An expert AI skill for statistical analysis, machine learning development, and data-driven storytelling.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ah-data-scientist
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 ah-data-scientist 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 Scientist?
The Data Scientist skill transforms your AI agent into a senior-level data expert capable of handling complex analytical workflows. Built for the Openclaw Skills ecosystem, it provides the specialized knowledge required for hypothesis testing, predictive modeling, and experimental design. Whether you are dealing with large-scale datasets or fine-tuning neural networks, this skill ensures your AI follows rigorous scientific methodologies.
By integrating this skill, users gain access to a technical powerhouse proficient in languages like Python, R, and SQL. It bridges the gap between raw data and actionable insights by automating feature engineering, model evaluation, and the creation of production-ready machine learning pipelines.
Data Scientist Use Cases
- Developing and evaluating machine learning models using frameworks like scikit-learn and XGBoost.
- Designing and analyzing A/B tests with proper statistical significance and sample size calculations.
- Conducting exploratory data analysis (EDA) and generating high-fidelity visualizations using Plotly or Seaborn.
- Performing time series forecasting and causal inference for business intelligence.
- Building deep learning architectures with TensorFlow or PyTorch for complex pattern recognition.
How Data Scientist Works
- Data Ingestion & Cleaning: The skill initiates by validating data quality and performing necessary cleaning to ensure reliable analysis.
- Hypothesis Definition: It identifies core success metrics and defines statistical hypotheses based on the project goals.
- Feature Engineering: The agent creates and selects meaningful features to optimize model performance.
- Model Selection & Training: Leveraging a wide range of ML libraries, it selects the best-fit algorithms and trains them with cross-validation.
- Evaluation & Interpretation: Results are interpreted using tools like SHAP or LIME to ensure model transparency and practical significance.
- Insight Delivery: The workflow concludes with the generation of comprehensive notebooks, dashboards, and actionable recommendations.
Data Scientist Setup
To integrate this skill into your local environment, follow these steps within your Openclaw Skills configuration:
# Install the data-scientist skill
claw install data-scientist
# Ensure required data science dependencies are present
pip install pandas numpy scikit-learn matplotlib seaborn
Configure your environment variables if you plan to use cloud-based platforms like AWS SageMaker or Databricks.
Data Scientist Data Schema & Taxonomy
The Data Scientist skill organizes its output into structured formats designed for reproducibility and clarity:
| Data Component | Format | Description |
|---|---|---|
| Analysis Notebooks | .ipynb | Interactive documents containing code, visualizations, and commentary. |
| Statistical Reports | Markdown | Summary of test results, p-values, and confidence intervals. |
| Model Artifacts | .pkl / .h5 | Exported model weights and configurations for deployment. |
| Visualization Assets | PNG/HTML | Static and interactive charts for stakeholder presentations. |
| Documentation | README.md | Detailed logs of assumptions, methodologies, and limitations. |
Data Scientist Advanced Features
- Multi-framework support including Spark, Dask, and Ray for big data processing.
- Automated model interpretability using SHAP and LIME for white-box machine learning.
- Comprehensive A/B testing framework with multiple testing corrections.
- Seamless integration with cloud platforms like Google AI Platform and Azure ML.
- Time series analysis and forecasting capabilities using specialized econometric models.
SKILL.md
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