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Virtual Lists

Organization
PatternsDev
virtual-lists

Teaches virtual list (windowing) techniques for rendering large datasets. Use when rendering lists or tables with hundreds or thousands of items that cause scroll jank or slow initial render.

Overview

PublisherPatternsDev
Repositoryskills
Skill namevirtual-lists
Stars
250
Forks
27
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Virtual Lists 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/PatternsDev/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/javascript/virtual-lists .claude/skills/virtual-lists
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Virtual Lists 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 Virtual Lists 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 Virtual Lists 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.

List Virtualization

Table of Contents

List virtualization (also known as windowing) is the idea of rendering only visible rows of content in a dynamic list instead of the entire list. The rows rendered are only a small subset of the full list with what is visible (the window) moving as the user scrolls. This can improve rendering performance.

If you use React and need to display large lists of data efficiently, you may be familiar with react-virtualized. It's a windowing library by Brian Vaughn that renders only the items currently visible in a list (within a scrolling "viewport"). This means you don't need to pay the cost of thousands of rows of data being rendered at once.

When to Use

  • Use this when rendering large lists or grids (hundreds/thousands of items) that cause performance issues
  • This is helpful for reducing initial render time and improving scroll performance

When NOT to Use

  • For short lists (under ~100 items) where native rendering is fast enough without virtualization
  • When accessibility requirements demand all list items be in the DOM for screen readers
  • When the list items have unpredictable, content-dependent heights that make virtualization measurements unreliable

Instructions

  • Use react-window (or react-virtualized) to render only visible items in a scrollable container
  • Choose FixedSizeList for items of equal height or VariableSizeList for items of different heights
  • Use react-window-infinite-loader for incrementally loading data as the user scrolls
  • Consider CSS content-visibility: auto for simpler cases where full virtualization isn't needed

Details

How does list virtualization work?

"Virtualizing" a list of items involves maintaining a window and moving that window around your list. Windowing in react-virtualized works by:

  • Having a small container DOM element (e.g <ul>) with relative positioning (window)
  • Having a big DOM element for scrolling
  • Absolutely positioning children inside the container, setting their styles for top, left, width and height.

Rather than rendering 1000s of elements from a list at once (which can cause slower initial rendering or impact scroll performance), virtualization focuses on rendering just items visible to the user.

This can help keep list rendering fast on mid to low-end devices. You can fetch/display more items as the user scrolls, unloading previous entries and replacing them with new ones.

A smaller alternative to react-virtualized

react-window is a rewrite of react-virtualized by the same author aiming to be smaller, faster and more tree-shakeable.

In a tree-shakeable library, size is a function of which API surfaces you choose to use. You can see ~20-30KB (gzipped) savings using it in place of react-virtualized.

The APIs for both packages are similar and where they differ, react-window tends to be simpler. react-window's components include:

List

Lists render a windowed list (row) of elements meaning that only the visible rows are displayed to users (e.g FixedSizeList, VariableSizeList). Lists use a Grid (internally) to render rows, relaying props to that inner Grid.

Rendering a list of data using React

Here's an example of rendering a list of simple data (itemsArray) using React:

js
import React from "react";
import ReactDOM from "react-dom";

const itemsArray = [
  { name: "Drake" },
  { name: "Halsey" },
  { name: "Camillo Cabello" },
  { name: "Travis Scott" },
  { name: "Bazzi" },
  { name: "Flume" },
  { name: "Nicki Minaj" },
  { name: "Kodak Black" },
  { name: "Tyga" },
  { name: "Buno Mars" },
  { name: "Lil Wayne" }, ...
]; // our data

const Row = ({ index, style }) => (
  <div className={index % 2 ? "ListItemOdd" : "ListItemEven"} style={style}>
    {itemsArray[index].name}
  </div>
);

const Example = () => (
  <div
    style={{
      height: 150,
      width: 300
    }}
    class="List"
  >
    {itemsArray.map((item, index) => Row({ index }))}
  </div>
);

ReactDOM.render(<Example />, document.getElementById("root"));

Rendering a list using react-window

...and here's the same example using react-window's FixedSizeList, which takes a few props (width, height, itemCount, itemSize) and a row rendering function passed as a child:

js
import React from "react";
import ReactDOM from "react-dom";
import { FixedSizeList as List } from "react-window";

const itemsArray = [...]; // our data

const Row = ({ index, style }) => (
  <div className={index % 2 ? "ListItemOdd" : "ListItemEven"} style={style}>
    {itemsArray[index].name}
  </div>
);

const Example = () => (
  <List
    className="List"
    height={150}
    itemCount={itemsArray.length}
    itemSize={35}
    width={300}
  >
    {Row}
  </List>
);

ReactDOM.render(<Example />, document.getElementById("root"));

You can try out FixedSizeList on CodeSandbox.

Grid

Grid renders tabular data with virtualization along the vertical and horizontal axes (e.g FixedSizeGrid, VariableSizeGrid). It only renders the Grid cells needed to fill itself based on current horizontal/vertical scroll positions.

js
import React from 'react';
import ReactDOM from 'react-dom';
import { FixedSizeGrid as Grid } from 'react-window';

const itemsArray = [
  [{},{},{},...],
  [{},{},{},...],
  [{},{},{},...],
  [{},{},{},...],
];

const Cell = ({ columnIndex, rowIndex, style }) => (
  <div
    className={
      columnIndex % 2
        ? rowIndex % 2 === 0
          ? 'GridItemOdd'
          : 'GridItemEven'
        : rowIndex % 2
          ? 'GridItemOdd'
          : 'GridItemEven'
    }
    style={style}
  >
    {itemsArray[rowIndex][columnIndex].name}
  </div>
);

const Example = () => (
  <Grid
    className="Grid"
    columnCount={5}
    columnWidth={100}
    height={150}
    rowCount={5}
    rowHeight={35}
    width={300}
  >
    {Cell}
  </Grid>
);

ReactDOM.render(<Example />, document.getElementById('root'));

You can also try out FixedSizeGrid on CodeSandbox.

More in-depth react-window examples

Scott Taylor implemented an open-source Pitchfork music reviews scraper (src) using react-window and FixedSizeGrid.

Pitchfork scraper uses react-window-infinite-loader (demo) which helps break large data sets down into chunks that can be loaded as they are scrolled into view.

Here's a snippet of how react-window-infinite-loader is incorporated in this app:

js
import React, { Component } from 'react';
import { FixedSizeGrid as Grid } from 'react-window';
import InfiniteLoader from 'react-window-infinite-loader';

// ...

render() {
  return (
    <InfiniteLoader
      isItemLoaded={this.isItemLoaded}
      loadMoreItems={this.loadMoreItems}
      itemCount={this.state.count + 1}
    >
      {({ onItemsRendered, ref }) => (
        <Grid
          onItemsRendered={this.onItemsRendered(onItemsRendered)}
          columnCount={COLUMN_SIZE}
          columnWidth={180}
          height={800}
          rowCount={Math.max(this.state.count / COLUMN_SIZE)}
          rowHeight={220}
          width={1024}
          ref={ref}
        >
          {this.renderCell}
        </Grid>
      )}
    </InfiniteLoader>
  );
}

You might find the commit porting the app over from react-virtualized useful.

An implementation using FixedSizeList is also available:

js
return (
  <InfiniteLoader
    isItemLoaded={this.isItemLoaded}
    loadMoreItems={this.loadMoreItems}
    itemCount={this.state.count}
  >
    {({ onItemsRendered, ref }) => (
      <section>
        <FixedSizeList
          itemCount={this.state.count}
          itemSize={ROW_HEIGHT}
          onItemsRendered={onItemsRendered}
          height={this.state.height}
          width={this.state.width}
          ref={ref}
        >
          {this.renderCell}
        </FixedSizeList>
      </section>
    )}
  </InfiniteLoader>
);

For even more complex needs, a The Movie Database demo app used react-virtualized and Infinite Loader under the hood. Porting it over to react-window and react-window-infinite-loader didn't take long, but we did discover a few components were not yet supported. Regardless, the final functionality is pretty close.

The missing components were WindowScroller and AutoSizer, which we'll look at next.

js
// ...
return (
  <section>
    <AutoSizer disableHeight>
      {({width}) => {
        const {movies, hasMore} = this.props;
        const rowCount = getRowsAmount(width, movies.length, hasMore);
        // ...
        return (
          <InfiniteLoader
            ref={this.infiniteLoaderRef}
            // ...
            {({onRowsRendered, registerChild}) => (
              <WindowScroller>
                {({height, scrollTop}) => (

What's missing from react-window?

react-window does not yet have the complete API surface of react-virtualized, so do check the comparison docs if considering it. What's missing?

  • WindowScroller - This is a react-virtualized component that enables Lists to be scrolled based on the window's scroll positions. There are currently no plans to implement this for react-window so you'll need to solve this in userland.
  • AutoSizer - HOC that grows to fit all of the available space, automatically adjusting the width and height of a single child. Brian implemented this as a standalone package. Follow this issue for the latest.
  • CellMeasurer - HOC automatically measuring a cell's content by rendering it in a way that is not visible to the user. Follow here for discussion on support.

That said, we found react-window sufficient for most of our needs with what it includes out of the box.

Improvements in the web platform

Some modern browsers now support CSS content-visibility. content-visibility:auto allows you to skip rendering & painting offscreen content until needed. If you have a long HTML document with costly rendering, consider trying the property out.

For rendering lists of dynamic content, I still recommend using a library like react-window. It would be hard to have a content-visibility:hidden version of such a library that beats a version aggressively using display:none or removing DOM nodes when offscreen like many list virtualization libraries may do today.

Source

References

Frequently asked questions

What does the Virtual Lists AI skill do?

Teaches virtual list (windowing) techniques for rendering large datasets. Use when rendering lists or tables with hundreds or thousands of items that cause scroll jank or slow initial render.

Why use Virtual Lists on TypingMind?

Because you install it once and use it with any model. Virtual Lists 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 Virtual Lists in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PatternsDev/skills/tree/main/javascript/virtual-lists. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Virtual Lists?

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 Virtual Lists?

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

Is the Virtual Lists AI skill free?

Yes. It is published on GitHub by PatternsDev 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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