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data-visualizer — Claude Code skill: what it does, how to install

Guides coding agents in building charts and interactive dashboards. Helpful when adding metrics views to React, Vue, or Angular applications.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add daffy0208/ai-dev-standards --skill data-visualizer -a claude-code
Open on GitHub

What data-visualizer does

This skill instructs your AI assistant on library selection and interface patterns for data rendering. It directs React and Next.js tasks toward recharts. For Vue or Angular codebases, it applies chart.js, reserving d3 for custom projections and complex interactive graphics.

The patterns cover KPI summary cards with trend badges, heatmaps, Sankey diagrams, and live streaming dashboards powered by EventSource with Server-Sent Events. The examples use TypeScript alongside responsive layouts.

Keep in mind that this skill only supplies implementation guidelines. You must still install packages like recharts or d3 into your project dependencies directly.

What it helps with

  • Pick the right charting library for a given frontend stack
  • Build a KPI dashboard with trend badges and responsive layout
  • Set up a live monitoring chart fed by Server-Sent Events
  • Render custom SVG charts with axes and margins using D3.js

Example prompts for the agent

  • Build a responsive revenue line chart component using Recharts
  • Create an executive KPI dashboard with trend indicators in React
  • Write a real-time traffic chart connected to a Server-Sent Events stream

How to install data-visualizer

1Skills CLI (Vercel)

npx skills add daffy0208/ai-dev-standards --skill data-visualizer -a claude-code

2Manually: put the files in place

~/.claude/skills/data-visualizer/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/data-visualizer/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about data-visualizer

How to install data-visualizer in Claude Code?

Run npx skills add daffy0208/ai-dev-standards --skill data-visualizer -a claude-code in your terminal.

What does data-visualizer do?

It injects reference implementations and decision rules for building charts and metrics dashboards into the agent context.

Which charting libraries are supported?

The guidelines cover recharts for React, chart.js for framework-agnostic setups, and d3 for advanced custom graphics.

Source text

SKILL.md · MIT
---
name: data-visualizer
description: Expert in creating charts, dashboards, and data visualizations using modern libraries
version: 1.0.0
tags: [data-viz, charts, dashboards, d3, recharts, analytics]
---

# Data Visualizer Skill

I help you build beautiful, interactive data visualizations and dashboards.

## What I Do

**Chart Creation:**

- Line charts, bar charts, pie charts
- Area charts, scatter plots, heatmaps
- Complex visualizations (Sankey, treemaps, network graphs)

**Dashboard Building:**

- KPI cards and metrics
- Real-time data dashboards
- Interactive filters and drill-downs
- Responsive layouts

**Data Presentation:**

- Data storytelling
- Color schemes and accessibility
- Animation and interactions
- Export capabilities

## Library Selection Guide

### Recharts (Recommended for React)

**Best for:**

- Quick, simple charts
- React/Next.js projects
- Standard chart types
- Responsive design

**Example:**

```typescript
import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend } from 'recharts'

const data = [
  { month: 'Jan', revenue: 4000, expenses: 2400 },
  { month: 'Feb', revenue: 3000, expenses: 1398 },
  { month: 'Mar', revenue: 2000, expenses: 9800 },
]

function RevenueChart() {
  return (
    <LineChart width={600} height={300} data={data}>
      <CartesianGrid strokeDasharray="3 3" />
      <XAxis dataKey="month" />
      <YAxis />
      <Tooltip />
      <Legend />
      <Line type="monotone" dataKey="revenue" stroke="#8884d8" />
      <Line type="monotone" dataKey="expenses" stroke="#82ca9d" />
    </LineChart>
  )
}
```

---

### Chart.js (Recommended for Vue/Angular)

**Best for:**

- Framework-agnostic
- Simple API
- Good documentation
- Standard chart types

**Example:**

```typescript
import { Chart } from 'chart.js/auto'

const ctx = document.getElementById('myChart')
const chart = new Chart(ctx, {
  type: 'bar',
  data: {
    labels: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    datasets: [
      {
        label: 'Sales',
        data: [12, 19, 3, 5, 2, 3],
        backgroundColor: 'rgba(54, 162, 235, 0.5)'
      }
    ]
  },
  options: {
    responsive: true,
    plugins: {
      legend: { position: 'top' },
      title: { display: true, text: 'Monthly Sales' }
    }
  }
})
```

---

### D3.js (Advanced)

**Best for:**

- Custom visualizations
- Complex interactions
- Full control over rendering
- Data-driven documents

**When to use:**

- Need custom chart type
- Complex data transformations
- Advanced interactions
- Publication-quality graphics

**Example:**

```typescript
import * as d3 from 'd3'

function createBarChart(data: Array<{ name: string; value: number }>) {
  const width = 600
  const height = 400
  const margin = { top: 20, right: 20, bottom: 30, left: 40 }

  const svg = d3.select('#chart').append('svg').attr('width', width).attr('height', height)

  const x = d3
    .scaleBand()
    .domain(data.map(d => d.name))
    .range([margin.left, width - margin.right])
    .padding(0.1)

  const y = d3
    .scaleLinear()
    .domain([0, d3.max(data, d => d.value)])
    .range([height - margin.bottom, margin.top])

  svg
    .selectAll('rect')
    .data(data)
    .join('rect')
    .attr('x', d => x(d.name))
    .attr('y', d => y(d.value))
    .attr('height', d => y(0) - y(d.value))
    .attr('width', x.bandwidth())
    .attr('fill', 'steelblue')

  // Add axes
  svg
    .append('g')
    .attr('transform', `translate(0,${height - margin.bottom})`)
    .call(d3.axisBottom(x))

  svg.append('g').attr('transform', `translate(${margin.left},0)`).call(d3.axisLeft(y))
}
```

---

## Dashboard Patterns

### Pattern 1: KPI Dashboard

**Use case:** Executive dashboard with key metrics

```typescript
// components/KPIDashboard.tsx
import { Card } from '@/components/ui/card'

interface KPICardProps {
  title: string
  value: string | number
  change: number
  trend: 'up' | 'down'
}

function KPICard({ title, value, change, trend }: KPICardProps) {
  const trendColor = trend === 'up' ? 'text-green-600' : 'text-red-600'
  const trendIcon = trend === 'up' ? '↑' : '↓'

  return (
    <Card className="p-6">
      <h3 className="text-sm font-medium text-gray-600">{title}</h3>
      <div className="mt-2 flex items-baseline">
        <p className="text-3xl font-semibold">{value}</p>
        <span className={`ml-2 text-sm ${trendColor}`}>
          {trendIcon} {Math.abs(change)}%
        </span>
      </div>
    </Card>
  )
}

export default function Dashboard() {
  return (
    <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-4 gap-6">
      <KPICard title="Total Revenue" value="$45,231" change={12.5} trend="up" />
      <KPICard title="Active Users" value="2,350" change={-5.2} trend="down" />
      <KPICard title="Conversion Rate" value="3.24%" change={8.1} trend="up" />
      <KPICard title="Avg Order Value" value="$158" change={2.3} trend="up" />
    </div>
  )
}
```

---

### Pattern 2: Real-Time Dashboard

**Use case:** Live data monitoring

```typescript
// components/RealtimeDashboard.tsx
'use client'

import { useEffect, useState } from 'react'
import { LineChart, Line, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts'

interface DataPoint {
  time: string
  value: number
}

export default function RealtimeDashboard() {
  const [data, setData] = useState<DataPoint[]>([])

  useEffect(() => {
    // Fetch initial data
    fetch('/api/metrics/realtime')
      .then(res => res.json())
      .then(setData)

    // Subscribe to real-time updates
    const eventSource = new EventSource('/api/metrics/stream')

    eventSource.onmessage = (event) => {
      const newDataPoint = JSON.parse(event.data)

      setData(prev => {
        const updated = [...prev, newDataPoint]
        // Keep last 20 data points
        return updated.slice(-20)
      })
    }

    return () => eventSource.close()
  }, [])

  return (
    <div className="p-6 bg-white rounded-lg shadow">
      <h2 className="text-xl font-bold mb-4">Live Traffic</h2>
      <ResponsiveContainer width="100%" height={300}>
        <LineChart data={data}>
          <XAxis dataKey="time" />
          <YAxis />
          <Tooltip />
          <Line
            type="monotone"
            dataKey="value"
            stroke="#8884d8"
            strokeWidth={2}
            dot={false}
            isAnimationActive={false}
          />
        </LineChart>
      </ResponsiveContainer>
    </div>
  )
}
```

**API Route for SSE:**

```typescript
// app/api/metrics/stream/route.ts
export async function GET(req: Request) {
  const encoder = new TextEncoder()

  const stream = new ReadableStream({
    async start(controller) {
      const interval = setInterval(async () => {
        const value = Math.floor(Math.random() * 100)
        const time = new Date().toLocaleTimeString()

        const data = `data: ${JSON.stringify({ time, value })}\n\n`
        controller.enqueue(encoder.encode(data))
      }, 1000)

      // Cleanup on close
      req.signal.addEventListener('abort', () => {
        clearInterval(interval)
        controller.close()
      })
    }
  })

  return new Response(stream, {
    headers: {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
      Connection: 'keep-alive'
    }
  })
}
```

---

### Pattern 3: Interactive Dashboard with Filters

```typescript
// components/SalesDashboard.tsx
'use client'

import { useState } from 'react'
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts'

type Period = '7d' | '30d' | '90d'
type Region = 'all' | 'us' | 'eu' | 'asia'

export default function SalesDashboard() {
  const [period, setPeriod] = useState<Period>('30d')
  const [region, setRegion] = useState<Region>('all')

  const { data, loading } = useSalesData({ period, region })

  return (
    <div className="space-y-6">
      {/* Filters */}
      <div className="flex gap-4">
        <select
          value={period}
          onChange={(e) => setPeriod(e.target.value as Period)}
          className="px-4 py-2 border rounded"
        >
          <option value="7d">Last 7 days</option>
          <option value="30d">Last 30 days</option>
          <option value="90d">Last 90 days</option>
        </select>

        <select
          value={region}
          onChange={(e) => setRegion(e.target.value as Region)}
          className="px-4 py-2 border rounded"
        >
          <option value="all">All Regions</option>
          <option value="us">United States</option>
          <option value="eu">Europe</option>
          <option value="asia">Asia</option>
        </select>
      </div>

      {/* Chart */}
      {loading ? (
        <div>Loading...</div>
      ) : (
        <ResponsiveContainer width="100%" height={400}>
          <BarChart data={data}>
            <XAxis dataKey="date" />
            <YAxis />
            <Tooltip />
            <Bar dataKey="sales" fill="#8884d8" />
          </BarChart>
        </ResponsiveContainer>
      )}
    </div>
  )
}

// Custom hook for data fetching
function useSalesData({ period, region }: { period: Period, region: Region }) {
  const [data, setData] = useState([])
  const [loading, setLoading] = useState(true)

  useEffect(() => {
    setLoading(true)
    fetch(`/api/sales?period=${period}&region=${region}`)
      .then(res => res.json())
      .then(data => {
        setData(data)
        setLoading(false)
      })
  }, [period, region])

  return { data, loading }
}
```

---

## Chart Types Guide

### Line Chart

**Best for:** Trends over time, continuous data

```typescript
<LineChart data={data}>
  <Line type="monotone" dataKey="value" stroke="#8884d8" />
</LineChart>
```

**Use when:**

- Stock prices, temperature, website traffic
- Showing change over time
- Multiple data series comparison

---

### Bar Chart

**Best for:** Comparing categories

```typescript
<BarChart data={data}>
  <Bar dataKey="value" fill="#8884d8" />
</BarChart>
```

**Use when:**

- Sales by product, users by country
- Discrete categories
- Ranking/comparison

---

### Pie/Donut Chart

**Best for:** Part-to-whole relationships

```typescript
<PieChart>
  <Pie data={data} dataKey="value" nameKey="name" fill="#8884d8" />
</PieChart>
```

**Use when:**

- Market share, budget allocation
- Proportions (max 5-7 slices)
- Simple percentages

⚠️ **Avoid when:**

- Too many categories (> 7)
- Precise comparison needed (use bar chart)

---

### Area Chart

**Best for:** Volume over time

```typescript
<AreaChart data={data}>
  <Area type="monotone" dataKey="value" fill="#8884d8" />
</AreaChart>
```

**Use when:**

- Cumulative totals
- Filled regions show magnitude
- Stacked categories

---

### Scatter Plot

**Best for:** Correlation between variables

```typescript
<ScatterChart>
  <Scatter data={data} fill="#8884d8" />
</ScatterChart>
```

**Use when:**

- Finding correlations
- Outlier detection
- Distribution analysis

---

### Heatmap

**Best for:** Intensity across two dimensions

```typescript
// Using D3
const colorScale = d3.scaleSequential(d3.interpolateBlues).domain([0, d3.max(data)])

svg
  .selectAll('rect')
  .data(data)
  .join('rect')
  .attr('fill', d => colorScale(d.value))
```

**Use when:**

- Time-based patterns (day/hour)
- Geographic intensity
- Matrix data

---

## Responsive Design

### Pattern: Mobile-Friendly Charts

```typescript
'use client'

import { useEffect, useState } from 'react'
import { LineChart, Line, ResponsiveContainer } from 'recharts'

export default function ResponsiveChart({ data }) {
  const [isMobile, setIsMobile] = useState(false)

  useEffect(() => {
    const checkMobile = () => setIsMobile(window.innerWidth < 768)
    checkMobile()
    window.addEventListener('resize', checkMobile)
    return () => window.removeEventListener('resize', checkMobile)
  }, [])

  return (
    <ResponsiveContainer width="100%" height={isMobile ? 200 : 400}>
      <LineChart data={data}>
        <Line
          dataKey="value"
          stroke="#8884d8"
          strokeWidth={isMobile ? 1 : 2}
        />
      </LineChart>
    </ResponsiveContainer>
  )
}
```

---

## Color Schemes

### Accessible Colors

```typescript
// colors.ts
export const chartColors = {
  // WCAG AA compliant
  primary: '#0066CC', // Blue
  success: '#007A3D', // Green
  warning: '#C87000', // Orange
  danger: '#D32F2F', // Red

  // Multi-series (colorblind-safe)
  series: [
    '#0066CC', // Blue
    '#CC6600', // Orange
    '#7A00CC', // Purple
    '#00CC66', // Green
    '#CC0066' // Magenta
  ]
}
```

**Colorblind-Safe Palettes:**

```typescript
// For up to 5 data series
const colorblindSafe = [
  '#000000', // Black
  '#E69F00', // Orange
  '#56B4E9', // Sky Blue
  '#009E73', // Green
  '#F0E442' // Yellow
]
```

---

## Data Formatting

### Number Formatting

```typescript
// utils/formatters.ts

export function formatCurrency(value: number): string {
  return new Intl.NumberFormat('en-US', {
    style: 'currency',
    currency: 'USD',
    minimumFractionDigits: 0,
    maximumFractionDigits: 0,
  }).format(value)
}

export function formatPercent(value: number): string {
  return new Intl.NumberFormat('en-US', {
    style: 'percent',
    minimumFractionDigits: 1,
    maximumFractionDigits: 1,
  }).format(value / 100)
}

export function formatNumber(value: number): string {
  if (value >= 1000000) {
    return `${(value / 1000000).toFixed(1)}M`
  }
  if (value >= 1000) {
    return `${(value / 1000).toFixed(1)}K`
  }
  return value.toFixed(0)
}

// Usage in chart
<YAxis tickFormatter={formatCurrency} />
```

---

## Export Functionality

### Export Chart as PNG

```typescript
'use client'

import html2canvas from 'html2canvas'

export function ExportableChart({ children }) {
  const chartRef = useRef<HTMLDivElement>(null)

  const exportToPNG = async () => {
    if (!chartRef.current) return

    const canvas = await html2canvas(chartRef.current)
    const link = document.createElement('a')
    link.download = 'chart.png'
    link.href = canvas.toDataURL()
    link.click()
  }

  return (
    <div>
      <button onClick={exportToPNG} className="mb-4 px-4 py-2 bg-blue-600 text-white rounded">
        Export as PNG
      </button>
      <div ref={chartRef}>
        {children}
      </div>
    </div>
  )
}
```

### Export Data as CSV

```typescript
export function exportToCSV(data: any[], filename: string) {
  const headers = Object.keys(data[0])
  const csv = [
    headers.join(','),
    ...data.map(row => headers.map(h => row[h]).join(','))
  ].join('\n')

  const blob = new Blob([csv], { type: 'text/csv' })
  const link = document.createElement('a')
  link.download = `${filename}.csv`
  link.href = URL.createObjectURL(blob)
  link.click()
}

// Usage
<button onClick={() => exportToCSV(data, 'sales-data')}>
  Export to CSV
</button>
```

---

## Performance Optimization

### Lazy Loading Charts

```typescript
// Lazy load chart libraries (reduce initial bundle)
import dynamic from 'next/dynamic'

const LineChart = dynamic(
  () => import('recharts').then(mod => mod.LineChart),
  { ssr: false }
)

export default function ChartPage() {
  return <LineChart data={data} />
}
```

### Virtualization for Large Datasets

```typescript
import { useVirtualizer } from '@tanstack/react-virtual'

export function LargeDataTable({ data }: { data: any[] }) {
  const parentRef = useRef<HTMLDivElement>(null)

  const virtualizer = useVirtualizer({
    count: data.length,
    getScrollElement: () => parentRef.current,
    estimateSize: () => 50,
  })

  return (
    <div ref={parentRef} className="h-96 overflow-auto">
      <div style={{ height: `${virtualizer.getTotalSize()}px` }}>
        {virtualizer.getVirtualItems().map((virtualRow) => (
          <div key={virtualRow.index} className="py-2 border-b">
            {data[virtualRow.index].name}: {data[virtualRow.index].value}
          </div>
        ))}
      </div>
    </div>
  )
}
```

---

## Animation Best Practices

### Smooth Transitions

```typescript
<LineChart data={data}>
  <Line
    type="monotone"
    dataKey="value"
    stroke="#8884d8"
    animationDuration={500}
    animationEasing="ease-in-out"
  />
</LineChart>
```

### Disable Animation for Real-Time

```typescript
// For real-time dashboards, disable animation
<Line
  dataKey="value"
  isAnimationActive={false}
/>
```

---

## Common Patterns

### Pattern: Drill-Down Chart

```typescript
'use client'

import { useState } from 'react'
import { BarChart, Bar, XAxis, YAxis } from 'recharts'

export default function DrillDownChart() {
  const [level, setLevel] = useState<'year' | 'month' | 'day'>('year')
  const [selectedYear, setSelectedYear] = useState<number | null>(null)

  const handleBarClick = (data: any) => {
    if (level === 'year') {
      setSelectedYear(data.year)
      setLevel('month')
    } else if (level === 'month') {
      setLevel('day')
    }
  }

  const goBack = () => {
    if (level === 'day') setLevel('month')
    else if (level === 'month') {
      setLevel('year')
      setSelectedYear(null)
    }
  }

  return (
    <div>
      {level !== 'year' && (
        <button onClick={goBack} className="mb-4">← Back</button>
      )}

      <BarChart data={getData(level, selectedYear)} width={600} height={300}>
        <Bar dataKey="value" fill="#8884d8" onClick={handleBarClick} />
        <XAxis dataKey="name" />
        <YAxis />
      </BarChart>
    </div>
  )
}
```

---

## When to Use Me

**Perfect for:**

- Building analytics dashboards
- Creating interactive charts
- Data storytelling
- Real-time monitoring
- Visualizing complex datasets

**I'll help you:**

- Choose the right chart type
- Implement responsive layouts
- Add interactivity
- Optimize performance
- Ensure accessibility

## What I'll Create

```
📊 Charts and Visualizations
📈 KPI Dashboards
🎨 Custom Color Schemes
📱 Responsive Layouts
⚡ Real-Time Updates
💾 Export Functionality
```

Let's make your data beautiful and understandable!

© daffy0208 · MIT · daffy0208/ai-dev-standards

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