Skill843 repo starsupdated 4d ago

data-visualization-expert

This Claude Code skill generates publication-quality statistical visualizations from datasets in formats like CSV, JSON, and Excel using Python libraries such as Matplotlib, Seaborn, and Plotly. Use it when creating figures for scientific reports, exploratory data analysis, academic papers, or presentations that require high-resolution plots like volcano plots, heatmaps, and scatter charts with proper labeling and styling.

View source Repository: ScienceClaw

Install in Claude Code

Copy

git clone --depth 1 https://github.com/beita6969/ScienceClaw /tmp/data-visualization-expert && cp -r /tmp/data-visualization-expert/skills/data-visualization-expert ~/.claude/skills/data-visualization-expert

Then start a new Claude Code session; the skill loads automatically.

Definition

SKILL.md

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# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
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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
```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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