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data-visualization-expert

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Skill by beita6969

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/beita6969-data-visualization-expert

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COPYRIGHT NOTICE

Use cases: data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/data-visualization-expert

What's inside

Skill name
data-visualization-expert
Description
COPYRIGHT NOTICE
License
MIT
Instructions
73 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

<!--
# 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
```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.


<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

From beita6969/ScienceClaw, MIT.

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Last commit (this folder)
Mar 12, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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data-visualization-expert skill. Agent Mart. https://agentmart.dev/skills/beita6969-data-visualization-expert (last updated 2026-10-24)