Dashboard Builder
Data and analytics skill, available on Zeplik
Dashboard Builder is a ready-to-run data and analytics skill on Zeplik. Not for choosing which KPIs to track or dashboard design strategy (use kpi-dashboard-design). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer. It returns a structured dashboard you can keep and reuse: Self-contained HTML dashboard -- metric-tile grid (value + signed delta), supporting charts/tables, optional client-side filters (see artifact-templates/dashboard.md).
The Dashboard Builder skill loads automatically when your request matches it, or you can invoke it directly by typing /build-dashboard in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the Dashboard Builder skill can do
- Build a self-contained interactive HTML dashboard file
- Embed KPI cards, charts, filters and sortable tables in one page
- Generate realistic sample data when no data source is provided
- Wire up Chart.js visualizations with dropdown and date filters
Try these prompts on Zeplik
Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the Dashboard Builder skill works
/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/[email protected]" integrity="sha384-jb8JQMbMoBUzgWatfe6COACi2ljcDdZQ2OxczGA3bGNeWe+6DChMTBJemed7ZnvJ" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]" 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
Zeplik output presentation
Present the final deliverable as a single polished artifact: clear headings, tables where the content is tabular, fenced code where it is code. Lead with the deliverable itself; keep process commentary to a single short line. If the skill produced multiple files or sections, end with a compact list of them with one-line purposes.
How to use the Dashboard Builder skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Dashboard Builder skill right away.
Describe your data and analytics task
Ask in plain language, or type /build-dashboard to invoke the skill directly. Zeplik recognizes the Dashboard Builder skill and applies its method.
Review and refine the result
Zeplik returns a structured dashboard you can edit, download, and reuse. Ask follow-ups to refine it.
Source and credit
- Author
- Anthropic
- License
- Apache-2.0
Adapted from the open-source anthropics/knowledge-work-plugins project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the Dashboard Builder skill?
- Dashboard Builder is a ready-to-run data and analytics skill on Zeplik. Not for choosing which KPIs to track or dashboard design strategy (use kpi-dashboard-design). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer. It returns a structured dashboard you can keep and reuse: Self-contained HTML dashboard -- metric-tile grid (value + signed delta), supporting charts/tables, optional client-side filters (see artifact-templates/dashboard.md).
- How do I use Dashboard Builder on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /build-dashboard in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the Dashboard Builder skill use?
- Any model you choose. Zeplik works across every model in one chat, so the Dashboard Builder skill runs on your preferred model for the task.
- Where does the Dashboard Builder skill come from?
- The Dashboard Builder skill is adapted from the open-source anthropics/knowledge-work-plugins project (Apache-2.0) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the Dashboard Builder skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
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- Chart & Visualization MakerUse when the user wants a data chart from query results, a DataFrame, or a CSV: 'plot this', 'make a chart of revenue over time', 'visualize these results', picking the right chart type and producing publication-quality Python plots. Not for flowcharts or architecture diagrams (use diagram).
- ClickHouseClickHouse for high-performance analytics: query optimization, schema design, data engineering patterns
- CocoIndex ETLBuild CocoIndex ETL flows: embed docs to vector DBs, knowledge graphs, search indexes with incremental updates
- Data & ML EngineeringUse when building data pipelines or ML infra -- Airflow DAGs, dbt models, Spark tuning, data quality checks, MLOps pipelines, recommenders. Not for one-off dataset analysis (use analyze-dataset).
- Data Context ExtractorUse when extracting tribal data knowledge -- entity definitions, metric formulas, standard filters, gotchas -- into a reusable context doc. Not for exploring a pasted dataset (use explore-data).
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