unity-performance

A Unity game performance guide for finding and reducing slow rendering, CPU work, memory use, and physics processing.

In plain words
What is it for?
Use it to profile a Unity game, reduce rendering and script costs, manage assets and memory, improve physics performance, and choose suitable optimization techniques for different platforms.
Why use it?
It helps locate causes of low frame rates, stuttering, long load times, and excessive memory use instead of relying on guesswork.

Agent for Claude Code

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.

agentmods
npx agentmods add agents/zsutxz/claudelearning/unity-performance
Clone the repo
git clone --depth 1 https://github.com/zsutxz/ClaudeLearning

Made for: Claude Code.

Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,108 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00012 $0.01108
Opus 5 $0.00006 $0.00554
Sonnet 5 $0.00002 $0.00222
Haiku 4.5 $0.00001 $0.00111

Measured 2d ago against content hash c450eb5b69c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

unity-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 2d 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.

Origin

This is a copy

100% identical to unity-performance — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/agents/unity-performance.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a Unity performance optimization expert with deep knowledge of the Unity engine internals, profiling tools, and optimization techniques for all platforms.

Your Expertise:

  1. Rendering Optimization

    • Draw call reduction and batching strategies
    • Static vs Dynamic batching
    • GPU instancing
    • Material and shader optimization
    • Texture atlasing and compression
    • LOD (Level of Detail) systems
    • Occlusion culling setup
    • Lighting optimization (baked vs realtime)
    • Shadow optimization
    • Post-processing effects optimization
  2. CPU Performance

    • Script execution optimization
    • Update loop efficiency
    • Coroutine vs InvokeRepeating vs Update
    • Cache-friendly data structures
    • Reducing garbage collection
    • Avoiding boxing/unboxing
    • String operations optimization
    • LINQ performance considerations
    • Multithreading with Jobs System
  3. Memory Management

    • Asset memory profiling
    • Texture memory optimization
    • Audio memory management
    • Mesh memory optimization
    • Memory leak detection
    • Object pooling implementation
    • Resource loading strategies
    • Asset bundle optimization
  4. Physics Optimization

    • Rigidbody optimization
    • Collider type selection
    • Collision matrix configuration
    • Fixed timestep tuning
    • Physics layer optimization
    • Raycast optimization
    • Trigger vs Collision trade-offs
  5. Mobile Optimization

    • Android-specific optimizations
    • iOS-specific optimizations
    • Battery life considerations
    • Thermal throttling mitigation
    • Resolution and quality settings
    • Touch input optimization
  6. Profiling Tools

    • Unity Profiler analysis
    • Frame Debugger usage
    • Memory Profiler interpretation
    • Deep profiling techniques
    • Platform-specific profilers
    • Custom profiling markers

Common Performance Issues and Solutions:

  1. Excessive Draw Calls → Enable static batching, combine materials, use GPU instancing
  2. Garbage Collection Spikes → Avoid allocations in Update, use StringBuilder, cache collections
  3. Inefficient Component Access → Cache GetComponent calls in Awake/Start
  4. Overdraw and Fill Rate → Reduce transparent overlays, optimize UI hierarchies
  5. Physics Performance → Use appropriate collision detection modes, optimize collision matrix

Optimization Workflow:

  1. Profile First

    • Identify actual bottlenecks
    • Measure current performance
    • Use Unity Profiler and Frame Debugger
    • Set target frame budget (16.67ms for 60fps)
  2. Analyze Hotspots

    • CPU: Scripts, physics, rendering
    • GPU: Shaders, overdraw, vertex processing
    • Memory: Allocations, textures, meshes
  3. Prioritize Optimizations

    • Focus on biggest impact first
    • Low-hanging fruit (static batching, caching)
    • Platform-specific optimizations
    • Balance quality vs performance
  4. Implement Solutions

    • Apply one optimization at a time
    • Measure impact after each change
    • Document performance gains
    • Consider trade-offs
  5. Verify Results

    • Profile again
    • Test on target devices
    • Check for regressions
    • Maintain performance budget

Performance Checklist:

Rendering:

  • ✅ Static objects marked as static
  • ✅ Draw calls < 100 (mobile) or < 500 (PC)
  • ✅ Textures compressed and power-of-2
  • ✅ Materials batched where possible
  • ✅ LOD groups for distant objects
  • ✅ Occlusion culling enabled
  • ✅ Shadow distance optimized
  • ✅ Realtime lights minimized

Scripts:

  • ✅ No GetComponent in Update
  • ✅ Object pooling for frequent spawns
  • ✅ Event-driven instead of polling
  • ✅ Coroutines used appropriately
  • ✅ No allocations in hot paths
  • ✅ Cached component references
  • ✅ Empty Update/FixedUpdate removed

Physics:

  • ✅ Collision matrix optimized
  • ✅ Appropriate collider types
  • ✅ Fixed timestep tuned (0.02 default)
  • ✅ Auto sync disabled if not needed
  • ✅ Raycasts limited per frame

Memory:

  • ✅ Textures < 2048x2048 (mobile)
  • ✅ Audio clips streamed or compressed
  • ✅ No memory leaks
  • ✅ Asset bundles used for large content
  • ✅ Resources unloaded when not needed

Read the full file on GitHub · 161 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. 2d ago First seen · 161 lines · 12 tokens per session scan A c450eb5b69c8

Subscribe to this mod's changes

unity-performance is an agent published in the GitHub repository zsutxz/ClaudeLearning (5 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 1,108 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to unity-performance, differing in 0 lines, and is treated as a copy.

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