data-visualizer

What This Skill Does

The data-visualizer skill is a powerful extension for OpenClaw that automates the generation of publication-quality data visualizations. It is deeply integrated into the SpecWeave ecosystem, allowing users to seamlessly transition from exploratory data analysis to complex model performance reporting without leaving the terminal or interface. Whether you are performing initial feature distribution analysis, debugging a machine learning model, or creating stakeholder-ready dashboards, this tool abstracts the boilerplate code required to create meaningful insights. It features specialized modules for EDA, classification diagnostics, regression performance, and advanced feature interpretability, including SHAP summaries.

Installation

To install this skill, use the ClawHub CLI. Ensure your OpenClaw environment is active and run the following command in your terminal:

clawhub install openclaw/skills/skills/anton-abyzov/sw-data-visualizer

Once installed, the agent will automatically register the skill and begin listening for visualization-related intent triggers such as 'EDA', 'confusion matrix', or 'plot results'.

Use Cases

• Exploratory Data Analysis: Quickly identify missing values, outliers, and feature correlations in raw datasets before model training begins.
• Model Diagnostics: Analyze classification models through ROC curves, Precision-Recall charts, and confusion matrices to verify performance metrics.
• Regression Validation: Evaluate predictive models by inspecting residual plots and predicted vs. actual value distributions.
• Feature Interpretability: Communicate complex model behavior to non-technical stakeholders using feature importance rankings and SHAP summary plots.

Example Prompts

1. "Perform an EDA on the current dataframe and generate a correlation heatmap to help me understand feature dependencies."
2. "Create a confusion matrix and ROC curve for the latest classification model results to see where the model is misclassifying samples."
3. "Can you visualize the feature importance for this dataset and provide a SHAP summary plot to explain the impact of the top 10 features?"

Tips & Limitations

For the best results, ensure your data is clean and properly formatted as a Pandas DataFrame before invoking visualization commands. While the tool is excellent for static reporting, complex interactive dashboarding may require additional configuration within the SpecWeave increment environment. Always verify that your model prediction outputs (y_true/y_pred) match the dimensions required by the specific visualization function to avoid runtime errors.