An end-to-end data processing skill designed to transform raw information into actionable business intelligence through statistical modeling and visualization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install datafenxi
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 datafenxi 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 Data Analysis?
The Data Analysis skill empowers AI agents to perform complex operations including data collection, cleaning, processing, and analytical modeling. By integrating statistics, computer science, and business logic, this skill enables organizations to derive valuable insights from diverse data sources like databases, APIs, and web crawlers.
Leveraging industry-standard tools such as Pandas, R, and Tableau, this Openclaw Skills component ensures high-quality data governance. It focuses on transforming raw metrics into visual reports and strategic recommendations, bridging the gap between technical data science and executive decision-making.
Data Analysis Use Cases
- Digital marketing optimization through user segmentation and sales forecasting.
- Product improvement using A/B testing and behavioral analysis.
- Operational efficiency via supply chain optimization and cost control.
- Financial risk management including credit scoring and fraud detection.
How Data Analysis Works
- Data Acquisition: Pulls raw information from various sources including SQL databases and external APIs.
- Data Cleaning: Processes missing values and anomalies using libraries like Pandas or dplyr to ensure format consistency.
- Exploratory Analysis: Applies statistical methods and machine learning models to identify patterns and trends.
- Visualization: Generates intuitive charts and dashboards using Matplotlib or BI tools for clear presentation.
- Business Interpretation: Translates technical findings into actionable business strategies and operational advice.
Data Analysis Setup
To integrate this capability into your workflow, ensure your environment is configured with the necessary analytical libraries:
# Install core data analysis dependencies
pip install pandas numpy matplotlib scikit-learn
# Optional: Install R language support for advanced statistics
# apt-get install r-base
Configuration requires access credentials for your data sources (SQL, API keys) to be defined in your environment variables.
Data Analysis Data Schema & Taxonomy
The skill organizes data through a structured pipeline to maintain integrity:
| Stage | Description | Key Artifacts |
|---|---|---|
| Raw | Unprocessed source data | CSV, JSON, SQL Dumps |
| Processed | Cleaned and normalized data | Parquet, Cleaned DataFrames |
| Analytical | Modeled results and subsets | Statistical Summaries, Coefficients |
| Output | Final insights for the user | PNG/SVG Charts, Executive Summaries |
Data Analysis Advanced Features
- Multi-source data fusion from disparate API and database endpoints.
- Automated anomaly detection and data validation hooks.
- Large-scale data processing support via Hadoop and Spark integrations.
- Seamless export to professional BI suites like Power BI and Tableau for Openclaw Skills users.
SKILL.md
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