build-dashboard

name	build-dashboard
description	Build an interactive HTML dashboard — a Data Schematic — with charts, filters, and tables. Use when creating a dashboard, an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
argument-hint	<description> [data source]

/build-dashboard - Build Interactive Data Schematics

Build a self-contained interactive HTML Data Schematic with charts, filters, tables, and professional styling. Opens directly in a browser -- no server or dependencies required.

Usage

/build-dashboard <description of Data Schematic> [data source]

Workflow

1. Understand the Data Schematic Requirements

Determine:

• Purpose: Executive overview, operational monitoring, deep-dive analysis, team reporting
• Audience: Who will use this Data Schematic?
• Key metrics: What numbers matter most?
• Dimensions: What should users be able to filter or slice by?
• Data source: CSV/TSV file, query results, pasted data, or sample data

2. Gather the Data

If data is in CSV/TSV files (common with qsv):

1. Use qsv commands to explore and pre-aggregate the data:
   • qsv stats --cardinality --stats-jsonl to understand columns and distributions
   • qsv frequency to see value distributions for categorical columns
   • qsv sqlp to run SQL aggregations (GROUP BY, SUM, COUNT, etc.)
   • qsv slice to sample large files before embedding
2. Embed the aggregated results as JSON within the HTML file

If data is pasted or uploaded:

1. Parse and clean the data
2. Embed as JSON in the Data Schematic

If working from a description without data:

1. Create a realistic sample dataset matching the described schema
2. Note in the Data Schematic that it uses sample data
3. Provide instructions for swapping in real data

3. Design the Data Schematic Layout

Follow a standard Data Schematic layout pattern:

+--------------------------------------------------+
|  Data Schematic Title                    [Filters v]  |
+------------+------------+------------+-----------+
|  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 Data Schematic

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
• Data Schematic works completely offline

5. Implement Chart Types

Use Chart.js for all charts. Common Data Schematic 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

1. Save the Data Schematic as an HTML file with a descriptive name (e.g., sales_dashboard.html)
2. Open it in the user's default browser
3. Confirm it renders correctly
4. Provide instructions for updating data or customizing

---

Data Preparation with qsv

Before embedding data, use qsv to pre-aggregate large datasets:

# Get column stats and cardinality
qsv stats data.csv --cardinality --stats-jsonl

# Aggregate with SQL for chart-ready data
qsv sqlp data.csv "SELECT month, SUM(revenue) as revenue, COUNT(*) as orders FROM data GROUP BY month ORDER BY month"

# Get frequency distributions for categorical breakdowns
qsv frequency data.csv --select category --limit 10

# Sample large files for detail tables
qsv slice data.csv --len 200

For datasets over 100K rows, always pre-aggregate with qsv sqlp rather than embedding raw data.

---

Base Template

Every Data Schematic 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>Data Schematic 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>
        /* Data Schematic styles go here */
    </style>
</head>
<body>
    <div class="data-schematic-container">
        < =>
            Data Schematic Title
            
                
            
        

        
            
        

        
            
        

        
            
        

        
            Data as of: 
        
    

    

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 >= )  ;
             ;
         :
             ;
         :
             (value >= )  ;
             (value >= )  ;
             value.();
        :
             value.();
    }
}

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: {
                : ,
                : ,
            },
            : {
                : {
                    : ,
                    : { : , :  }
                },
                : {
                    : {
                        : () {
                             ;
                        }
                    }
                }
            },
            : {
                : {
                    : { :  }
                },
                : {
                    : ,
                    : {
                        : () {
                             (value, );
                        }
                    }
                }
            }
        }
    });
}

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:  },
                : {
                    : {
                        : () {
                             (context.[isHorizontal ?  : ], options. || );
                        }
                    }
                }
            },
            : {
                : {
                    : ,
                    : { : isHorizontal },
                    : isHorizontal ? {
                        : () {
                             (value, options. || );
                        }
                    } : {}
                },
                : {
                    : !isHorizontal,
                    : { : !isHorizontal },
                    : !isHorizontal ? {
                        : () {
                             (value, options. || );
                        }
                    } : {}
                }
            }
        }
    });
}

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) {
                             total = context...( a + b, );
                             pct = ((context. / total) * ).();
                             ;
                        }
                    }
                }
            }
        }
    });
}

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="dataSchematic.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="dataSchematic.applyFilters()">
    <span>to</span>
    <input type="date" id="filter-date-end" onchange="dataSchematic.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></td>`;
            });
            html += ;
        });
        html += ;

        container. = html;
    }

    . = () {
         (sortCol === field) {
            sortDir = sortDir ===  ?  : ;
        }  {
            sortCol = field;
            sortDir = ;
        }
         sorted = [...data].( {
             aVal = a[field], bVal = b[field];
             cmp = aVal < bVal ? - : aVal > bVal ?  : ;
             sortDir ===  ? cmp : -cmp;
        });
        (sorted);
    };

    (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;
}

.data-schematic-container {
    max-width: 1400px;
    margin: 0 auto;
    padding: var(--gap);
}

.data-schematic-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;
}

.data-schematic-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;
}

 { : (--positive); }
 { : (--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: (--bg-header);
    : (--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);
    : ;
}

   {
    :  ;
    :  solid ;
}

   {
    : ;
}

    {
    : none;
}

Responsive Design

@media (max-width: 768px) {
    .data-schematic-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; }
    .data-schematic-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 with qsv sqlp. Embed only aggregated data.
>100,000 rows	Pre-aggregate heavily with qsv sqlp. Consider qsv_to_parquet for archival. Use pagination for detail tables.

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 with qsv sqlp 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: false in Chart.js options
• Use Chart.update('none') instead of Chart.update() for filter-triggered updates

DOM Performance

• Limit data tables to 100-200 visible rows. Add pagination for more.
• Use requestAnimationFrame for 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 Data Schematic with revenue trend, top products, and regional breakdown from sales.csv

/build-dashboard Here's our support ticket data [pastes CSV]. Build a Data Schematic showing volume by priority, response time trends, and resolution rates.

/build-dashboard Create a template executive Data Schematic 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
• Use qsv stats and qsv frequency to understand your data before designing the Data Schematic layout
• Use qsv sqlp for complex aggregations (GROUP BY, window functions, pivots) before embedding
• For real-time dashboards, consider 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