Generate insightful, publication-quality visualizations from complex datasets.
Scanned 9/5/2026
Install to Claude Code
npx -y skills add FridrichMethod/awesome-skills --skill data-visualization-expert --agent claude-codeInstalls into .claude/skills of the current project.
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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
---
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# 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
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# 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.
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