Data Visualization Pro (Automaton)

AI-powered data visualization tool with 6 chart types (bar, line, pie, scatter, heatmap, radar), CSV/JSON import, AI-driven chart recommendations, interactiv...

Security Scan

The skill's high-level description fits a data-visualization helper, but a few inconsistencies and vague instructions about data handling and missing sample files mean you should verify sources and avoid sending sensitive files until you're comfortable.

Purpose & Capability

Name/description describe a client-side visualization tool and the SKILL.md stays on-topic, but it claims included sample datasets and a 382KB production build / specific frontend stack while no code or sample files are present in the package. The live demo URL is provided but there is no install or source code to verify the app. These are marketing-style claims that aren't reflected in the bundle.

Instruction Scope

Instructions tell the agent to accept pasted CSV/JSON or a 'file path' and to parse/analyze data — this is appropriate for visualization, but 'provide file path' is ambiguous and could lead the agent to read local files. The SKILL.md does not explicitly instruct the agent to upload data to remote services, but the presence of an external demo URL means users might be directed to or influenced to use an external endpoint. No instructions request unrelated system files or env vars.

Install Mechanism

No install spec and no code files — nothing is written to disk by the skill itself. This is the lower-risk configuration for an instruction-only skill.

Credentials

The skill requests no environment variables, secrets, or config paths. There are no declared credentials, which is proportionate for a visualization helper that runs client-side or as instructions.

Persistence & Privilege

always is false and the skill requires user invocation. It does not request permanent platform presence or to modify other skills or system-wide settings.

What to consider before installing

This skill appears to be a benign data-visualization helper, but it has small inconsistencies that merit caution: (1) the SKILL.md claims sample datasets and a web app build but the package contains no code or those files — treat the live demo URL as an external, unverified site; (2) the skill asks for pasted CSV/JSON or a file path — do not give file paths to sensitive local files, and avoid uploading private data when testing; (3) if you plan to rely on this skill, ask the publisher for source code or a trustworthy repository, test with non-sensitive sample data first, and confirm where data is processed (local agent vs external demo). If you need higher assurance, only install skills with verifiable source code or from trusted publishers.

Data Visualization Pro

AI-powered data visualization with smart chart recommendations.

Features

• 6 Chart Types: Bar, Line, Pie, Scatter, Heatmap, Radar
• AI Chart Recommendations: Analyzes your data and suggests the best chart type
• CSV/JSON Import: Drop in your data file and visualize instantly
• Interactive Dashboards: Combine multiple charts into a single view
• Export: PNG, SVG, PDF — publication-ready output
• Responsive: Works on desktop and mobile

Quick Start

1. Visualize a CSV file

Visualize this data: [paste CSV or provide file path]

The agent will:

1. Parse the data (CSV, JSON, or raw text)
2. Analyze column types (numeric, categorical, temporal)
3. Recommend the best chart type
4. Generate an interactive visualization

2. Create a specific chart

Create a bar chart comparing Q1-Q4 revenue for 2024 and 2025

3. Build a dashboard

Build a dashboard from sales-data.csv with:
- Revenue trend (line chart)
- Regional breakdown (pie chart)
- Product comparison (bar chart)

Chart Selection Guide

Data Pattern	Recommended Chart	When to Use
Trends over time	Line	Time-series, stock prices, growth
Category comparison	Bar	Revenue by region, product sales
Part-of-whole	Pie	Market share, budget allocation
Correlation	Scatter	Height vs weight, price vs demand
Multi-variable	Radar	Product comparison, skill assessment
Density/matrix	Heatmap	Correlation matrix, geographic data

AI Recommendation Engine

The AI analyzes your data to recommend the optimal visualization:

1. Column type detection: Numeric, categorical, temporal, boolean
2. Relationship analysis: Correlation strength, distribution shape
3. Data volume assessment: Row count determines complexity level
4. Pattern recognition: Trends, clusters, outliers, proportions

Sample Datasets Included

• sample-data.csv — Mixed business metrics
• sample-categories.csv — Category comparison data
• sample-correlation.csv — Multi-variable correlation data
• sample-proportions.csv — Part-of-whole data

Technical Stack

• Frontend: React + TypeScript + Vite
• Charts: Recharts (built on D3.js)
• Styling: Tailwind CSS
• Export: html2canvas + jsPDF
• Build: 382KB production build

Web App

Try the live demo: https://courageous-bonbon-d1af15.netlify.app

Usage Tips

• For large datasets (>10K rows), use aggregation before visualizing
• AI recommendations work best with 3-20 columns
• Export at 2x resolution for print-quality output
• Use the dashboard view to tell a complete data story