data-visualization-expert

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---

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

# 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.

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