An automated data analysis tool that transforms raw CSV and Excel files into comprehensive HTML reports with cleaning, statistics, and visualizations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-data-analyzer-chen
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 auto-data-analyzer-chen 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 Auto-Data-Analyzer?
Auto-Data-Analyzer is a robust technical solution designed to streamline the data science lifecycle. As a standout among Openclaw Skills, this tool automates the tedious aspects of data preparation and exploratory data analysis (EDA). It provides developers and analysts with a standardized way to process datasets, ensuring high-quality output through systematic cleaning and statistical validation.
By leveraging industry-standard libraries, it bridges the gap between raw data collection and actionable business intelligence. Whether you are dealing with missing values or complex correlation matrices, this skill ensures that your data stories are told through high-fidelity PNG charts and interactive HTML summaries.
Auto-Data-Analyzer Use Cases
- Business Data Analysis: Rapidly evaluate quarterly performance metrics and operational efficiency.
- Market Research Reports: Analyze consumer trends and demographic shifts with automated statistical descriptions.
- User Behavior Analysis: Identify patterns in application usage or website engagement data.
- Sales Data Analysis: Track revenue growth, seasonal fluctuations, and product performance via regression and time series analysis.
How Auto-Data-Analyzer Works
- Data Ingestion: The skill loads CSV or Excel files into a structured environment using specialized data protocols.
- Automated Cleaning: It identifies missing values, detects outliers, and handles duplicates to ensure data integrity.
- Statistical Profiling: The system calculates means, medians, quantiles, and standard deviations to establish a mathematical baseline.
- Visualization Engine: It generates a suite of plots including histograms, box plots, and heatmaps for visual discovery.
- Advanced Modeling: Performs correlation, clustering, and regression analysis to find hidden relationships within the variables.
- Report Generation: Consolidates all findings into a shareable HTML report alongside raw PNG and CSV assets.
Auto-Data-Analyzer Setup
To integrate this capability into your workflow, ensure you have the necessary dependencies installed via pip:
pip install pandas numpy matplotlib seaborn scikit-learn
You can then execute the analysis via the CLI for immediate results:
python main.py your_data.csv --output report.html
Auto-Data-Analyzer Data Schema & Taxonomy
The Auto-Data-Analyzer organizes output into a structured directory containing the following assets:
| File Type | Description | Usage |
|---|---|---|
| HTML | Comprehensive Report | Final presentation of all insights and charts. |
| PNG | Visualization Assets | Individual charts (scatter, box plots) for external docs. |
| CSV | Statistical Results | Tabular data containing cleaned metrics and coefficients. |
All metadata, including versioning and author info, is managed via the standard Openclaw Skills manifest structure.
Auto-Data-Analyzer Advanced Features
- Multi-Model Analysis: Includes support for clustering and time series forecasting beyond basic statistics.
- Automated Outlier Handling: Advanced detection logic to prevent data skewing in regression models.
- Extensible Visualization: Leverages Seaborn and Matplotlib for high-resolution graphical exports.
- Headless CLI Integration: Perfect for automated devops pipelines and cloud-based data processing tasks.
SKILL.md
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