r3f-performance

r3f-performance is a skill for Claude Code from bullish0x/GameStudio. It costs 27 tokens per session (3,439 once invoked), scanned A, original, MIT.

A guide to improving React Three Fiber performance in 3D applications. It covers techniques such as memoization, instancing, level of detail, deferred loading, and adaptive quality.

In plain words
What is it for?
Use it to profile and optimize 3D scenes, render many objects more efficiently, load assets progressively, and adjust quality for device capability.
Why use it?
It helps reduce slow frames, stuttering, and excessive work when scenes contain many objects or run on less capable mobile devices.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to profile and optimize 3D scenes, render many objects more efficiently, load assets progressively, and adjust quality for device capability.

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Install with agentmods
npx agentmods add skills/bullish0x/gamestudio/r3f-performance
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add bullish0x/GameStudio --skill r3f-performance
Clone the repo
git clone --depth 1 https://github.com/bullish0x/GameStudio

Made for: Claude Code.

Wrote this? Show the measurements

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agentmods badge for r3f-performance

README.md
[![agentmods](https://agentmods.dev/badge/skills/bullish0x/gamestudio/r3f-performance/github.svg)](https://agentmods.dev/skills/bullish0x/gamestudio/r3f-performance)
Your own site
<a href="https://agentmods.dev/skills/bullish0x/gamestudio/r3f-performance"><img src="https://agentmods.dev/badge/skills/bullish0x/gamestudio/r3f-performance/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for r3f-performance

Your own site · 80×15
<a href="https://agentmods.dev/skills/bullish0x/gamestudio/r3f-performance"><img src="https://agentmods.dev/badge/skills/bullish0x/gamestudio/r3f-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,439 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00027 $0.03439
Opus 5 $0.00014 $0.01720
Sonnet 5 $0.00005 $0.00688
Haiku 4.5 $0.00003 $0.00344

Measured 8d ago against content hash 8a76c8be29bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

r3f-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/r3f-performance/SKILL.md · 520 lines

How it starts

The opening of the file, as written. The whole thing — 520 lines — stays where its author put it; the contents beside it link to each section on GitHub.

React Three Fiber Performance

When to Use

Use this skill when:

  • Optimizing R3F applications for mobile
  • Dealing with performance bottlenecks
  • Rendering many objects (100+)
  • Implementing adaptive quality
  • Reducing frame drops and stuttering

Core Principles

  1. Measure First: Profile before optimizing
  2. React Optimization: Prevent unnecessary re-renders
  3. Three.js Optimization: Reduce draw calls, vertices
  4. Adaptive Quality: Scale down on low-end devices
  5. Lazy Loading: Load assets progressively
  6. Frame Budget: Stay under 16ms (60fps) or 33ms (30fps)

Implementation

1. React Memoization

// components/OptimizedComponents.tsx
import { memo, useMemo, useCallback } from 'react';
import * as THREE from 'three';

// Memoize expensive components
export const MemoizedBox = memo(function Box({
  position,
  color,
}: {
  position: [number, number, number];
  color: string;
}) {
  // Memoize geometry (created once)
  const geometry = useMemo(() => new THREE.BoxGeometry(1, 1, 1), []);

  // Memoize material (recreated only when color changes)
  const material = useMemo(
    () => new THREE.MeshStandardMaterial({ color }),
    [color]
  );

  return <mesh position={position} geometry={geometry} material={material} />;
});

// Custom comparison function
export const SmartMemoBox = memo(
  function Box(props: { position: [number, number, number]; color: string }) {
    const geometry = useMemo(() => new THREE.BoxGeometry(1, 1, 1), []);
    const material = useMemo(
      () => new THREE.MeshStandardMaterial({ color: props.color }),
      [props.color]
    );

    return <mesh position={props.position} geometry={geometry} material={material} />;
  },
  (prev, next) => {
    // Only re-render if color changes (ignore position changes)
    return prev.color === next.color;
  }
);

2. Instancing for Many Objects

// components/InstancedObjects.tsx
import { useRef, useMemo } from 'react';
import { useFrame } from '@react-three/fiber';
import { InstancedMesh, Object3D, Matrix4 } from 'three';

interface InstancedObjectsProps {
  count: number;
  spread: number;
}

export function InstancedObjects({ count, spread }: InstancedObjectsProps) {
  const meshRef = useRef<InstancedMesh>(null);

  // Initialize instances
  const { positions, rotations, matrices } = useMemo(() => {
    const positions = new Float32Array(count * 3);
    const rotations = new Float32Array(count * 3);
    const matrices: Matrix4[] = [];

    const dummy = new Object3D();

    for (let i = 0; i < count; i++) {
      // Random positions
      positions[i * 3] = (Math.random() - 0.5) * spread;
      positions[i * 3 + 1] = (Math.random() - 0.5) * spread;
      positions[i * 3 + 2] = (Math.random() - 0.5) * spread;

      // Random rotations
      rotations[i * 3] = Math.random() * Math.PI;
      rotations[i * 3 + 1] = Math.random() * Math.PI;
      rotations[i * 3 + 2] = Math.random() * Math.PI;

      // Set matrix
      dummy.position.set(
        positions[i * 3],
        positions[i * 3 + 1],
        positions[i * 3 + 2]
      );
      dummy.rotation.set(
        rotations[i * 3],
        rotations[i * 3 + 1],
        rotations[i * 3 + 2]
      );
      dummy.updateMatrix();

      matrices.push(dummy.matrix.clone());
    }

    return { positions, rotations, matrices };
  }, [count, spread]);

  // Apply matrices on mount
  useMemo(() => {
    if (!meshRef.current) return;

    matrices.forEach((matrix, i) => {
      meshRef.current!.setMatrixAt(i, matrix);
    });
    meshRef.current.instanceMatrix.needsUpdate = true;
  }, [matrices]);

  // Animate (optional - remove if static)
  useFrame((state, delta) => {
    if (!meshRef.current) return;

    const dummy = new Object3D();

    for (let i = 0; i < count; i++) {
      meshRef.current.getMatrixAt(i, dummy.matrix);
      dummy.matrix.decompose(dummy.position, dummy.quaternion, dummy.scale);

      // Rotate
      rotations[i * 3 + 1] += delta * 0.5;
      dummy.rotation.y = rotations[i * 3 + 1];

      dummy.updateMatrix();
      meshRef.current.setMatrixAt(i, dummy.matrix);
    }

    meshRef.current.instanceMatrix.needsUpdate = true;
  });

  return (
    <instancedMesh ref={meshRef} args={[undefined, undefined, count]}>
      <boxGeometry />
      <meshStandardMaterial />
    </instancedMesh>
  );
}

Read the full file on GitHub · 520 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 520 lines · 27 tokens per session scan A 8a76c8be29bc

Subscribe to this mod's changes

r3f-performance is a skill published in the GitHub repository bullish0x/GameStudio (12 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 3,439 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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