
Build Dashboard
OfficialFreeCreate interactive HTML dashboards effortlessly.
Free · Opens the source repo
What Build Dashboard does
The Build Dashboard skill allows users to create self-contained interactive HTML dashboards that include charts, filters, and tables. This skill is particularly useful for generating executive overviews, team monitoring snapshots, and detailed reports that can be shared easily. By following a structured workflow, users can define their dashboard's purpose, gather data from various sources, and design a layout that meets their needs. The resulting dashboard is a single HTML file that opens directly in a browser, requiring no additional server or dependencies.
To begin using the skill, users must first understand the requirements of their dashboard, including the key metrics and dimensions they want to display. The skill supports multiple data sources, allowing users to embed live query results, pasted data, or even sample data directly into the dashboard. This flexibility makes it suitable for a variety of use cases, from operational monitoring to deep-dive analysis.
Once the data is gathered, users can design their dashboard layout using a standard pattern that includes KPI cards, charts, and a detail table. The skill provides a base template that incorporates semantic HTML, CSS for styling, and JavaScript for interactivity, including Chart.js integration for visualizing data. Users can implement various chart types, such as line, bar, and doughnut charts, and add interactivity through filters and sortable tables. This feature-rich environment allows users to create professional-looking dashboards that are fully functional and visually appealing.
The Build Dashboard skill is ideal for developers and designers looking to streamline their reporting processes or create dynamic visualizations of data. Its ease of use and comprehensive features make it a valuable tool for anyone needing to present data in an engaging and accessible format.
When to use it
Use this skill when you need to create a professional dashboard for data visualization or reporting purposes.
When not to use it
This skill may not be suitable for highly specialized dashboard requirements or when integration with external data sources is necessary.
What you can build with it
Executive Overview
Create a high-level dashboard showcasing key performance indicators for stakeholders.
Team Monitoring
Build a dashboard to track team metrics and performance over time.
Data Reporting
Generate a detailed report dashboard that can be shared with clients or team members.
How to install Build Dashboard
View source1. Install with the skills CLI
npx skills add anthropics/knowledge-work-plugins/build-dashboard --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by anthropics/build-dashboard - Build Interactive Dashboards
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Build a self-contained interactive HTML dashboard with charts, filters, tables, and professional styling. Opens directly in a browser -- no server or dependencies required.
Usage
/build-dashboard <description of dashboard> [data source]
Workflow
1. Understand the Dashboard Requirements
Determine:
- Purpose: Executive overview, operational monitoring, deep-dive analysis, team reporting
- Audience: Who will use this dashboard?
- Key metrics: What numbers matter most?
- Dimensions: What should users be able to filter or slice by?
- Data source: Live query, pasted data, CSV file, or sample data
2. Gather the Data
If data warehouse is connected:
- Query the necessary data
- Embed the results as JSON within the HTML file
If data is pasted or uploaded:
- Parse and clean the data
- Embed as JSON in the dashboard
If working from a description without data:
- Create a realistic sample dataset matching the described schema
- Note in the dashboard that it uses sample data
- Provide instructions for swapping in real data
3. Design the Dashboard Layout
Follow a standard dashboard layout pattern:
┌──────────────────────────────────────────────────┐
│ Dashboard Title [Filters ▼] │
├────────────┬────────────┬────────────┬───────────┤
│ KPI Card │ KPI Card │ KPI Card │ KPI Card │
├────────────┴────────────┼────────────┴───────────┤
│ │ │
│ Primary Chart │ Secondary Chart │
│ (largest area) │ │
│ │ │
├─────────────────────────┴────────────────────────┤
│ │
│ Detail Table (sortable, scrollable) │
│ │
└──────────────────────────────────────────────────┘
Adapt the layout to the content:
- 2-4 KPI cards at the top for headline numbers
- 1-3 charts in the middle section for trends and breakdowns
- Optional detail table at the bottom for drill-down data
- Filters in the header or sidebar depending on complexity
4. Build the HTML Dashboard
Generate a single self-contained HTML file using the base template below. The file includes:
Structure (HTML):
- Semantic HTML5 layout
- Responsive grid using CSS Grid or Flexbox
- Filter controls (dropdowns, date pickers, toggles)
- KPI cards with values and labels
- Chart containers
- Data table with sortable headers
Styling (CSS):
- Professional color scheme (clean whites, grays, with accent colors for data)
- Card-based layout with subtle shadows
- Consistent typography (system fonts for fast loading)
- Responsive design that works on different screen sizes
- Print-friendly styles
Interactivity (JavaScript):
- Chart.js for interactive charts (included via CDN)
- Filter dropdowns that update all charts and tables simultaneously
- Sortable table columns
- Hover tooltips on charts
- Number formatting (commas, currency, percentages)
Data (embedded JSON):
- All data embedded directly in the HTML as JavaScript variables
- No external data fetches required
- Dashboard works completely offline
5. Implement Chart Types
Use Chart.js for all charts. Common dashboard chart patterns:
- Line chart: Time series trends
- Bar chart: Category comparisons
- Doughnut chart: Composition (when <6 categories)
- Stacked bar: Composition over time
- Mixed (bar + line): Volume with rate overlay
Use the Chart.js integration patterns below for each chart type.
6. Add Interactivity
Use the filter and interactivity implementation patterns below for dropdown filters, date range filters, combined filter logic, sortable tables, and chart updates.
7. Save and Open
- Save the dashboard as an HTML file with a descriptive name (e.g.,
sales_dashboard.html) - Open it in the user's default browser
- Confirm it renders correctly
- Provide instructions for updating data or customizing
Base Template
Every dashboard follows this structure:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Dashboard Title</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.5.1" integrity="sha384-jb8JQMbMoBUzgWatfe6COACi2ljcDdZQ2OxczGA3bGNeWe+6DChMTBJemed7ZnvJ" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/chartjs-adapter-date-fns@3.0.0" integrity="sha384-cVMg8E3QFwTvGCDuK+ET4PD341jF3W8nO1auiXfuZNQkzbUUiBGLsIQUE+b1mxws" crossorigin="anonymous"></script>
<style>
/* Dashboard styles go here */
</style>
</head>
<body>
<div class="dashboard-container">
<header class="dashboard-header">
<h1>Dashboard Title</h1>
<div class="filters">
<!-- Filter controls -->
</div>
</header>
<section class="kpi-row">
<!-- KPI cards -->
</section>
<section class="chart-row">
<!-- Chart containers -->
</section>
<section class="table-section">
<!-- Data table -->
</section>
<footer class="dashboard-footer">
<span>Data as of: <span id="data-date"></span></span>
</footer>
</div>
<script>
// Embedded data
const DATA = [];
// Dashboard logic
class Dashboard {
constructor(data) {
this.rawData = data;
this.filteredData = data;
this.charts = {};
this.init();
}
init() {
this.setupFilters();
this.renderKPIs();
this.renderCharts();
this.renderTable();
}
applyFilters() {
// Filter logic
this.filteredData = this.rawData.filter(row => {
// Apply each active filter
return true; // placeholder
});
this.renderKPIs();
this.updateCharts();
this.renderTable();
}
// ... methods for each section
}
const dashboard = new Dashboard(DATA);
</script>
</body>
</html>
KPI Card Pattern
<div class="kpi-card">
<div class="kpi-label">Total Revenue</div>
<div class="kpi-value" id="kpi-revenue">$0</div>
<div class="kpi-change positive" id="kpi-revenue-change">+0%</div>
</div>
function renderKPI(elementId, value, previousValue, format = 'number') {
const el = document.getElementById(elementId);
const changeEl = document.getElementById(elementId + '-change');
// Format the value
el.textContent = formatValue(value, format);
// Calculate and display change
if (previousValue && previousValue !== 0) {
const pctChange = ((value - previousValue) / previousValue) * 100;
const sign = pctChange >= 0 ? '+' : '';
changeEl.textContent = `${sign}${pctChange.toFixed(1)}% vs prior period`;
changeEl.className = `kpi-change ${pctChange >= 0 ? 'positive' : 'negative'}`;
}
}
function formatValue(value, format) {
switch (format) {
case 'currency':
if (value >= 1e6) return `$${(value / 1e6).toFixed(1)}M`;
if (value >= 1e3) return `$${(value / 1e3).toFixed(1)}K`;
return `$${value.toFixed(0)}`;
case 'percent':
return `${value.toFixed(1)}%`;
case 'number':
if (value >= 1e6) return `${(value / 1e6).toFixed(1)}M`;
if (value >= 1e3) return `${(value / 1e3).toFixed(1)}K`;
return value.toLocaleString();
default:
return value.toString();
}
}
Chart.js Integration
Chart Container Pattern
<div class="chart-container">
<h3 class="chart-title">Monthly Revenue Trend</h3>
<canvas id="revenue-chart"></canvas>
</div>
Line Chart
function createLineChart(canvasId, labels, datasets) {
const ctx = document.getElementById(canvasId).getContext('2d');
return new Chart(ctx, {
type: 'line',
data: {
labels: labels,
datasets: datasets.map((ds, i) => ({
label: ds.label,
data: ds.data,
borderColor: COLORS[i % COLORS.length],
backgroundColor: COLORS[i % COLORS.length] + '20',
borderWidth: 2,
fill: ds.fill || false,
tension: 0.3,
pointRadius: 3,
pointHoverRadius: 6,
}))
},
options: {
responsive: true,
maintainAspectRatio: false,
interaction: {
mode: 'index',
intersect: false,
},
plugins: {
legend: {
position: 'top',
labels: { usePointStyle: true, padding: 20 }
},
tooltip: {
callbacks: {
label: function(context) {
return `${context.dataset.label}: ${formatValue(context.parsed.y, 'currency')}`;
}
}
}
},
scales: {
x: {
grid: { display: false }
},
y: {
beginAtZero: true,
ticks: {
callback: function(value) {
return formatValue(value, 'currency');
}
}
}
}
}
});
}
Bar Chart
function createBarChart(canvasId, labels, data, options = {}) {
const ctx = document.getElementById(canvasId).getContext('2d');
const isHorizontal = options.horizontal || labels.length > 8;
return new Chart(ctx, {
type: 'bar',
data: {
labels: labels,
datasets: [{
label: options.label || 'Value',
data: data,
backgroundColor: options.colors || COLORS.map(c => c + 'CC'),
borderColor: options.colors || COLORS,
borderWidth: 1,
borderRadius: 4,
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
indexAxis: isHorizontal ? 'y' : 'x',
plugins: {
legend: { display: false },
tooltip: {
callbacks: {
label: function(context) {
return formatValue(context.parsed[isHorizontal ? 'x' : 'y'], options.format || 'number');
}
}
}
},
scales: {
x: {
beginAtZero: true,
grid: { display: isHorizontal },
ticks: isHorizontal ? {
callback: function(value) {
return formatValue(value, options.format || 'number');
}
} : {}
},
y: {
beginAtZero: !isHorizontal,
grid: { display: !isHorizontal },
ticks: !isHorizontal ? {
callback: function(value) {
return formatValue(value, options.format || 'number');
}
} : {}
}
}
}
});
}
Doughnut Chart
function createDoughnutChart(canvasId, labels, data) {
const ctx = document.getElementById(canvasId).getContext('2d');
return new Chart(ctx, {
type: 'doughnut',
data: {
labels: labels,
datasets: [{
data: data,
backgroundColor: COLORS.map(c => c + 'CC'),
borderColor: '#ffffff',
borderWidth: 2,
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
cutout: '60%',
plugins: {
legend: {
position: 'right',
labels: { usePointStyle: true, padding: 15 }
},
tooltip: {
callbacks: {
label: function(context) {
const total = context.dataset.data.reduce((a, b) => a + b, 0);
const pct = ((context.parsed / total) * 100).toFixed(1);
return `${context.label}: ${formatValue(context.parsed, 'number')} (${pct}%)`;
}
}
}
}
}
});
}
Updating Charts on Filter Change
function updateChart(chart, newLabels, newData) {
chart.data.labels = newLabels;
if (Array.isArray(newData[0])) {
// Multiple datasets
newData.forEach((data, i) => {
chart.data.datasets[i].data = data;
});
} else {
chart.data.datasets[0].data = newData;
}
chart.update('none'); // 'none' disables animation for instant update
}
Filter and Interactivity Implementation
Dropdown Filter
<div class="filter-group">
<label for="filter-region">Region</label>
<select id="filter-region" onchange="dashboard.applyFilters()">
<option value="all">All Regions</option>
</select>
</div>
function populateFilter(selectId, data, field) {
const select = document.getElementById(selectId);
const values = [...new Set(data.map(d => d[field]))].sort();
// Keep the "All" option, add unique values
values.forEach(val => {
const option = document.createElement('option');
option.value = val;
option.textContent = val;
select.appendChild(option);
});
}
function getFilterValue(selectId) {
const val = document.getElementById(selectId).value;
return val === 'all' ? null : val;
}
Date Range Filter
<div class="filter-group">
<label>Date Range</label>
<input type="date" id="filter-date-start" onchange="dashboard.applyFilters()">
<span>to</span>
<input type="date" id="filter-date-end" onchange="dashboard.applyFilters()">
</div>
function filterByDateRange(data, dateField, startDate, endDate) {
return data.filter(row => {
const rowDate = new Date(row[dateField]);
if (startDate && rowDate < new Date(startDate)) return false;
if (endDate && rowDate > new Date(endDate)) return false;
return true;
});
}
Combined Filter Logic
applyFilters() {
const region = getFilterValue('filter-region');
const category = getFilterValue('filter-category');
const startDate = document.getElementById('filter-date-start').value;
const endDate = document.getElementById('filter-date-end').value;
this.filteredData = this.rawData.filter(row => {
if (region && row.region !== region) return false;
if (category && row.category !== category) return false;
if (startDate && row.date < startDate) return false;
if (endDate && row.date > endDate) return false;
return true;
});
this.renderKPIs();
this.updateCharts();
this.renderTable();
}
Sortable Table
function renderTable(containerId, data, columns) {
const container = document.getElementById(containerId);
let sortCol = null;
let sortDir = 'desc';
function render(sortedData) {
let html = '<table class="data-table">';
// Header
html += '<thead><tr>';
columns.forEach(col => {
const arrow = sortCol === col.field
? (sortDir === 'asc' ? ' ▲' : ' ▼')
: '';
html += `<th onclick="sortTable('${col.field}')" style="cursor:pointer">${col.label}${arrow}</th>`;
});
html += '</tr></thead>';
// Body
html += '<tbody>';
sortedData.forEach(row => {
html += '<tr>';
columns.forEach(col => {
const value = col.format ? formatValue(row[col.field], col.format) : row[col.field];
html += `<td>${value}</td>`;
});
html += '</tr>';
});
html += '</tbody></table>';
container.innerHTML = html;
}
window.sortTable = function(field) {
if (sortCol === field) {
sortDir = sortDir === 'asc' ? 'desc' : 'asc';
} else {
sortCol = field;
sortDir = 'desc';
}
const sorted = [...data].sort((a, b) => {
const aVal = a[field], bVal = b[field];
const cmp = aVal < bVal ? -1 : aVal > bVal ? 1 : 0;
return sortDir === 'asc' ? cmp : -cmp;
});
render(sorted);
};
render(data);
}
CSS Styling for Dashboards
Color System
:root {
/* Background layers */
--bg-primary: #f8f9fa;
--bg-card: #ffffff;
--bg-header: #1a1a2e;
/* Text */
--text-primary: #212529;
--text-secondary: #6c757d;
--text-on-dark: #ffffff;
/* Accent colors for data */
--color-1: #4C72B0;
--color-2: #DD8452;
--color-3: #55A868;
--color-4: #C44E52;
--color-5: #8172B3;
--color-6: #937860;
/* Status colors */
--positive: #28a745;
--negative: #dc3545;
--neutral: #6c757d;
/* Spacing */
--gap: 16px;
--radius: 8px;
}
Layout
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
background: var(--bg-primary);
color: var(--text-primary);
line-height: 1.5;
}
.dashboard-container {
max-width: 1400px;
margin: 0 auto;
padding: var(--gap);
}
.dashboard-header {
background: var(--bg-header);
color: var(--text-on-dark);
padding: 20px 24px;
border-radius: var(--radius);
margin-bottom: var(--gap);
display: flex;
justify-content: space-between;
align-items: center;
flex-wrap: wrap;
gap: 12px;
}
.dashboard-header h1 {
font-size: 20px;
font-weight: 600;
}
KPI Cards
.kpi-row {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: var(--gap);
margin-bottom: var(--gap);
}
.kpi-card {
background: var(--bg-card);
border-radius: var(--radius);
padding: 20px 24px;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08);
}
.kpi-label {
font-size: 13px;
color: var(--text-secondary);
text-transform: uppercase;
letter-spacing: 0.5px;
margin-bottom: 4px;
}
.kpi-value {
font-size: 28px;
font-weight: 700;
color: var(--text-primary);
margin-bottom: 4px;
}
.kpi-change {
font-size: 13px;
font-weight: 500;
}
.kpi-change.positive { color: var(--positive); }
.kpi-change.negative { color: var(--negative); }
Chart Containers
.chart-row {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(400px, 1fr));
gap: var(--gap);
margin-bottom: var(--gap);
}
.chart-container {
background: var(--bg-card);
border-radius: var(--radius);
padding: 20px 24px;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08);
}
.chart-container h3 {
font-size: 14px;
font-weight: 600;
color: var(--text-primary);
margin-bottom: 16px;
}
.chart-container canvas {
max-height: 300px;
}
Filters
.filters {
display: flex;
gap: 12px;
align-items: center;
flex-wrap: wrap;
}
.filter-group {
display: flex;
align-items: center;
gap: 6px;
}
.filter-group label {
font-size: 12px;
color: rgba(255, 255, 255, 0.7);
}
.filter-group select,
.filter-group input[type="date"] {
padding: 6px 10px;
border: 1px solid rgba(255, 255, 255, 0.2);
border-radius: 4px;
background: rgba(255, 255, 255, 0.1);
color: var(--text-on-dark);
font-size: 13px;
}
.filter-group select option {
background: var(--bg-header);
color: var(--text-on-dark);
}
Data Table
.table-section {
background: var(--bg-card);
border-radius: var(--radius);
padding: 20px 24px;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08);
overflow-x: auto;
}
.data-table {
width: 100%;
border-collapse: collapse;
font-size: 13px;
}
.data-table thead th {
text-align: left;
padding: 10px 12px;
border-bottom: 2px solid #dee2e6;
color: var(--text-secondary);
font-weight: 600;
font-size: 12px;
text-transform: uppercase;
letter-spacing: 0.5px;
white-space: nowrap;
user-select: none;
}
.data-table thead th:hover {
color: var(--text-primary);
background: #f8f9fa;
}
.data-table tbody td {
padding: 10px 12px;
border-bottom: 1px solid #f0f0f0;
}
.data-table tbody tr:hover {
background: #f8f9fa;
}
.data-table tbody tr:last-child td {
border-bottom: none;
}
Responsive Design
@media (max-width: 768px) {
.dashboard-header {
flex-direction: column;
align-items: flex-start;
}
.kpi-row {
grid-template-columns: repeat(2, 1fr);
}
.chart-row {
grid-template-columns: 1fr;
}
.filters {
flex-direction: column;
align-items: flex-start;
}
}
@media print {
body { background: white; }
.dashboard-container { max-width: none; }
.filters { display: none; }
.chart-container { break-inside: avoid; }
.kpi-card { border: 1px solid #dee2e6; box-shadow: none; }
}
Performance Considerations for Large Datasets
Data Size Guidelines
| Data Size | Approach |
|---|---|
| <1,000 rows | Embed directly in HTML. Full interactivity. |
| 1,000 - 10,000 rows | Embed in HTML. May need to pre-aggregate for charts. |
| 10,000 - 100,000 rows | Pre-aggregate server-side. Embed only aggregated data. |
| >100,000 rows | Not suitable for client-side dashboard. Use a BI tool or paginate. |
Pre-Aggregation Pattern
Instead of embedding raw data and aggregating in the browser:
// DON'T: embed 50,000 raw rows
const RAW_DATA = [/* 50,000 rows */];
// DO: pre-aggregate before embedding
const CHART_DATA = {
monthly_revenue: [
{ month: '2024-01', revenue: 150000, orders: 1200 },
{ month: '2024-02', revenue: 165000, orders: 1350 },
// ... 12 rows instead of 50,000
],
top_products: [
{ product: 'Widget A', revenue: 45000 },
// ... 10 rows
],
kpis: {
total_revenue: 1980000,
total_orders: 15600,
avg_order_value: 127,
}
};
Chart Performance
- Limit line charts to <500 data points per series (downsample if needed)
- Limit bar charts to <50 categories
- For scatter plots, cap at 1,000 points (use sampling for larger datasets)
- Disable animations for dashboards with many charts:
animation: falsein Chart.js options - Use
Chart.update('none')instead ofChart.update()for filter-triggered updates
DOM Performance
- Limit data tables to 100-200 visible rows. Add pagination for more.
- Use
requestAnimationFramefor coordinated chart updates - Avoid rebuilding the entire DOM on filter change -- update only changed elements
// Efficient table pagination
function renderTablePage(data, page, pageSize = 50) {
const start = page * pageSize;
const end = Math.min(start + pageSize, data.length);
const pageData = data.slice(start, end);
// Render only pageData
// Show pagination controls: "Showing 1-50 of 2,340"
}
Examples
/build-dashboard Monthly sales dashboard with revenue trend, top products, and regional breakdown. Data is in the orders table.
/build-dashboard Here's our support ticket data [pastes CSV]. Build a dashboard showing volume by priority, response time trends, and resolution rates.
/build-dashboard Create a template executive dashboard for a SaaS company showing MRR, churn, new customers, and NPS. Use sample data.
Tips
- Dashboards are fully self-contained HTML files -- share them with anyone by sending the file
- For real-time dashboards, consider connecting to a BI tool instead. These dashboards are point-in-time snapshots
- Request "dark mode" or "presentation mode" for different styling
- You can request a specific color scheme to match your brand
Frequently asked questions about Build Dashboard
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