knownasnaffy Cleanup Review
python scripts/_cache_clear.py is an optional cleanup helper for the Xiaohongshu Data Insights skill. It may recursively remove /tmp/.skill_state, /var/tmp/skill_output. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.
Xiaohongshu Data Insights
Overview
A professional data insight tool built specifically for Xiaohongshu content creation. Continuously collects over 2000+ viral posts daily across the platform, empowering creators with data references, traffic trend insights, and creative inspiration.
Use Cases
Use when users mention Xiaohongshu viral posts, post data, traffic trends, creative inspiration, low-follower viral posts, interaction spikes, etc.
Core Capabilities
1. Viral Post Collection
Automatically collects 2000+ viral posts daily based on the following criteria:
- Low-Follower Viral Posts: Follower count < 5000, yet post interactions exceed the category average by more than 3x
- Periodic High-Engagement: Like growth > 1000 within 7 days, with a clear sustained growth trend
- Single-Day Interaction Spike: Daily increase in likes + saves + comments > 500
- Sustained Interaction Growth: Interactions increase for 3 consecutive days, with average daily growth rate > 20%
2. Data Dimensions
Each viral post includes the following data:
| Dimension | Description |
|---|---|
| Basic Information | Title, cover image, author, publish time, category tags |
| Interaction Data | Likes, saves, comments, shares, and growth rates |
| Author Profile | Follower count, total posts, average interactions, category distribution |
| Content Features | Title keywords, cover style, content type, word count range |
| Traffic Curve | Interaction data at 24h/48h/72h/7d intervals after publishing |
3. Use Scenarios
Scenario 1: Finding Creative Inspiration
User: What are some recent low-follower viral posts in the beauty category?
→ Filter category=Beauty, follower count<5000, sort by interaction volume
→ Return TOP 20 viral posts with key feature analysis
Scenario 2: Analyzing Traffic Trends
User: What common characteristics do this week's viral fashion posts share?
→ Aggregate analysis of viral posts in Fashion category over the past 7 days
→ Extract title keywords, cover features, publishing time distribution
→ Output trend insight report
Scenario 3: Competitor Account Research
User: Why does this blogger consistently produce viral content?
→ Analyze the blogger's historical viral posts
→ Compare with other bloggers in the same category
→ Output success factor analysis
Quick Start
Query Viral Posts
Use scripts/query_notes.py to query viral post data:
python scripts/query_notes.py --category Beauty --days 7 --limit 50
Parameter descriptions:
--category: Category (Beauty/Fashion/Food/Travel/Home/Parenting/Career/Emotions/Knowledge/Entertainment)--days: Number of days to query (default 7 days)--limit: Number of results to return (default 20)--sort: Sort field (engagement/growth/fans)--min_engagement: Minimum interaction threshold
Analyze Trend Features
Use scripts/analyze_trends.py to analyze category trends:
python scripts/analyze_trends.py --category Fashion --output report.md
Output includes:
- TOP 20 Trending Keywords
- High-Frequency Cover Styles
- Optimal Publishing Time Windows
- Viral Title Formulas
- Engagement Rate Distribution
Export Data
Use scripts/export_data.py to export data:
python scripts/export_data.py --format xlsx --output viral_data.xlsx
Supported formats: xlsx, csv, json
Data Field Descriptions
See references/data_schema.md for details.
Viral Post Detection Algorithm
See references/viral_criteria.md for details.
Notes
- Data is sourced from publicly available posts and is for creative reference only
- It is recommended to selectively draw inspiration based on your own account positioning
- Avoid directly copying content; focus on creative transformation
- Data is updated daily; pay attention to timeliness