Data Visualization Studio

Create interactive and static data visualizations from datasets. Supports charts, graphs, dashboards, and statistical plots with multiple output formats (PNG...

Security Scan

The skill's code, instructions, and requirements are consistent with a data-visualization tool and do not request unrelated credentials, installs, or access.

Purpose & Capability

Name/description align with the included SKILL.md, reference docs, and the Python script which implements chart creation and export. The requested capabilities (reading data files and producing PNG/HTML/PDF/etc.) match the stated purpose; there are no unrelated credentials, binaries, or config paths required.

Instruction Scope

Runtime instructions are limited to loading data (CSV/JSON/XLSX/etc.), creating visualizations, and saving outputs. The SKILL.md examples and the script operate only on user-supplied data and output files; there are no instructions to read unrelated system files, environment secrets, or to transmit data to external endpoints.

Install Mechanism

This is instruction-only with no install spec. There is one bundled Python script that imports common visualization libraries (pandas, plotly, matplotlib, seaborn). No external download URLs or install actions are present. (Note: runtime requires those Python packages to be available, but the skill does not attempt to install them.)

Credentials

The skill declares no required environment variables, credentials, or config paths. The code imports os but does not read environment secrets. No disproportionate access to unrelated services or secrets is requested.

Persistence & Privilege

The skill is not marked always:true and does not request persistent platform privileges or modify other skills' settings. It is user-invocable and may be invoked autonomously (the platform default), which is expected for a utility skill of this kind.

Assessment

This skill appears coherent and implements what it claims: creating and exporting visualizations from local data. Before installing, ensure the runtime has the required Python libraries (pandas, plotly, matplotlib, seaborn and an image engine like kaleido or orca for static exports). Be aware that the skill reads local data files and writes output files—do not supply sensitive data you don’t want written to disk or potentially overwritten. If you run in a shared environment, confirm file paths to avoid accidental overwrites. If you need the skill to fetch remote data or install missing packages automatically, request those behaviors explicitly and review any future install steps or external URLs carefully.

Create professional data visualizations from raw data or existing datasets.

When to Use

• Creating charts and graphs from CSV, JSON, or database data
• Building interactive dashboards for data exploration
• Generating statistical plots and visual analytics
• Exporting visualizations in multiple formats (PNG, SVG, HTML, PDF)
• Creating publication-ready figures and reports

Quick Start

Basic Chart Creation

# Example: Create a simple bar chart
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('data.csv')
plt.bar(data['category'], data['values'])
plt.savefig('chart.png', dpi=300, bbox_inches='tight')

Interactive Dashboard

# Example: Create interactive plot with Plotly
import plotly.express as px

df = pd.read_csv('data.csv')
fig = px.scatter(df, x='x_column', y='y_column', color='category')
fig.write_html('dashboard.html')

Supported Libraries

• Matplotlib: Static plots, publication-quality figures
• Plotly: Interactive visualizations, web dashboards
• Seaborn: Statistical graphics, beautiful default styles
• Bokeh: Interactive web plots, streaming data support
• Altair: Declarative visualization, Vega-Lite integration

Output Formats

• PNG/JPEG: High-resolution static images
• SVG: Scalable vector graphics for web/print
• HTML: Interactive web pages with embedded JavaScript
• PDF: Publication-ready documents
• JSON: Data export for further processing

Best Practices

1. Data Preparation: Clean and validate data before visualization
2. Color Schemes: Use accessible color palettes (avoid red-green)
3. Labels: Always include clear axis labels and titles
4. Resolution: Use appropriate DPI for intended use (72 for web, 300+ for print)
5. File Size: Optimize file sizes for web delivery when needed

Advanced Features

• Animation: Create animated transitions and time-series visualizations
• Geospatial: Map-based visualizations with geographic data
• 3D Plots: Three-dimensional data representation
• Custom Styling: Brand-consistent themes and styling
• Real-time: Live updating visualizations from streaming data

References

For detailed examples and advanced usage patterns, see the bundled reference files:

• references/chart-types.md - Complete catalog of supported chart types
• references/styling-guide.md - Customization and branding guidelines
• references/performance.md - Optimization for large datasets