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Creating Dashboards

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ancoleman
creating-dashboards

Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.

Overview

Publisherancoleman
Repositoryai-design-components
Skill namecreating-dashboards
Stars
523
Forks
73
Bundled files
23
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 23 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ancoleman on GitHub. Read the source before you install it.

Installation

Install the Creating Dashboards AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ancoleman/ai-design-components.git /tmp/ai-design-components
mkdir -p .claude/skills
cp -r /tmp/ai-design-components/skills/creating-dashboards .claude/skills/creating-dashboards
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Creating Dashboards in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Creating Dashboards on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Creating Dashboards is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Creating Dashboards

Purpose

This skill enables the creation of sophisticated dashboard interfaces that aggregate and present data through coordinated widgets including KPI cards, charts, tables, and filters. Dashboards serve as centralized command centers for data-driven decision making, combining multiple component types from other skills (data-viz, tables, design-tokens) into unified analytics experiences with real-time updates, responsive layouts, and interactive filtering.

When to Use

Activate this skill when:

  • Building business intelligence or analytics dashboards
  • Creating executive reporting interfaces
  • Implementing real-time monitoring systems
  • Designing KPI displays with metrics and trends
  • Developing customizable widget-based layouts
  • Coordinating filters across multiple data displays
  • Building responsive data-heavy interfaces
  • Implementing drag-and-drop dashboard editors
  • Creating template-based analytics systems
  • Designing multi-tenant SaaS dashboards

Core Dashboard Elements

KPI Card Anatomy

┌────────────────────────────┐
│ Revenue (This Month)       │ ← Label with time period
│                            │
│  $1,245,832               │ ← Big number (primary metric)
│  ↑ 15.3% vs last month    │ ← Trend indicator with comparison
│  ▂▃▅▆▇█ (sparkline)       │ ← Mini visualization
└────────────────────────────┘

Widget Container Structure

  • Title bar with widget name and actions
  • Loading state (skeleton or spinner)
  • Error boundary with retry option
  • Resize handles for adjustable layouts
  • Settings menu (export, configure, refresh)

Dashboard Layout Types

Fixed Layout: Designer-defined placement, consistent across users Customizable Grid: User drag-and-drop, resizable widgets, saved layouts Template-Based: Pre-built patterns, industry-specific starting points

Global Dashboard Controls

  • Date range picker (affects all widgets)
  • Filter panel (coordinated across widgets)
  • Refresh controls (manual/auto-refresh)
  • Export actions (PDF, image, data)
  • Theme switcher (light/dark/custom)

Implementation Approach

1. Choose Dashboard Architecture

For Quick Analytics Dashboard → Use Tremor Pre-built KPI cards, charts, and tables with minimal code:

bash
npm install @tremor/react

For Customizable Dashboard → Use react-grid-layout Drag-and-drop, resizable widgets, user-defined layouts:

bash
npm install react-grid-layout

2. Set Up Global State Management

Implement filter context for cross-widget coordination:

tsx
// Dashboard context for shared filters
const DashboardContext = createContext({
  filters: { dateRange: null, categories: [] },
  setFilters: () => {},
  refreshInterval: 30000
});

// Wrap dashboard with provider
<DashboardContext.Provider value={dashboardState}>
  <FilterPanel />
  <WidgetGrid />
</DashboardContext.Provider>

3. Implement Data Fetching Strategy

Parallel Loading: Fetch all widget data simultaneously Lazy Loading: Load visible widgets first, others on scroll Cached Updates: Serve from cache while fetching fresh data

4. Configure Real-Time Updates

Server-Sent Events (Recommended for Dashboards):

tsx
const eventSource = new EventSource('/api/dashboard/stream');
eventSource.onmessage = (event) => {
  const update = JSON.parse(event.data);
  updateWidget(update.widgetId, update.data);
};

5. Apply Responsive Design

Define breakpoints for different screen sizes:

  • Desktop (>1200px): Multi-column grid
  • Tablet (768-1200px): 2-column layout
  • Mobile (<768px): Single column stack

Quick Start with Tremor

Basic KPI Dashboard

tsx
import { Card, Grid, Metric, Text, BadgeDelta, AreaChart } from '@tremor/react';

function QuickDashboard({ data }) {
  return (
    <Grid numItems={1} numItemsSm={2} numItemsLg={4} className="gap-4">
      {/* KPI Cards */}
      <Card>
        <Text>Total Revenue</Text>
        <Metric>$45,231.89</Metric>
        <BadgeDelta deltaType="increase">+12.5%</BadgeDelta>
      </Card>

      <Card>
        <Text>Active Users</Text>
        <Metric>1,234</Metric>
        <BadgeDelta deltaType="decrease">-2.3%</BadgeDelta>
      </Card>

      {/* Chart Widget */}
      <Card className="lg:col-span-2">
        <Text>Revenue Trend</Text>
        <AreaChart
          data={data.revenue}
          index="date"
          categories={["revenue"]}
          valueFormatter={(value) => `$${value.toLocaleString()}`}
        />
      </Card>
    </Grid>
  );
}

For complete implementation, see examples/tremor-dashboard.tsx.

Customizable Dashboard Implementation

Drag-and-Drop Grid Layout

tsx
import { Responsive, WidthProvider } from 'react-grid-layout';
import 'react-grid-layout/css/styles.css';

const ResponsiveGridLayout = WidthProvider(Responsive);

function CustomizableDashboard() {
  const [layouts, setLayouts] = useState(getStoredLayouts());

  return (
    <ResponsiveGridLayout
      layouts={layouts}
      breakpoints={{ lg: 1200, md: 996, sm: 768 }}
      cols={{ lg: 12, md: 10, sm: 6 }}
      rowHeight={60}
      onLayoutChange={(layout, layouts) => {
        setLayouts(layouts);
        localStorage.setItem('dashboardLayout', JSON.stringify(layouts));
      }}
      draggableHandle=".widget-header"
    >
      <div key="kpi1">
        <KPIWidget data={kpiData} />
      </div>
      <div key="chart1">
        <ChartWidget data={chartData} />
      </div>
      <div key="table1">
        <TableWidget data={tableData} />
      </div>
    </ResponsiveGridLayout>
  );
}

For full example with widget catalog, see examples/customizable-dashboard.tsx.

Real-Time Data Patterns

Server-Sent Events (Recommended)

Best for unidirectional updates from server to dashboard:

tsx
function useSSEUpdates(endpoint) {
  useEffect(() => {
    const eventSource = new EventSource(endpoint);

    eventSource.onmessage = (event) => {
      const update = JSON.parse(event.data);
      // Update specific widget or all widgets
      dispatch({ type: 'UPDATE_WIDGET', payload: update });
    };

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

WebSocket (For Bidirectional)

Use when dashboard needs to send commands back to server:

tsx
const ws = new WebSocket('ws://localhost:3000/dashboard');
ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  updateDashboard(data);
};
// Send filter changes to server
ws.send(JSON.stringify({ type: 'FILTER_CHANGE', filters }));

Smart Polling Fallback

For environments without WebSocket/SSE support:

tsx
function useSmartPolling(fetchData, interval = 30000) {
  const [isPaused, setIsPaused] = useState(false);

  useEffect(() => {
    if (isPaused || document.hidden) return;

    const timer = setInterval(fetchData, interval);
    return () => clearInterval(timer);
  }, [isPaused, interval]);

  // Pause when tab inactive
  useEffect(() => {
    const handleVisibilityChange = () => {
      setIsPaused(document.hidden);
    };
    document.addEventListener('visibilitychange', handleVisibilityChange);
    return () => document.removeEventListener('visibilitychange', handleVisibilityChange);
  }, []);
}

For detailed patterns including error handling and reconnection, see references/real-time-updates.md.

Performance Optimization

Lazy Loading Strategy

tsx
function DashboardGrid({ widgets }) {
  const [visibleWidgets, setVisibleWidgets] = useState(new Set());

  return widgets.map(widget => (
    <LazyLoad
      key={widget.id}
      height={widget.height}
      offset={100}
      once
      placeholder={<WidgetSkeleton />}
    >
      <Widget {...widget} />
    </LazyLoad>
  ));
}

Parallel Data Fetching

tsx
// Fetch all widget data simultaneously
const loadDashboard = async () => {
  const [kpis, charts, tables] = await Promise.all([
    fetchKPIs(),
    fetchChartData(),
    fetchTableData()
  ]);

  return { kpis, charts, tables };
};

Widget-Level Caching

tsx
function CachedWidget({ id, fetcher, ttl = 60000 }) {
  const cache = useRef({ data: null, timestamp: 0 });

  const getData = async () => {
    const now = Date.now();
    if (cache.current.data && now - cache.current.timestamp < ttl) {
      return cache.current.data;
    }

    const fresh = await fetcher();
    cache.current = { data: fresh, timestamp: now };
    return fresh;
  };

  // Use cached data while fetching fresh
  return <Widget data={cache.current.data} onRefresh={getData} />;
}

To analyze and optimize dashboard performance, run:

bash
python scripts/optimize-dashboard-performance.py --analyze dashboard-config.json

Cross-Skill Integration

Using Data Visualization Components

Reference the data-viz skill for chart widgets:

tsx
// Use charts from data-viz skill
import { createChart } from '../data-viz/chart-factory';

const revenueChart = createChart('area', {
  data: revenueData,
  xAxis: 'date',
  yAxis: 'revenue',
  theme: dashboardTheme
});

Integrating Data Tables

Reference the tables skill for data grids:

tsx
// Use advanced tables from tables skill
import { DataGrid } from '../tables/data-grid';

<DataGrid
  data={transactions}
  columns={columnDefs}
  pagination={true}
  sorting={true}
  filtering={true}
/>

Applying Design Tokens

Use the design-tokens skill for consistent theming:

tsx
// Dashboard-specific tokens from design-tokens skill
const dashboardTokens = {
  '--dashboard-bg': 'var(--color-bg-secondary)',
  '--widget-bg': 'var(--color-white)',
  '--widget-shadow': 'var(--shadow-lg)',
  '--kpi-value-size': 'var(--font-size-4xl)',
  '--kpi-trend-positive': 'var(--color-success)',
  '--kpi-trend-negative': 'var(--color-error)'
};

Filter Input Components

Optionally use the forms skill for filter controls:

tsx
// Advanced filter inputs from forms skill
import { DateRangePicker, MultiSelect } from '../forms/inputs';

<FilterPanel>
  <DateRangePicker onChange={handleDateChange} />
  <MultiSelect options={categories} onChange={handleCategoryFilter} />
</FilterPanel>

Library Selection Guide

Choose Tremor When:

  • Need to build dashboards quickly
  • Want pre-styled, professional components
  • Using Tailwind CSS in your project
  • Building standard analytics interfaces
  • Limited customization requirements

Choose react-grid-layout When:

  • Users need to customize layouts
  • Drag-and-drop is required
  • Different users need different views
  • Building a dashboard builder tool
  • Maximum flexibility is priority

Combine Both When:

  • Use Tremor for widget contents (KPIs, charts)
  • Use react-grid-layout for layout management
  • Get best of both worlds

Bundled Resources

Scripts (Token-Free Execution)

  • scripts/generate-dashboard-layout.py - Generate responsive grid configurations
  • scripts/calculate-kpi-metrics.py - Calculate trends, comparisons, sparklines
  • scripts/validate-widget-config.py - Validate widget and filter configurations
  • scripts/optimize-dashboard-performance.py - Analyze and optimize performance
  • scripts/export-dashboard.py - Export dashboards to various formats

Run scripts directly without loading into context:

bash
python scripts/calculate-kpi-metrics.py --data metrics.json --period monthly

References (Detailed Patterns)

  • references/kpi-card-patterns.md - KPI card design patterns and variations
  • references/layout-strategies.md - Grid systems and responsive approaches
  • references/real-time-updates.md - WebSocket, SSE, and polling implementations
  • references/filter-coordination.md - Cross-widget filter synchronization
  • references/performance-optimization.md - Advanced optimization techniques
  • references/library-guide.md - Detailed Tremor and react-grid-layout guides

Examples (Complete Implementations)

  • examples/sales-dashboard.tsx - Full sales analytics dashboard
  • examples/monitoring-dashboard.tsx - Real-time monitoring with alerts
  • examples/executive-dashboard.tsx - Polished executive reporting
  • examples/customizable-dashboard.tsx - Drag-and-drop with persistence
  • examples/tremor-dashboard.tsx - Quick Tremor implementation
  • examples/filter-context.tsx - Global filter coordination

Assets (Templates & Configurations)

  • assets/dashboard-templates.json - Pre-built dashboard layouts
  • assets/widget-library.json - Widget catalog and configurations
  • assets/grid-layouts.json - Responsive grid configurations
  • assets/kpi-formats.json - Number formatting rules
  • assets/theme-tokens.json - Dashboard-specific design tokens

Dashboard Creation Workflow

  1. Define Requirements: Fixed or customizable? Real-time or static?
  2. Choose Libraries: Tremor for quick, react-grid-layout for flexible
  3. Set Up Structure: Global state, filter context, layout system
  4. Build Widgets: KPI cards, charts (data-viz), tables (tables skill)
  5. Implement Data Flow: Fetching strategy, caching, updates
  6. Add Interactivity: Filters, drill-downs, exports
  7. Optimize Performance: Lazy loading, parallel fetching, caching
  8. Apply Theming: Use design-tokens for consistent styling
  9. Test Responsiveness: Desktop, tablet, mobile breakpoints
  10. Deploy & Monitor: Track performance, user engagement

For specific patterns and detailed implementations, explore the bundled resources referenced above.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Creating Dashboards AI skill do?

Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.

Why use Creating Dashboards on TypingMind?

Because you install it once and use it with any model. Creating Dashboards is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Creating Dashboards in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ancoleman/ai-design-components/tree/main/skills/creating-dashboards. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Creating Dashboards?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Creating Dashboards?

As many as you like. As long as a model supports skills, you can use Creating Dashboards with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Creating Dashboards AI skill free?

Yes. It is published on GitHub by ancoleman under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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