Data Visualization Advisor
Analyze data and generate accessible, production-ready chart code instantly
What You Can Do
Analyze any dataset to identify the optimal chart type for your data's structure and story. Claude generates complete, production-ready code in your chosen library (Chart.js, Recharts, Matplotlib, or Plotly) with accessibility features built-in—ARIA labels, semantic markup, high-contrast color palettes, and proper legend placement. You get working visualizations you can paste into your projects immediately, not mockups or design suggestions.
Features
analyzes data structure to recommend the most effective visualization (line, bar, scatter, pie, heatmap, etc.)
outputs production-ready code in Chart.js, Recharts, Matplotlib, or Plotly based on your tech stack
includes ARIA labels, semantic HTML, keyboard navigation support, and sufficient color contrast ratios
uses scientifically-validated color schemes (deuteranopia, protanopia, tritanopia safe)
manages missing data, outliers, time-series patterns, and logarithmic scales intelligently
explains why a specific chart type works for your data and how to interpret the visualization
generated code includes comments and parameters you can quickly adjust for styling, dimensions, and interactivity
Example Output
Example 1: Bar Chart Recommendation
Input: Monthly revenue data across 5 product categories
Output: "A grouped bar chart is ideal here—it lets you compare categories within each month AND see trends across months side-by-side. Here's your Recharts component..."
<BarChart data={revenueData}>
<CartesianGrid strokeDasharray="3 3" />
<XAxis dataKey="month" />
<YAxis />
<Tooltip />
<Legend />
<Bar dataKey="category1" fill="#0173B2" />
<Bar dataKey="category2" fill="#DE8F05" />
...
</BarChart>
Example 2: Accessibility Compliance
Input: Website traffic dashboard for stakeholder reports
Output: Generated Matplotlib chart with alt="Line chart showing daily active users increasing from 1,200 to 4,800 over Q3" and WCAG AA compliant color contrast ratios (4.5:1).
Example 3: Edge Case Handling
Input: Sales data with gaps in 2 months and 1 major outlier spike
Output: "Consider using a line chart with a secondary annotation for the spike event. Here's the code with tooltips explaining the anomaly, plus a cleaned version without the outlier for comparison."
What's Included
- data-visualization-advisor SKILL.md: instruction file with chart selection logic, accessibility standards, and library-specific code patterns
- Chart Type Decision Tree: framework for choosing visualizations based on data dimensionality, comparison type, and composition
- Accessibility Checklist: WCAG 2.1 AA compliance checklist for colors, labels, keyboard navigation, and screen reader support
- Library Templates: boilerplate code snippets for Chart.js, Recharts, Matplotlib, and Plotly with accessibility patterns pre-built
- Color Palette Reference: color-blind-safe palette swatches with hex codes tested for deuteranopia, protanopia, and tritanopia
Who It's For
- Data analysts & business intelligence professionals — turn raw datasets into stakeholder-ready dashboards and reports
- Full-stack developers — generate frontend chart components that pass accessibility audits without extra work
- Product managers & designers — visualize user behavior, metrics, and product performance data clearly and accessibly
- Academic researchers — create publication-ready charts that meet journal accessibility and visualization standards
- Content creators & journalists — communicate data stories through effective, accessible visualizations
Best For
- Selecting the right chart type when you're unsure (line vs. bar vs. scatter, etc.)
- Generating production-ready chart code you can paste into projects immediately
- Creating accessible dashboards and reports that meet WCAG compliance standards
- Building multi-library chart components (React, Python, JavaScript) from a single data description
- Troubleshooting visualization problems (outliers, missing data, too many categories) with recommendations
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