comparison.md\n\nsentiment_analysis.py\n\nAnalyze sentiment of review text.\n\nParameters:\n\n--input: Input file or text (required)\n--type: Input type (file, text, url)\n--aspects: Analyze specific aspects (comma-separated)\n--output: Output file\n\nExample:\n\npython3 scripts/sentiment_analysis.py \\\n  --input reviews.txt \\\n  --type file \\\n  --aspects battery,sound,quality \\\n  --output sentiment_report.md\n\nquick_summary.py\n\nGenerate a brief executive summary.\n\nParameters:\n\n--url: Review URL (required)\n--brief: Brief summary only (no detailed breakdown)\n--words: Summary word count (default: 150)\n--output: Output file\n\nExample:\n\npython3 scripts/quick_summary.py \\\n  --url \"https://yelp.com/biz/example-business\" \\\n  --brief \\\n  --words 100 \\\n  --output summary.txt\n\nexport_data.py\n\nExport review data for further analysis.\n\nParameters:\n\n--input: Summary file or JSON data (required)\n--format: Export format (csv, json, excel)\n--output: Output file\n\nExample:\n\npython3 scripts/export_data.py \\\n  --input product_summary.json \\\n  --format csv \\\n  --output reviews_data.csv\n\nOutput Format\nMarkdown Summary Structure\n# Product Review Summary: [Product Name]\n\n## Overview\n- **Platform:** Amazon\n- **Reviews Analyzed:** 247\n- **Average Rating:** 4.3/5.0\n- **Overall Sentiment:** +0.72 (Positive)\n\n## Key Insights\n\n### Top Pros\n1. Excellent sound quality (89 reviews)\n2. Great battery life (76 reviews)\n3. Comfortable fit (65 reviews)\n\n### Top Cons\n1. Expensive (34 reviews)\n2. Connection issues (22 reviews)\n3. Limited color options (18 reviews)\n\n## Sentiment Analysis\n- **Positive:** 78% (193 reviews)\n- **Neutral:** 15% (37 reviews)\n- **Negative:** 7% (17 reviews)\n\n## Recommendation\n✅ **Recommended** - Strong positive sentiment with high customer satisfaction.\n\nBest Practices\nFor Arbitrage Research\nCompare across platforms - Check Amazon vs eBay seller ratings\nLook for red flags - High return rates, quality complaints\nCheck authenticity - Verified purchases only\nAnalyze trends - Recent review sentiment vs older reviews\nFor Affiliate Content\nExtract real quotes - Use actual customer feedback\nIdentify use cases - How people use the product\nFind pain points - Problems the product solves\nBuild credibility - Use data from many reviews\nFor Purchasing Decisions\nCheck recent reviews - Last 30-90 days\nLook at 1-star reviews - Understand worst-case scenarios\nConsider your needs - Match features to your use case\nCompare alternatives - Use compare_reviews.py\nIntegration Opportunities\nWith Price Tracker\n\nUse review summaries to validate arbitrage opportunities:\n\n# 1. Find arbitrage opportunity\nprice-tracker/scripts/compare_prices.py --keyword \"Sony WH-1000XM5\"\n\n# 2. Validate with reviews\nreview-summarizer/scripts/scrape_reviews.py --url [amazon_url]\nreview-summarizer/scripts/scrape_reviews.py --url [ebay_url]\n\n# 3. Make informed decision\n\nWith Content Recycler\n\nGenerate content from review insights:\n\n# 1. Summarize reviews\nreview-summarizer/scripts/scrape_reviews.py --url [amazon_url]\n\n# 2. Use insights in article\nseo-article-gen --keyword \"[product name] review\" --use-insights review_summary.json\n\n# 3. Recycle across platforms\ncontent-recycler/scripts/recycle_content.py --input article.md\n\nAutomation\nWeekly Review Monitoring\n# Monitor competitor products\n0 9 * * 1 /path/to/review-summarizer/scripts/compare_reviews.py \\\n  --product \"competitor-product\" \\\n  --platforms amazon,google \\\n  --output /path/to/competitor_analysis.md\n\nAlert on Negative Trends\n# Check for sentiment drops below threshold\nif [ $(grep -o \"Sentiment: -\" summary.md | wc -l) -gt 0 ]; then\n  echo \"Negative sentiment alert\" | mail -s \"Review Alert\" user@example.com\nfi\n\nData Privacy & Ethics\nOnly scrape publicly available reviews\nRespect robots.txt and rate limits\nDon't store PII (personal information)\nAggregate data, don't expose individual reviewers\nFollow platform terms of service\nLimitations\nRate limiting on some platforms\nCannot access verified purchase status on all platforms\nFake reviews may skew analysis\nLanguage support varies by platform\nSome platforms block scraping\n\nMake data-driven decisions. Automate research. Scale intelligence."
  },
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    "provenanceUrl": "https://clawhub.ai/Michael-laffin/review-summarizer",
    "publisherUrl": "https://clawhub.ai/Michael-laffin/review-summarizer",
    "owner": "Michael-laffin",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
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