An automated research tool for scraping, analyzing, and synthesizing product reviews into actionable sentiment reports and recommendations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install review-summarizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install review-summarizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Review Summarizer?
The Review Summarizer is a comprehensive technical solution within the Openclaw Skills ecosystem designed to automate the extraction of consumer insights from major review platforms. It eliminates the manual effort of parsing thousands of customer comments by programmatically scraping data from Amazon, Google, Yelp, and TripAdvisor. By leveraging this tool from the Openclaw Skills library, developers and researchers can quickly identify market trends, product flaws, and high-performing features.
This skill goes beyond simple scraping by incorporating a sophisticated sentiment analysis engine that scores feedback on a scale from -1.0 to +1.0. It provides a structured breakdown of pros and cons based on frequency, making it an essential asset for anyone looking to scale their market intelligence or streamline their product research workflows.
Review Summarizer Use Cases
- Perform retail arbitrage research by identifying products with high sentiment scores and low return-related complaints.
- Create high-converting affiliate marketing content by extracting authentic customer quotes and specific use cases.
- Conduct competitive analysis by monitoring how rival products are perceived across different platforms.
- Streamline personal purchasing decisions by summarizing verified purchase reviews and filtering out noise.
- Monitor business reputation on service-oriented platforms like Yelp and TripAdvisor to track customer satisfaction trends.
How Review Summarizer Works
- The user initiates a request by providing a product URL or keyword to one of the Openclaw Skills specialized Python scripts.
- The system fetches review data from the target platform, applying user-defined filters like verified status, minimum rating, or specific time ranges.
- The raw text undergoes natural language processing to calculate an overall sentiment score and distribution.
- Key insights are extracted, including the most frequently mentioned pros, cons, and feature-specific feedback (e.g., battery life, durability).
- The skill generates a structured Markdown, JSON, or CSV report containing an executive summary and a data-driven recommendation.
Review Summarizer Setup
To deploy this skill from the Openclaw Skills repository, ensure you have a Python 3 environment configured. Use the following commands to begin analyzing reviews:
# Install dependencies (if applicable) and run a single-product scrape
python3 scripts/scrape_reviews.py --url "[PRODUCT_URL]" --platform amazon --max-reviews 100 --output report.md
# Compare a product across multiple platforms simultaneously
python3 scripts/compare_reviews.py --product "Sony WH-1000XM5" --platforms amazon,google,yelp
Review Summarizer Data Schema & Taxonomy
The Review Summarizer organizes its output into a consistent taxonomy, ensuring it integrates seamlessly with other Openclaw Skills. The standard output includes:
| Section | Data Points |
|---|---|
| Overview | Platform source, total reviews analyzed, and average numerical rating. |
| Sentiment Analysis | Global sentiment score (-1.0 to +1.0) and Positive/Neutral/Negative percentage breakdown. |
| Insight Lists | Top 3-5 pros and cons with associated review counts. |
| Feature Extraction | Aspect-based sentiment for specific categories like quality, shipping, or price. |
| Recommendation | A final verdict (Recommended/Caution/Avoid) based on aggregated data metrics. |
Review Summarizer Advanced Features
- Aspect-Based Sentiment: Target specific product attributes like 'build quality' or 'customer service' for granular analysis.
- Multi-Platform Aggregation: Compare consumer perception across different ecosystems (e.g., Amazon vs. Yelp) to identify platform-specific biases.
- Automated Monitoring: Integrate with cron jobs to receive alerts when a product's sentiment score drops below a predefined threshold.
- Workflow Integration: Connect with other Openclaw Skills like price trackers or SEO content generators to build fully automated research and marketing pipelines.
SKILL.md
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