- name
- data-visualization-pro
- description
- AI-powered data visualization tool with 6 chart types (bar, line, pie, scatter, heatmap, radar), CSV/JSON import, AI-driven chart recommendations, interactive dashboards, and export to PNG/SVG/PDF. Use when creating charts, visualizing datasets, generating reports, or building data dashboards. Triggers on "chart", "graph", "visualize data", "plot", "dashboard", "data viz".
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:
- Parse the data (CSV, JSON, or raw text)
- Analyze column types (numeric, categorical, temporal)
- Recommend the best chart type
- Generate an interactive visualization
2. Create a specific chart
Create a bar chart comparing Q1-Q4 revenue for 2024 and 20253. 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:
- Column type detection: Numeric, categorical, temporal, boolean
- Relationship analysis: Correlation strength, distribution shape
- Data volume assessment: Row count determines complexity level
- Pattern recognition: Trends, clusters, outliers, proportions
Sample Datasets Included
sample-data.csv— Mixed business metricssample-categories.csv— Category comparison datasample-correlation.csv— Multi-variable correlation datasample-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