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Threejs Performance

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staruhub
threejs-performance

Three.js 性能优化指南。当 Three.js/React Three Fiber 项目出现掉帧、内存增长、加载缓慢、绘制调用过多,或需要迁移 WebGPU、实现大规模粒子/实例化渲染、配置资源压缩管线时使用。触发关键词:"Three.js 优化"、"WebGPU"、"draw calls"、"内存泄漏"、"TSL"、"InstancedMesh"、"R3F 优化"等。不用于:通用 React 性能问题(用 vercel-react-best-practices)、WebGL/Three.js 入门教学、3D 美术资产的制作本身。

Overview

Publisherstaruhub
RepositoryClaudeSkills
Skill namethreejs-performance
Stars
728
Forks
131
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Threejs Performance 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/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-threejs-performance
  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/staruhub/ClaudeSkills.git /tmp/ClaudeSkills
mkdir -p .claude/skills
cp -r /tmp/ClaudeSkills/skills/Geek-skills-threejs-performance .claude/skills/threejs-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threejs Performance 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 Threejs Performance 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 Threejs Performance 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.

Three.js 性能优化指南

黄金法则

绘制调用 < 100 次/帧。 三角形数量不如绘制调用数量重要;超过 500 次绘制调用,强大 GPU 也会吃力。用 renderer.info.render.calls 监控(完整监控代码见 references/examples.md)。

诊断决策树

症状先查深入
掉帧、卡顿renderer.info.render.calls 是否 >100实例化/合批(下文),后处理链
内存持续增长renderer.info.memory 的 geometries/textures 计数是否只增不减dispose 铁律(下文),references/examples.md 完整清理代码
加载慢、显存爆模型是否未压缩references/assets.md 压缩管线
粒子/物理瓶颈CPU 粒子是否 >5 万WebGPU 计算着色器,references/webgpu.md
R3F 项目莫名重渲染useFrame 里是否 setStateR3F 三规则(下文)

各领域规则速查

WebGPU:何时迁移

绘制调用密集掉帧 / 需要计算着色器做物理粒子(CPU 约 5 万上限,GPU 可达数百万)/ 复杂后处理链卡顿。 WebGPURenderer 必须 await renderer.init()。TSL 写一次自动编译 WGSL/GLSL。 浏览器支持下限(记录时点数据,现查 caniuse 为准):Chrome/Edge 113+,Firefox 141+,Safari 26+。 初始化回退、TSL 完整指南、计算着色器示例:references/webgpu.md。

绘制调用优化

  • 大量相同几何体(树、石头)→ InstancedMesh:1000 棵树 = 1 次绘制
  • 多个不同几何体共享材质 → BatchedMesh
  • 静态小物件 → mergeGeometries 合并
  • 材质共享复用,永远不要在循环里 new Material

资源压缩(收益数字)

  • Draco 几何体压缩:体积减 90-95%
  • KTX2 纹理:GPU 内存约降 10 倍
  • 一条命令:gltf-transform optimize model.glb out.glb --texture-compress ktx2 --compress draco
  • 解码器路径配置与 Meshopt/Draco 选型:references/assets.md

内存铁律

Three.js 不会自动回收 GPU 资源。 移除对象必须:geometry.dispose() + 遍历 material 的所有 texture 属性逐个 dispose + material.dispose();GLTF 的 ImageBitmap 还要 texture.source.data.close()。频繁增删对象用对象池。完整代码:references/examples.md。

着色器三规则

① 移动端 precision mediump float(约快 2 倍)② 用 mix/step 替代 if 分支(分支破坏 GPU 并行)③ 数据打包进 vec4,一次纹理取 4 个值。

光照阴影预算

活动光源 ≤3;PointLight 阴影 = 每光源 6 次阴影贴图渲染;贴图尺寸移动端 512-1024、桌面 1024-2048;静态场景 shadowMap.autoUpdate = false 手动触发 + 烘焙光照。

React Three Fiber 三规则

① 动画走 useFrame 直改 ref,永远不在 useFrame 里 setState ② 静态场景用 frameloop="demand" + invalidate() 按需渲染 ③ 显隐切 visible 属性,不要条件挂载 {show && <Model/>}(重挂载重建资源)。完整优化模板:references/examples.md。

后处理选型

WebGL → pmndrs/postprocessing(多效果合并 EffectPass);WebGPU → 原生 TSL 后处理管线。两者完整设置:references/examples.md。

验收标准(优化任务完成前自查)

  • renderer.info.render.calls < 100(超出需给出场景理由)
  • renderer.info.memory 计数在增删对象后回落,无单调增长
  • 目标设备实测帧率达标(移动端也要测,不只桌面)
  • 发布资源经过 Draco/KTX2 压缩管线
  • 每条优化建议都对应用户场景的实测症状,不是清单式全量套用

不做什么

  • React 组件层的通用性能问题(memo/useMemo/bundle)→ vercel-react-best-practices
  • Three.js 基础教学、场景搭建入门
  • 模型/贴图美术制作本身(只管加载与渲染性能)
  • 未量测先优化:没有 renderer.info 或帧率数据时,先装监控再动手

已知陷阱

陷阱具体表现应对
dispose 不彻底只 dispose 了 geometry,material 上挂的 texture 全泄漏遍历 material 属性逐个 dispose;GLTF ImageBitmap 还需 close()
useFrame 里 setState每帧触发 React 重渲染,帧率断崖直改 ref;状态只在交互事件里更新
条件挂载切换模型{show && <Model/>} 每次重建几何体和纹理切 visible 属性
demand 模式忘 invalidate相机动了画面不动,被当成"卡死"交互回调里调用 invalidate()
循环里 new Material1000 个网格 1000 个材质,合批全部失效材质提到循环外共享
PointLight 随手加阴影一个点光 6 次阴影渲染,移动端直接跪优先 SpotLight/DirectionalLight 阴影,点光阴影只留一个

调试工具

stats-gl(FPS/CPU/GPU)· lil-gui(实时调参)· Spector.js(WebGL 帧捕获)· three-mesh-bvh(8 万+ 面 @60fps 射线检测)· r3f-perf(R3F 监控)

参考文档(按需加载)

文件何时读
references/webgpu.md迁移 WebGPU / 写 TSL / 计算着色器时
references/assets.md配置压缩管线、LOD、渐进加载时
references/examples.md需要完整可粘贴代码时(监控/内存清理/对象池/粒子/R3F 模板/后处理/上下文丢失恢复)

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 Threejs Performance AI skill do?

Three.js 性能优化指南。当 Three.js/React Three Fiber 项目出现掉帧、内存增长、加载缓慢、绘制调用过多,或需要迁移 WebGPU、实现大规模粒子/实例化渲染、配置资源压缩管线时使用。触发关键词:"Three.js 优化"、"WebGPU"、"draw calls"、"内存泄漏"、"TSL"、"InstancedMesh"、"R3F 优化"等。不用于:通用 React 性能问题(用 vercel-react-best-practices)、WebGL/Three.js 入门教学、3D 美术资产的制作本身。

Why use Threejs Performance on TypingMind?

Because you install it once and use it with any model. Threejs Performance 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 Threejs Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/staruhub/ClaudeSkills/tree/main/skills/Geek-skills-threejs-performance. 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 Threejs Performance?

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 Threejs Performance?

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

Is the Threejs Performance AI skill free?

Yes. It is published on GitHub by staruhub 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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