Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Clean Skill
Cross-reference restaurant recommendations from Xiaohongshu (小红书) and Dianping (大众点评) to validate restaurant quality and consistency. Use when querying resta...
skill openclawclawhub Free
Cross-reference restaurant recommendations from Xiaohongshu (小红书) and Dianping (大众点评) to validate restaurant quality and consistency. Use when querying resta...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, examples/example_usage.py, examples/test_high_consistency.py, references/api_limitations.md, references/data_schema.md, references/sentiment_analysis.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.0
Provenance
- Publisher
- zhongrenfei1-hub
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Restaurant Review Cross-Check
Cross-reference restaurant data from Xiaohongshu and Dianping to provide validated recommendations.
Quick Start
Query restaurants by location and cuisine type: # Basic query crosscheck-restaurants "上海静安区" "日式料理" # With filters crosscheck-restaurants "北京朝阳区" "火锅" --min-rating 4.5 --min-reviews 100
1. Data Collection
Query both platforms simultaneously: Dianping: Fetch restaurants matching location + cuisine Extract: name, rating, review_count, price_range, address, tags Xiaohongshu: Search notes/posts matching location + cuisine Extract: restaurant_name, engagement_metrics (likes/saves), sentiment_score Note: Xiaohongshu data requires scraping as no public API
2. Data Matching
Match restaurants across platforms using fuzzy matching: Restaurant name similarity (Levenshtein distance) Location proximity (address matching) Handle name variations (e.g., "银座寿司" vs "银座寿司静安店") See scripts/match_restaurants.py for matching logic.
3. Consistency Analysis
Calculate consistency score based on: Rating correlation (0-1): Correlation between platform ratings Engagement validation (0-1): Do high ratings correlate with high engagement? Sentiment alignment (0-1): Do user sentiments align across platforms? Formula: consistency_score = (rating_corr * 0.5) + (engagement_val * 0.3) + (sentiment_align * 0.2)
4. Recommendation Score
Calculate final recommendation score: recommendation_score = ( (dianping_rating * 0.4) + (xhs_engagement_normalized * 0.3) + (consistency_score * 0.3) ) * 10 Output: 0-10 scale, where >8.0 = high confidence recommendation
Output Format
📍 [Location] [Cuisine Type] 餐厅推荐 1. [Restaurant Name] 🏆 推荐指数: X.X/10 ⭐ 大众点评: X.X (Xk评价) 💬 小红书: X.X⭐ (X笔记) 📍 地址: [Address] 💰 人均: ¥[Price] ✅ 一致性: [高/中/低] - [Brief explanation] 📊 平台对比: - 大众点评标签: [Tags] - 小红书热词: [Keywords] ⚠️ 注意: [Any discrepancies or warnings] [Continue for top 5-10 restaurants...]
Thresholds
Min rating: 4.0/5.0 (configurable) Min reviews: 50 on Dianping, 20 notes on Xiaohongshu (configurable) Max results: Top 10 restaurants by recommendation score High consistency: Score > 0.7 Medium consistency: Score 0.5-0.7 Low consistency: Score < 0.5 (flag for manual review)
Dianping
Method: Web scraping (Dianping API requires business partnership) Base URL: https://www.dianping.com Rate limiting: 1 request/2 seconds minimum Anti-scraping: Use residential proxies, rotate user agents See scripts/fetch_dianping.py for implementation.
Xiaohongshu
Method: Web scraping (no public API) Base URL: https://www.xiaohongshu.com Rate limiting: 1 request/3 seconds minimum Authentication: Cookies required for full access See scripts/fetch_xiaohongshu.py for implementation.
Configuration
Edit scripts/config.py to set: DEFAULT_THRESHOLDS = { "min_rating": 4.0, "min_dianping_reviews": 50, "min_xhs_notes": 20, "max_results": 10 } PROXY_CONFIG = { "use_proxy": True, "proxy_list": ["http://proxy1:port", "http://proxy2:port"] }
Error Handling
No matches found: Suggest broader search terms or nearby areas Platform timeout: Retry with exponential backoff, max 3 attempts Rate limiting detected: Pause for 60 seconds, rotate proxy Low confidence results: Flag results with consistency < 0.5 for manual review
Sentiment Analysis
Xiaohongshu posts use NLP to extract: Food quality mentions Service quality mentions Atmosphere mentions Price/value mentions See references/sentiment_analysis.md for methodology.
Fuzzy Matching
Handle restaurant name variations: Chain stores (e.g., "海底捞火锅" vs "海底捞静安店") Abbreviations (e.g., "鼎泰丰" vs "鼎泰丰上海店") Translation differences Uses thefuzz library for similarity scoring.
Dependencies
pip install requests beautifulsoup4 pandas numpy thefuzz selenium lxml See scripts/requirements.txt for complete list.
Troubleshooting
Issue: Xiaohongshu returns empty results Solution: Check if cookies expired, re-authenticate Issue: Dianping blocks requests Solution: Reduce request rate, rotate proxies Issue: Poor matching between platforms Solution: Adjust similarity threshold in match_restaurants.py
References
Data schema documentation Sentiment analysis guide API limitations
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- references/api_limitations.md
- references/data_schema.md
- references/sentiment_analysis.md
- examples/example_usage.py
- examples/test_high_consistency.py