data-visualization-expert

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA --> --- name: data-visualization-expert description: Generate insightful, publication-quality visualizations from complex datasets. keywords: - charts - plots - analysis - pandas - matplotlib - seaborn measurable_outcome: Create 3 high-resolution (300dpi) statistical plots (volcano, heatmap, scatter) within 15 minutes. license: MIT metadata: author: AI Agentic Skills Team version: "2.0.0" compatibility: - system: linux, macos allowed-tools: - run_shell_command - write_file - read_file --- # Data Visualization Expert A dedicated skill for transforming raw data (CSV, JSON, Excel) into compelling visual narratives. Specializes in statistical and scientific plotting. ## When to Use - **Reports:** Summarizing key metrics or KPIs. - **Exploration:** Initial data analysis (EDA) to find trends/outliers. - **Publication:** Generating figures for papers or presentations. - **Comparison:** Comparing models, cohorts, or experimental groups. ## Core Capabilities 1. **Code Generation:** Creates Python scripts (Matplotlib, Seaborn, Plotly) or R code (ggplot2). 2. **Style Enforcement:** Adheres to specific journal/company branding (fonts, colors). 3. **Data Cleaning:** Preprocesses data (handle missing values, normalize) for plotting. 4. **Artifact Management:** Saves plots as PNG/SVG/PDF files. ## Workflow 1. **Load Data:** Read input file (`pd.read_csv()`) and inspect columns/types. 2. **Clean & Transform:** Filter, pivot, or aggregate data as needed. 3. **Generate Plot:** Write plotting script with strict aesthetic controls. 4. **Save & Verify:** Execute script, check output file existence/size. ## Example Usage ```bash # Agent prompt: "Visualize the distribution of 'Age' vs 'Income' from customers.csv" # Triggers generation of `plot_age_income.py` using Seaborn scatterplot. ``` ## Guardrails - **Privacy:** Avoid plotting PII (names, emails) directly. - **Accuracy:** Ensure axes are labeled correctly with units. - **Readability:** Use appropriate scales (log vs linear) and avoid clutter. <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->