About threejs-perf
threejs-perf is a performance optimization skill for Three.js games, with instancing patterns for static objects and moving entities plus benchmark data. It is intended for projects where repeated objects or large entity counts increase draw calls and scene-update costs.
Key features
- 01Use InstancedMesh for repeated static objects
- 02Batch transform updates for moving entities
- 03Provide benchmark and optimized examples
- 04Explain when instancing is appropriate
Use cases
- 01Optimize scenes with many repeated props
- 02Reduce draw calls for moving entities
- 03Investigate scene-graph traversal bottlenecks
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ playableintelligence/ game-creator --skill threejs-perfAfter installing, type/threejs-perfin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
instancing-moving.mdinstancing-static.mdtemplates/moving-entities-baseline.tsmoving-entities-instanced.tsstatic-world-baseline.tsstatic-world-instanced.tsDetails
From the project READMEThree.js Performance Optimization
Performance patterns for Three.js games, backed by measured before/after numbers on Three.js r183 (headless Chromium via Playwright, Apple M1 Pro, software WebGL).
Reference Files
instancing-static.md— InstancedMesh for large static repeated objects (19,600 → 1 draw call)instancing-moving.md— Flat state buffer + batched InstancedMesh writes for moving entities (8,000 entities)templates/— Baseline vs optimized reference implementations for each pattern
When to Use This Skill
- Scene has 100+ repeated objects sharing geometry/material
- Draw calls exceed 500 and frame time is unstable
- Thousands of moving entities need per-frame transform updates
- Profile shows scene-graph traversal as a bottleneck
When NOT to Use
- Object count is low (<50 unique meshes) — simpler code wins
- Every object needs unique materials/shaders that defeat batching
- Geometry differs enough that instancing provides no batching benefit
Pattern 1: Instancing Large Static Object Sets
Problem: Forests, debris, decorations as individual Meshes = unnecessary draw calls.
Solution: One InstancedMesh per shared geometry+material combo.
Evidence: ~19,365 → 2 draw calls. Render CPU p95: 28.5ms → 0.5ms (~57× faster). Build: 39.4ms → 3.9ms. See instancing-static.md.
// Anti-pattern: one Mesh per prop
for (let i = 0; i < 19600; i++) {
const mesh = new THREE.Mesh(geometry, material);
mesh.position.set(x, 0, z);
scene.add(mesh); // 19,600 draw calls
}
// Correct: one InstancedMesh
const im = new THREE.InstancedMesh(geometry, material, 19600);
const mat = new THREE.Matrix4();
for (let i = 0; i < 19600; i++) {
mat.makeTranslation(x, 0, z);
im.setMatrixAt(i, mat);
}
im.instanceMatrix.needsUpdate = true;
scene.add(im); // 1 draw call
Pattern 2: Moving Entity Update Loops
Problem: Thousands of moving actors as individual Meshes = scene-graph churn + transform propagation.
Solution: Flat entity state buffer + batched InstancedMesh.setMatrixAt() writes.
Evidence: 8,000 → 1 draw calls. Render CPU p95: 9.9ms → 0.5ms (~20× faster). Update loop p95: 1.4ms → 0.3ms. See instancing-moving.md.
// Anti-pattern: per-entity Mesh position writes
meshes.forEach((mesh, i) => {
mesh.position.x = computeX(i, tick);
mesh.position.y = computeY(i, tick);
});
// Correct: batched instance matrix writes
const mat = new THREE.Matrix4();
for (let i = 0; i < count; i++) {
mat.makeTranslation(computeX(i, tick), computeY(i, tick), computeZ(i, tick));
instancedMesh.setMatrixAt(i, mat);
}
instancedMesh.instanceMatrix.needsUpdate = true;
Decision Tree
Is the object repeated 50+ times with same geometry+material?
├── YES → Is it static (no per-frame movement)?
│ ├── YES → Pattern 1: Static InstancedMesh (instancing-static.md)
│ └── NO → Pattern 2: Moving InstancedMesh with batched writes (instancing-moving.md)
└── NO → Standard Mesh is fine. Focus on material/geometry reuse.
Measured Results
Headless Chromium 147 via Playwright, Three.js r183, Apple M1 Pro, 30 warmup + 180 sample frames, median of 3 runs.
| Scenario | Metric | Baseline | Optimized | Improvement |
|---|---|---|---|---|
| Static World (19.6k cubes) | Draw calls | ~19,365 | 2 | ~9,682× |
| Static World (19.6k cubes) | Render CPU p95 | 28.5ms | 0.5ms | ~57× |
| Static World (19.6k cubes) | Build | 39.4ms | 3.9ms | ~10× |
| Moving Entities (8k wave-field) | Draw calls | 8,000 | 1 | 8,000× |
| Moving Entities (8k wave-field) | Render CPU p95 | 9.9ms | 0.5ms | ~20× |
| Moving Entities (8k wave-field) | Update loop p95 | 1.4ms | 0.3ms | ~4.7× |
Methodology notes
- CPU-side metrics are the trustworthy signal. Draw calls, render CPU p95, update loop, and build time reliably show the 1–2 order-of-magnitude win.
- FPS and frame-time p95 are unreliable in headless Chromium. Playwright's bundled Chromium uses SwiftShader (software WebGL), which bottlenecks on fragment shading of ~90 MB of visible geometry regardless of draw-call count. On real hardware WebGL, the FPS gap would be substantially larger — baseline would drop to single-digit FPS under real fill, and optimized would hit vsync cleanly.
- A benchmark passes if draw calls decreased and render CPU p95 did not regress.