data-viz-plots

This data visualization skill empowers users to generate professional, publication-quality figures directly within their local development environment. By leveraging the industry-standard Python libraries matplotlib and seaborn, the agent can transform raw data into high-resolution visuals suitable for scientific papers, reports, and complex exploratory data analysis (EDA). Because the logic executes locally, this skill remains entirely vendor-neutral and functions seamlessly across all major LLM providers including Claude, GPT, Gemini, DeepSeek, and Qwen, ensuring no dependency on cloud-hosted visualization tools.

The skill provides a robust framework for creating diverse plot types, including scatter plots, line charts, box plots, violin plots, bar plots with error bars, and heatmaps. It is specifically optimized for researchers, data scientists, and developers who require fine-grained control over plot aesthetics, such as fonts, color palettes, DPI settings for publication, and grid configurations. By utilizing a common coding pattern that integrates pandas and numpy, users can easily visualize gene expression data, clustering results, QC metrics, or time-series performance data in a reproducible manner.

• Generates publication-ready figures with customizable DPI, font sizes, and layout styles.

• Supports multi-panel figure generation through gridspec, allowing for complex dashboard-like visualization.

• Compatible with standard scientific Python stacks: matplotlib, seaborn, pandas, and numpy.

• Enables local execution, maintaining complete data privacy and independence from proprietary platform tools.

• Provides automated export options for high-resolution images, including formats optimized for print and digital reports.

• Includes specific code patterns for statistical visualizations, including violin plots, box plots, and heatmaps with annotation.

• Ensure all input data is structured as pandas DataFrames or numpy arrays before calling the visualization methods.

• Use the suggested boilerplate configuration (e.g., sns.set_style('whitegrid')) to maintain consistency across visual assets.

• Always define figure size and resolution parameters (DPI) explicitly to meet journal or publication requirements.

• The skill is intended for local execution; ensure the host environment has the necessary Python dependencies (matplotlib, seaborn, pandas) installed.

• When creating multi-panel figures, utilize the gridspec module to ensure proportional layout and alignment of subplots.