A professional-grade AI skill pack for automating SQL generation, Python-based data analysis, and visual business reporting.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pro-data-analyst
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install pro-data-analyst using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Data Analyst Pro is a comprehensive suite designed to turn raw data into professional insights. It provides a robust framework for handling complex data tasks, ranging from generating intricate SQL queries to performing advanced statistical analysis using Python and Pandas. This skill is built for professionals who need to bridge the gap between technical data manipulation and executive-level reporting.
By leveraging the power of Openclaw Skills, this tool ensures that your data processing remains local and secure. It offers a structured approach to data science workflows, including automated data cleaning, anomaly detection, and a library of over 30 SQL templates. Whether you are a developer, a data analyst, or a product manager, this skill accelerates your analytical throughput while maintaining high technical standards.
To deploy Data Analyst Pro within your environment, ensure you have the necessary dependencies installed for data processing.
# Install the core analytical stack
pip install pandas matplotlib seaborn scipy
# Add the skill to your local Openclaw Skills library
openclaw install data-analyst-pro
Once installed, you can trigger the skill by providing context about your database schema or uploading a dataset for analysis.
The skill organizes its operations into specialized modules for consistency and speed:
| Module | Data Type | Description |
|---|---|---|
| SQL Templates | Structured Query Language | Pre-defined patterns for growth, retention, and cohorts. |
| Python Analytics | Pandas/NumPy Scripts | Automated cleaning, merging, and aggregation logic. |
| Visualization | Matplotlib/Seaborn | Code for generating trend lines, heatmaps, and distributions. |
| Reporting | Markdown | Templates for executive summaries and detailed findings. |
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