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Building Tables

Community
ancoleman
building-tables

Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).

Overview

Publisherancoleman
Repositoryai-design-components
Skill namebuilding-tables
Stars
523
Forks
73
Bundled files
31
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.

  • 31 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 Building Tables 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/building-tables .claude/skills/building-tables
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Building Tables 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 Building Tables 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 Building Tables 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.

Building Tables & Data Grids

Purpose

This skill enables systematic creation of tables and data grids from simple HTML tables to enterprise-scale virtualized grids handling millions of rows. It provides clear decision frameworks based on data volume and required features, ensuring optimal performance, accessibility, and responsive design across all implementations.

When to Use

Activate this skill when:

  • Creating tables, data grids, or spreadsheet-like interfaces
  • Displaying tabular or structured data
  • Implementing sorting, filtering, or pagination features
  • Handling large datasets or addressing performance concerns
  • Building inline editing or data entry interfaces
  • Requiring row selection or bulk operations
  • Implementing data export (CSV, Excel, PDF)
  • Ensuring table accessibility or responsive behavior

Quick Decision Framework

Select implementation tier based on data volume:

<100 rows        → Simple HTML table with progressive enhancement
100-1,000 rows   → Client-side features (sort, filter, paginate)
1,000-10,000     → Server-side operations with API pagination
10,000-100,000   → Virtual scrolling with windowing
>100,000 rows    → Enterprise grid with streaming and workers

For detailed selection criteria, reference references/selection-framework.md.

Core Implementation Patterns

Tier 1: Basic Tables (<100 rows)

For simple, read-only data display:

  • Use semantic HTML <table> structure
  • Add responsive behavior via CSS
  • Implement client-side sorting if needed
  • Reference references/basic-tables.md for patterns

Example: examples/simple-responsive-table.tsx

Tier 2: Interactive Tables (100-10K rows)

For feature-rich interactions:

  • Add filtering, pagination, and selection
  • Implement inline or modal editing
  • Use client-side operations up to 1K rows
  • Switch to server-side beyond 1K rows
  • Reference references/interactive-tables.md

Example: examples/sortable-filtered-table.tsx

Tier 3: Advanced Grids (10K+ rows)

For massive datasets:

  • Implement virtual scrolling
  • Use server-side aggregation
  • Add grouping and hierarchies
  • Consider enterprise solutions
  • Reference references/advanced-grids.md

Example: examples/virtual-scrolling-grid.tsx

Performance Optimization

Critical performance thresholds:

  • Client-side operations: <1,000 rows (instant, <50ms)
  • Server-side operations: 1,000-10,000 rows (<200ms API)
  • Virtual scrolling: 10,000+ rows (60fps, constant memory)
  • Streaming: 100,000+ rows (progressive rendering)

To benchmark performance:

bash
# Generate test data
python scripts/generate_mock_data.py --rows 10000

# Analyze rendering performance
node scripts/analyze_performance.js

For optimization strategies, reference references/performance-optimization.md.

Feature Implementation

Sorting

  • Single or multi-column sorting
  • Custom sort logic (numeric, date, natural)
  • Visual indicators and keyboard support
  • Reference references/sorting-filtering.md

Filtering & Search

  • Column-specific filters (text, range, select)
  • Global search across all columns
  • Advanced filter logic (AND/OR)
  • Reference references/sorting-filtering.md

Pagination

  • Client-side for small datasets
  • Server-side for large datasets
  • Infinite scroll alternative
  • Reference references/pagination-strategies.md

Selection & Bulk Actions

  • Single or multi-row selection
  • Range selection (Shift+click)
  • Bulk operations toolbar
  • Reference references/selection-patterns.md

Inline Editing

  • Cell-level or row-level editing
  • Validation and error handling
  • Optimistic updates
  • Reference references/editing-patterns.md

Export

  • CSV, Excel, PDF formats
  • Preserve formatting and encoding
  • Stream large exports
  • Run scripts/export_table_data.py

Accessibility Requirements

Essential WCAG compliance:

  • Semantic HTML with proper structure
  • ARIA grid pattern for interactive tables
  • Full keyboard navigation
  • Screen reader announcements

To validate accessibility:

bash
node scripts/validate_accessibility.js

For complete requirements, reference references/accessibility-patterns.md.

Responsive Design

Four proven strategies:

  1. Horizontal scroll - Simple, preserves structure
  2. Card stack - Transform rows to cards on mobile
  3. Priority columns - Hide less important columns
  4. Truncate & expand - Compact with details on demand

See examples/responsive-patterns.tsx for implementations. Reference references/responsive-strategies.md for details.

Library Recommendations

Primary: TanStack Table (Headless)

Best for custom designs and complete control:

  • TypeScript-first with excellent DX
  • Small bundle size (~15KB)
  • Framework agnostic
  • Virtual scrolling support
bash
npm install @tanstack/react-table

See examples/tanstack-basic.tsx for setup.

Enterprise: AG Grid

Best for feature-complete solutions:

  • Handles millions of rows
  • Built-in advanced features
  • Community (free) + Enterprise (paid)
  • Excel-like user experience
bash
npm install ag-grid-react

See examples/ag-grid-enterprise.tsx for setup.

For detailed comparison, reference references/library-comparison.md.

Design Token Integration

Tables use the design-tokens skill for consistent theming:

  • Color tokens for backgrounds, borders, and states
  • Spacing tokens for cell padding
  • Typography tokens for text styling
  • Shadow tokens for elevation

Supports light, dark, high-contrast, and custom themes. Reference the design-tokens skill for theme switching.

Working Examples

Start with the example matching the requirements:

simple-responsive-table.tsx    # Basic HTML table
sortable-filtered-table.tsx    # With sorting and filtering
paginated-server-table.tsx      # Server-side pagination
virtual-scrolling-grid.tsx      # High-performance for 100K+ rows
editable-data-grid.tsx         # Inline editing with validation
grouped-aggregated-table.tsx   # Hierarchical with aggregations

Testing Tools

Generate test data:

bash
python scripts/generate_mock_data.py --rows 100000 --columns 20

Benchmark performance:

bash
node scripts/analyze_performance.js --rows 10000

Validate accessibility:

bash
node scripts/validate_accessibility.js

Next Steps

  1. Determine the data volume and feature requirements
  2. Select the appropriate implementation tier
  3. Choose between TanStack Table (flexibility) or AG Grid (features)
  4. Start with the matching example file
  5. Implement core features progressively
  6. Test performance and accessibility
  7. Apply responsive strategy for mobile

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 Building Tables AI skill do?

Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).

Why use Building Tables on TypingMind?

Because you install it once and use it with any model. Building Tables 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 Building Tables in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ancoleman/ai-design-components/tree/main/skills/building-tables. 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 Building Tables?

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 Building Tables?

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

Is the Building Tables 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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