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R3f Postprocessing

Community
EnzeD
r3f-postprocessing

Configure React Three Fiber postprocessing, bloom, selection effects, ambient occlusion, and depth of field. Use for composer pipelines and screen-space effects, rather than mesh material shaders.

Overview

PublisherEnzeD
Repositoryr3f-skills
Skill namer3f-postprocessing
Stars
116
Forks
7
Bundled files
Instructions only
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 EnzeD on GitHub. Read the source before you install it.

Installation

Install the R3f Postprocessing 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/EnzeD/r3f-skills.git /tmp/r3f-skills
mkdir -p .claude/skills
cp -r /tmp/r3f-skills/skills/r3f-postprocessing .claude/skills/r3f-postprocessing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable R3f Postprocessing 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 R3f Postprocessing 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 R3f Postprocessing 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.

React Three Fiber postprocessing

Check the actual renderer and package peer dependencies first. This example targets Fiber 9.7 / React 19 with @react-three/postprocessing 3.1 and postprocessing 6.39 on WebGL. Do not combine arbitrary newest package versions or silently upgrade a project.

Choose the pipeline

  • The React postprocessing composer here is for WebGL. Three.js WebGPURenderer uses node effects and RenderPipeline on r183+; verify that backend separately.
  • Let one owner render the final scene. EffectComposer uses a positive frame priority; an additional manual gl.render can overwrite or duplicate its output.
  • Ordinary Bloom can isolate bright surfaces via an HDR threshold. Use SelectiveBloom only when actual object selection is required; it adds work.

Actual selected-object bloom

Mount beneath Canvas. Click the left box to toggle selection. Both boxes are bright, but only the selected object contributes to this bloom pass.

tsx
import { useMemo, useRef, useState } from 'react'
import { EffectComposer, Select, Selection, SelectiveBloom, ToneMapping } from '@react-three/postprocessing'
import { ToneMappingMode } from 'postprocessing'
import type { DirectionalLight } from 'three'

export default function Example() {
  const light = useRef<DirectionalLight>(null)
  const lights = useMemo(() => [light], [])
  const [selected, setSelected] = useState(true)
  return (
    <Selection>
      <directionalLight ref={light} position={[0, 3, 5]} intensity={2} />
      <Select enabled={selected}>
        <mesh name="bloom-selected" position={[-1.2, 0, 0]} onClick={() => setSelected((value) => !value)}>
          <boxGeometry args={[0.7, 0.7, 0.7]} />
          <meshStandardMaterial color="black" emissive="white" emissiveIntensity={3} />
        </mesh>
      </Select>
      <mesh name="bloom-control" position={[1.2, 0, 0]}>
        <boxGeometry args={[0.7, 0.7, 0.7]} />
        <meshStandardMaterial color="black" emissive="white" emissiveIntensity={3} />
      </mesh>
      <EffectComposer multisampling={0}>
        <SelectiveBloom lights={lights} luminanceThreshold={0} intensity={2} mipmapBlur />
        <ToneMapping mode={ToneMappingMode.ACES_FILMIC} />
      </EffectComposer>
    </Selection>
  )
}

Color, selection, and refs

  • Selection/Select provide selection to effects that support it, such as Outline and SelectiveBloom. Wrapping ordinary Bloom in Selection does not make Bloom respect selected objects.
  • Supply SelectiveBloom's relevant lights and keep selection layers coordinated with other layer uses. Test an equally bright unselected object, not only a dark background.
  • Bloom operates on brightness before final tone mapping. Use emissive/HDR values and a meaningful threshold; do not flatten the whole scene to force a glow.
  • This composer disables renderer tone mapping; use a ToneMapping effect for the intended final appearance. Keep bloom/HDR effects before tone mapping, and avoid duplicate output conversion.
  • ref.current becoming non-null does not trigger a React render. Do not gate the initial mounting of an effect on a ref assignment alone; use supported refs or callback-ref state where an object is needed reactively.

Depth and effect-specific requirements

  • SSAO in this wrapper needs <EffectComposer enableNormalPass>; enable extra buffers only for effects that require them. Check the installed effect's source/types if docs and behavior disagree.
  • DepthOfField target is a world position (vector/tuple), not a mesh ref. focusDistance={0} is not a universal autofocus switch; use a supported target or Autofocus helper.
  • Some effect props accept Three.js Vector2/Vector3 instances rather than tuples. Type-check against the installed wrapper; don't transfer JSX coercion assumptions to arbitrary React components.
  • Alpha-blended surfaces, depth, selection, and multisampling interact. Test the actual transparent/transmissive scene rather than relying on opaque-box screenshots.

Performance and custom effects

  • Begin with few effects and modest DPR/resolution. Choose an anti-aliasing strategy deliberately; avoid blindly stacking MSAA, SMAA, and FXAA.
  • Effect count is not identical to pass count: compatible effects can be merged. Convolution/depth effects and auxiliary buffers can still be expensive; measure GPU cost.
  • Prefer supported wrapper components. For a custom postprocessing Effect, follow mainImage/mainUv, uniforms, input-buffer, and effect-attribute contracts; a UV-changing effect needs the appropriate convolution declaration.
  • Give custom Effect instances explicit cleanup ownership. Do not use dispose={null} without an owner, or dispose a shared effect from one consumer.
  • Check dynamic prop support after updates: construction-only settings may recreate an effect. Do not rebuild the composer each frame to animate a uniform.

Verify

Render the full pipeline, toggle effects/selection, resize, and unmount/remount under Strict Mode. Confirm selected-only behavior and final color output; TypeScript cannot prove either.

Sources

Frequently asked questions

What does the R3f Postprocessing AI skill do?

Configure React Three Fiber postprocessing, bloom, selection effects, ambient occlusion, and depth of field. Use for composer pipelines and screen-space effects, rather than mesh material shaders.

Why use R3f Postprocessing on TypingMind?

Because you install it once and use it with any model. R3f Postprocessing 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 R3f Postprocessing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EnzeD/r3f-skills/tree/main/skills/r3f-postprocessing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use R3f Postprocessing?

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 R3f Postprocessing?

As many as you like. As long as a model supports skills, you can use R3f Postprocessing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the R3f Postprocessing AI skill free?

It is published on GitHub by EnzeD. Check the repository for licensing terms. 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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