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Pixijs Core Concepts

Organization
pixijs
pixijs-core-concepts

Use this skill when understanding how PixiJS v8 renders frames: the systems-and-pipes renderer, the render loop, and how the library adapts to different environments. Covers WebGLRenderer/WebGPURenderer/CanvasRenderer selection, renderer.render() pipeline, environment detection, rendering into RenderTextures and RenderTargets (flipY, mipLevel, layer, bind/push/pop, copyDepthTexture), WebGL context loss and WebGPU device loss recovery, and pointers to per-topic deep dives. Triggers on: renderer, WebGL, WebGPU, Canvas, render loop, render pipeline, systems, environments, autoDetectRenderer, RenderTexture, RenderTarget, render to texture, flipY, mipLevel, layer, 3D texture, depth slice, renderTarget.bind, copyToTexture, copyDepthTexture, depth-only, WebGLLoader, WebGPULoader, CanvasLoader, RendererLoader, lazy-load renderer systems, code splitting, context lost, webglcontextlost, device lost, GPUDevice.lost.

Overview

Publisherpixijs
Repositorypixijs-skills
Skill namepixijs-core-concepts
Stars
357
Forks
19
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Pixijs Core Concepts 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.
    https://github.com/pixijs/pixijs-skills/tree/main/skills/pixijs-core-concepts
  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/pixijs/pixijs-skills.git /tmp/pixijs-skills
mkdir -p .claude/skills
cp -r /tmp/pixijs-skills/skills/pixijs-core-concepts .claude/skills/pixijs-core-concepts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pixijs Core Concepts 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 Pixijs Core Concepts 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 Pixijs Core Concepts 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.

Foundational model for how PixiJS v8 gets pixels on the screen: the renderer decides which GPU backend to use, the render loop drives per-frame work, and the environment layer adapts the library to browser, Web Worker, or SSR contexts. For the scene graph itself (Containers, transforms, destroy), see pixijs-scene-core-concepts.

Quick Start

ts
console.log(app.renderer.name); // 'webgl' | 'webgpu' | 'canvas'

app.ticker.add((ticker) => {
  sprite.rotation += 0.01 * ticker.deltaTime;
});

const tex = app.renderer.extract.texture({ target: app.stage });

app.renderer.render({ container: app.stage });

app.renderer is the WebGLRenderer, WebGPURenderer, or CanvasRenderer chosen by autoDetectRenderer. The TickerPlugin drives renderer.render() automatically; call it manually only with autoStart: false. Backend selection happens in Application.init({ preference }); see pixijs-application for setup.

Related skills: pixijs-application (Application construction and lifecycle), pixijs-ticker (per-frame logic, priorities, FPS capping), pixijs-environments (Web Worker, SSR, strict CSP), pixijs-custom-rendering (writing a RenderPipe), pixijs-scene-core-concepts (scene graph basics).

Topics

TopicReferenceWhen
Choosing a backendreferences/renderers.mdPreference forms, per-renderer options, systems and pipes
Per-frame executionreferences/render-loop.mdPriority order, time units, manual rendering

For deep dives into any single topic, open the corresponding reference file. Non-browser targets (DOMAdapter, WebWorkerAdapter, custom adapters, strict CSP) are covered in the pixijs-environments skill.

Decision guide

  • Setting up an Application? Start with pixijs-application. This skill explains what the renderer does under the hood.
  • Choosing between WebGL and WebGPU? Use ['webgpu', 'webgl'] as your preference array. WebGPU is fastest where available; WebGL is the reliable fallback. See references/renderers.md.
  • Running in a Web Worker? Set DOMAdapter.set(WebWorkerAdapter) before app.init. See the pixijs-environments skill for complete setup.
  • Need manual control over when rendering happens? Set autoStart: false and call app.renderer.render(app.stage) from your own loop. See references/render-loop.md.
  • Integrating with a physics library? Add your update at UPDATE_PRIORITY.HIGH so physics runs before the render at LOW. See references/render-loop.md.
  • Writing a custom renderable? Implement a RenderPipe. See pixijs-custom-rendering skill.
  • Rendering into a texture or a custom render target? Pass target to renderer.render(), or build a RenderTarget with explicit color and depth attachments. See references/renderers.md.
  • Running under strict CSP? Import 'pixi.js/unsafe-eval'. See the pixijs-environments skill.

Quick concepts

Renderer = systems + pipes

Each renderer is composed of Systems (lifecycle services: textures, buffers, state, filters, masks) and RenderPipes (per-renderable instruction builders: sprite, graphics, mesh, particle, text, tiling). Writing a custom renderable means implementing a RenderPipe and registering it via extensions. Backend-specific systems and pipes can be dynamic-imported through a renderer loader extension (WebGLLoader, WebGPULoader, CanvasLoader); see references/renderers.md.

The render loop

app.ticker.add(fn) registers a callback that runs every frame. The TickerPlugin registers app.render() at UPDATE_PRIORITY.LOW, so ticker callbacks at NORMAL or HIGH run before the draw. Disable the plugin with autoStart: false for manual control.

Environments

DOMAdapter abstracts every DOM call PixiJS makes (canvas creation, image loading, fetch, XML parsing). Swap with DOMAdapter.set(WebWorkerAdapter) for Workers or implement a custom Adapter for Node/SSR. Must be done before Application.init.

Common Mistakes

[HIGH] Accessing app.renderer before init() resolves

Wrong:

ts
const app = new Application();
app.init({ width: 800, height: 600 });
console.log(app.renderer.name); // undefined — init() is async

Correct:

ts
const app = new Application();
await app.init({ width: 800, height: 600 });
console.log(app.renderer.name); // 'webgl' | 'webgpu' | 'canvas'

Application.init() is async. app.renderer, app.canvas, and app.screen do not exist until after the promise resolves.

[HIGH] Setting DOMAdapter after Application.init

Wrong:

ts
const app = new Application();
await app.init({ width: 800, height: 600 });
DOMAdapter.set(WebWorkerAdapter); // too late — init already allocated resources

Correct:

ts
DOMAdapter.set(WebWorkerAdapter);
const app = new Application();
await app.init({ width: 800, height: 600 });

The adapter abstracts DOM calls the renderer makes during construction (canvas creation, image loading, fetch). Swap it before init() or the wrong adapter is baked into the renderer.

[MEDIUM] Treating preference as a guarantee

Wrong:

ts
await app.init({ preference: "webgpu" });
// assume WebGPU is active
useWebGPUOnlyFeature(app.renderer);

Correct:

ts
await app.init({ preference: "webgpu" });
if (app.renderer.name === "webgpu") {
  useWebGPUOnlyFeature(app.renderer);
}

preference is a hint, not a demand. If the browser lacks WebGPU support, PixiJS falls back to WebGL (or Canvas). Always branch on renderer.name for backend-specific code.

API Reference

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 Pixijs Core Concepts AI skill do?

Use this skill when understanding how PixiJS v8 renders frames: the systems-and-pipes renderer, the render loop, and how the library adapts to different environments. Covers WebGLRenderer/WebGPURenderer/CanvasRenderer selection, renderer.render() pipeline, environment detection, rendering into RenderTextures and RenderTargets (flipY, mipLevel, layer, bind/push/pop, copyDepthTexture), WebGL context loss and WebGPU device loss recovery, and pointers to per-topic deep dives. Triggers on: renderer, WebGL, WebGPU, Canvas, render loop, render pipeline, systems, environments, autoDetectRenderer, R...

Why use Pixijs Core Concepts on TypingMind?

Because you install it once and use it with any model. Pixijs Core Concepts 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 Pixijs Core Concepts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pixijs/pixijs-skills/tree/main/skills/pixijs-core-concepts. 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 Pixijs Core Concepts?

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 Pixijs Core Concepts?

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

Is the Pixijs Core Concepts AI skill free?

Yes. It is published on GitHub by pixijs 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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