> Find and fix game performance problems methodically — measure with the engine profiler first, object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine- neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".
npx skills add https://github.com/gamedev-skills/awesome-gamedev-agent-skills --skill performance-optimization
Performance work is a measurement discipline, not a bag of tricks. The method is always the
same: profile → find the one bottleneck → fix that → measure again. This skill teaches that
loop and the highest-leverage fixes (pooling, batching, allocation control, asset budgets), and
points you at each engine's profiler. It pairs with physics-tuning for simulation cost.
(60 FPS desktop, 30/60 mobile) and currently doesn't.
or GPU is the bottleneck before changing any code.
allocations and GC spikes, and setting asset budgets.
When *not* to use: for physics jitter/tunneling/timestep specifically, use physics-tuning.
For the engine's concrete profiler UI and rendering settings, use that
engine skill (godot-export covers some build settings; engine cores cover the rest). This skill
is the cross-engine method and the shared fixes.
Most performance "fixes" applied without profiling target the wrong thing and add complexity for
no gain. Do not optimize code you have not measured. Open the profiler, find the single
biggest cost in a representative scene on representative hardware, and fix that. Re-measure to
confirm the fix helped before moving on. Profile a release/optimized build where it matters —
editor and debug builds lie (editor overhead, no compiler optimization).
repeatable worst-case scene. "Sometimes slow" is unfixable; a reproducible spike is fixable.
time, and the split between CPU (game logic, physics, scripts) and GPU (rendering).
resolution. If CPU time dominates, attack scripts/physics/allocations. Fixing the wrong side
does nothing.
partition, run less often) over micro-optimizing a hot line. Apply the matching shared fix
(pooling, batching, allocation removal).
data, not intuition.
(texture sizes, triangle counts, draw-call ceilings); add a perf check to verification.
— never "should be faster". If you could only measure in-editor, say so.
target FPS → frame budget: 60 FPS = 16.67 ms | 30 FPS = 33.3 ms | 120 FPS = 8.33 ms
The WHOLE frame (CPU sim + render submit + GPU) must fit the budget; the GPU runs in parallel,
so the slower of CPU-frame and GPU-frame sets your FPS. Allocate sub-budgets, e.g. @60 FPS:
gameplay/scripts ~5 ms · physics ~3 ms · rendering(CPU submit) ~4 ms · UI/other ~2 ms · slack.
If one subsystem blows its slice, that's your target — not whatever you assumed.
Godot 4.x : Debugger ▸ Profiler (script/physics time) and Monitors tab (FPS, draw calls, memory).
In code: Performance.get_monitor(Performance.TIME_PROCESS) and
Performance.get_monitor(Performance.RENDER_TOTAL_DRAW_CALLS_IN_FRAME).
Unity 6 : Profiler window (CPU/GPU/Memory/Rendering modules) + Frame Debugger for draw calls.
In code: a ProfilerRecorder tracking "CPU Main Thread Frame Time" for a HUD/log.
Unreal 5 : `stat unit` (Frame/Game/Draw/GPU ms), `stat fps`, `stat scenerendering` (draw calls);
Unreal Insights for deep traces.
# Read the split: is the Draw/GPU line the biggest, or the Game/CPU line? That decides the fix.
# Bullets, particles, enemies, damage numbers: reuse a fixed set instead of instantiate()/free()
# every frame — that thrashes memory and (in C#) feeds the GC.
var _pool: Array[Node] = []
func acquire() -> Node:
var n: Node = _pool.pop_back() if not _pool.is_empty() else bullet_scene.instantiate()
n.set_process(true); n.visible = true
return n
func release(n: Node) -> void:
n.set_process(false); n.visible = false # disable + hide; DON'T free
_pool.append(n) # back to the pool for reuse
# RIGHT: pre-warm the pool at load; reuse. WRONG: instantiate()/queue_free() per shot.
Each unique material/texture/state change is roughly a draw call; thousands of them stall the GPU.
- Atlas textures and share materials so sprites/meshes batch into one call.
- Identical meshes → GPU instancing (Unity), MultiMesh / MultiMeshInstance (Godot), Instanced
Static Mesh (Unreal).
- Static geometry → static batching / baking; mark non-moving objects static.
- Reduce overdraw: limit large overlapping transparent/particle layers (they re-shade pixels).
- Fewer real-time lights/shadows; bake lighting where it doesn't move.
Measure draw calls before and after — the count should drop, and so should GPU frame time.
// Unity 6 (C#). Allocating every frame fills the managed heap; the GC then stalls a frame.
// WRONG (allocates each call): foreach (var e in FindObjectsOfType<Enemy>()) ... // + LINQ, new[]
// RIGHT: cache references once, reuse buffers, avoid LINQ/boxing in Update.
void Update() {
_hits = Physics.RaycastNonAlloc(ray, _hitBuffer); // reuse a preallocated array
for (int i = 0; i < _hits; i++) { /* ... */ } // no per-frame allocation
}
// Godot/GDScript: avoid building new arrays/dictionaries every frame in _process; reuse them.
time.
a release build on target hardware for real numbers.
versa) changes nothing. Check the CPU-vs-GPU split first.
full-scene query is the real cost. Reduce the work, don't polish it.
fragmentation and GC spikes. Pool them.
Update (C#) feed the GC → periodic hitches.Cache and reuse.
materials, instance, batch.
enforce them in your build/CI checks.
manager, batching/instancing rules per engine, allocation/GC guidance, LOD/culling, and asset
budgets (texture sizes, triangle counts, audio, mobile thermals), read
references/profiling-and-budgets.md.
physics-tuning — simulation cost, fixed-step budget, sleeping bodies, broadphase layers.godot-export — release/build settings that affect measured performance.procedural-gen, game-ai — common CPU hotspots (generation, pathfinding) to budget and defer.roguelike, tower-defense, survival-crafting — entity-heavy genres that need pooling/budgets.Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take gamedev-skills/performance-optimization from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.