A powerful validation tool that cross-references Xiaohongshu and Dianping data to provide high-confidence restaurant recommendations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install restaurant-crosscheck
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 restaurant-crosscheck 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 Restaurant Review Cross-Check?
The Restaurant Review Cross-Check skill is a specialized tool within the Openclaw Skills ecosystem designed to solve the problem of unreliable restaurant reviews. By simultaneously querying Dianping and Xiaohongshu, it validates whether a restaurant's popularity is backed by consistent professional ratings and genuine social media engagement.
This utility leverages advanced data matching and sentiment analysis to filter out anomalies and paid promotions. By using Openclaw Skills to bridge these two major platforms, users receive a unified recommendation score that accounts for rating correlation, user sentiment alignment, and overall consistency across the web.
Restaurant Review Cross-Check Use Cases
- Finding the most authentic local cuisine in specific districts like Shanghai Jing'an or Beijing Chaoyang.
- Verifying if a viral Xiaohongshu restaurant has the professional Dianping ratings to match the hype.
- Researching reliable dining options for travel planning where traditional review sites might be influenced by bias.
- Analyzing market sentiment for food and beverage businesses by comparing cross-platform performance metrics.
How Restaurant Review Cross-Check Works
- The skill initiates a dual-platform search based on the user's specified geographic location and cuisine type.
- It extracts structured data from Dianping, including ratings, review counts, and price ranges.
- It scrapes relevant Xiaohongshu notes to gather engagement metrics like likes and saves, alongside raw text for sentiment analysis.
- Using thefuzz library, it performs fuzzy matching to link restaurant entities despite slight naming variations or branch specificities.
- A consistency algorithm calculates a score based on how well the ratings and sentiments align across both platforms.
- It outputs a prioritized list of restaurants with a final recommendation score on a 0-10 scale.
Restaurant Review Cross-Check Setup
To integrate this functionality into your Openclaw Skills environment, ensure you have Python installed and run the following command to install dependencies:
pip install requests beautifulsoup4 pandas numpy thefuzz selenium lxml
Configure your scraping parameters in scripts/config.py, specifically setting up your PROXY_CONFIG to handle rate limiting and providing valid Xiaohongshu cookies for authenticated access.
Restaurant Review Cross-Check Data Schema & Taxonomy
The skill produces a structured report containing the following data points for each restaurant:
| Attribute | Description |
|---|---|
| Recommendation Index | A weighted 0-10 score combining Dianping ratings, XHS engagement, and consistency. |
| Consistency Rating | A qualitative label (High/Medium/Low) based on platform alignment. |
| Platform Comparison | Direct comparison of Dianping tags versus Xiaohongshu hot keywords. |
| Sentiment Analysis | Aggregated scores for food quality, service, and atmosphere. |
| Location Data | Precise address and branch identification. |
Restaurant Review Cross-Check Advanced Features
- Intelligent fuzzy matching to handle complex Chinese restaurant naming conventions and chain branch variations.
- NLP-driven sentiment analysis that extracts specific mentions of service and atmosphere from unstructured social media posts.
- Robust error handling with exponential backoff and proxy rotation to ensure reliable data fetching through Openclaw Skills.
- Automated consistency flagging that alerts users when there is a significant discrepancy between professional ratings and social buzz.
- Configurable thresholds for minimum review counts to filter out new or low-traffic establishments.
SKILL.md
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