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Data Visualizer

  • 559 installs
  • 33 repo stars
  • Updated December 25, 2025
  • daffy0208/ai-dev-standards

data-visualizer is a frontend skill that generates interactive charts, KPI dashboards, and exportable visualizations with modern chart libraries for developers who need analytics UI inside their applications.

About

data-visualizer is version 1.0.0 skill in daffy0208/ai-dev-standards labeled expert in charts, dashboards, and data visualizations using modern libraries. The manifest lists domains spanning ai, api, frontend, backend, testing, product, design, and data with medium cost and latency, low risk, and side effects that modify files and create artifacts. A required precondition checks that the project environment is initialized before the skill runs. Developers reach for data-visualizer when adding KPI panels, interactive charts, or exportable visualization components to web or app frontends instead of hand-rolling D3 or chart configs from scratch. The skill composes with frontend and design workflows where observability logs execution time and success rate metrics. Use when visualization is a deliverable inside the product UI rather than a standalone notebook exploration.

  • Creates interactive chart types: line, bar, pie, scatter, and heatmap
  • Builds KPI dashboards with real-time update patterns
  • Designs accessible color schemes and responsive layouts
  • Optimizes chart rendering performance
  • Adds export paths (PNG, CSV)

Data Visualizer by the numbers

  • 559 all-time installs (skills.sh)
  • Ranked #593 of 2,245 Frontend Development skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 30, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs559
repo stars33
Security audit3 / 3 scanners passed
Last updatedDecember 25, 2025
Repositorydaffy0208/ai-dev-standards

How do you add KPI dashboards and charts to an app?

Generate interactive charts, KPI dashboards, and exportable visualizations in your app using modern chart libraries.

Who is it for?

Frontend developers building in-app analytics views who need guided chart and dashboard generation with initialized project environments.

Skip if: Data engineers who only need server-side ETL pipelines without UI components should skip data-visualizer.

When should I use this skill?

The user asks to add charts, KPI dashboards, or exportable data visualizations to a web or app frontend using modern chart libraries.

What you get

Interactive chart components, KPI dashboard UI, and exportable visualization artifacts in the project codebase.

  • Chart components
  • KPI dashboard UI
  • Exportable visualization files

By the numbers

  • Skill manifest version 1.0.0 created 2025-10-30
  • Metadata lists 8 domains: ai, api, frontend, backend, testing, product, design, and data

Files

SKILL.mdMarkdownGitHub ↗

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:

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:

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:

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

// 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

// 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:

// 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

// 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

<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

<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

<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

<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

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

Use when:

  • Finding correlations
  • Outlier detection
  • Distribution analysis

---

Heatmap

Best for: Intensity across two dimensions

// 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

'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

// 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:

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

---

Data Formatting

Number Formatting

// 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

'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

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

// 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

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

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

Disable Animation for Real-Time

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

---

Common Patterns

Pattern: Drill-Down Chart

'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!

Related skills

How it compares

Use data-visualizer for in-app chart and dashboard UI; use notebook-centric data science skills when visualization stays in analysis environments only.

FAQ

What precondition does data-visualizer require?

data-visualizer requires project_initialized as a true precondition, ensuring the project environment is set up before the skill modifies files and creates chart or dashboard artifacts.

Which domains does data-visualizer cover?

data-visualizer version 1.0.0 lists domains including ai, api, frontend, backend, testing, product, design, and data, with medium cost and latency and low risk in its skill manifest.

Is Data Visualizer safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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