Data Visualization Expert
- 18 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
data-visualization-expert is a Claude skill that generates statistical and scientific plotting scripts (matplotlib, seaborn, plotly, ggplot2) from raw CSV, JSON or Excel data.
About
This skill turns raw datasets into statistical and scientific visualizations by generating plotting code. A developer uses it for EDA, reports, comparisons or publication figures, and it produces Matplotlib, Seaborn, Plotly or ggplot2 scripts with style enforcement. It cleans data, generates the plot, then saves and verifies the output file.
- Generates Python (matplotlib, seaborn, plotly) or R (ggplot2) plotting scripts from raw CSV/JSON/Excel
- Enforces journal or company styling and saves artifacts as PNG, SVG or PDF
- Guardrails avoid plotting PII and require correctly labeled axes
Data Visualization Expert by the numbers
- 18 all-time installs (skills.sh)
- Ranked #1,276 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
data-visualization-expert capabilities & compatibility
- Capabilities
- data visualization · plot generation · eda
- Use cases
- data analysis
- Platforms
- Linux · macOS
- Runs
- Runs locally
- Pricing
- Free
What data-visualization-expert says it does
Generate insightful, publication-quality visualizations from complex datasets.
A dedicated skill for transforming raw data (CSV, JSON, Excel) into compelling visual narratives. Specializes in statistical and scientific plotting.
Create 3 high-resolution (300dpi) statistical plots (volcano, heatmap, scatter) within 15 minutes.
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| Installs | 18 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Generate styled statistical plotting scripts from CSV/JSON/Excel and export figures as PNG, SVG or PDF.
Who is it for?
Turning tabular data into styled statistical plots via generated Python or R scripts.
Skip if: Plotting PII directly; guardrails explicitly avoid it.
When should I use this skill?
You need to visualize a dataset for a report, EDA, publication or model comparison.
What you get
Saved PNG/SVG/PDF plots produced from a verified plotting script.
- Plotting script
- Saved figure files (PNG/SVG/PDF)
By the numbers
- Stated outcome: 3 300dpi plots within 15 minutes
- 4 stated use cases (reports, exploration, publication, comparison)
Files
<!--
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
# 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 -->
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Does it write Python or R?
Both. It generates Matplotlib, Seaborn or Plotly Python scripts, or ggplot2 R code.
What is its stated outcome?
Create 3 high-resolution 300dpi statistical plots (volcano, heatmap, scatter) within 15 minutes.