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Data visualization expert

Skill FridrichMethod/awesome-skills/skills/data-visualization-expert

Curated, auto-synced collection of 2,000+ Claude Code & Codex skills for AI4Protein, bioinformatics, AI development, and academic paper writing. One curl command installs them all.

Install
npx -y skills add FridrichMethod/awesome-skills --skill data-visualization-expert

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Generate insightful, publication-quality visualizations from complex datasets.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.4 KB, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # 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 -->

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 -->

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.