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.
npx agentmods add skills/thearcforge/uniclaude/unity-performancenpx skills add TheArcForge/UniClaude --skill unity-performancegit clone --depth 1 https://github.com/TheArcForge/UniClaudeWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00033 | $0.01192 |
| Opus 5 | $0.00016 | $0.00596 |
| Sonnet 5 | $0.00007 | $0.00238 |
| Haiku 4.5 | $0.00003 | $0.00119 |
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.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unity Performance — Full Decision Framework
Use this when unity-architect's condensed performance section needs more depth.
Before Optimizing
Profile first. Never optimize based on assumptions.
Tools:
- Unity Profiler (CPU, GPU, Memory modules)
- Frame Debugger (draw calls, batching, overdraw)
- Memory Profiler package (heap snapshots, native allocations)
- project_get_console_log — check for warning spam (often a hidden perf cost)
Rule: Identify the bottleneck, then optimize that specific thing. Optimizing the wrong thing is wasted effort.
CPU Cost Model
Expensive (avoid in hot paths):
- GetComponent() — reflection-based lookup. Cache in Awake().
- GameObject.Find() / FindObjectOfType() — linear scene scan. Never in Update.
- string concatenation — allocates new string each time. Use StringBuilder.
- LINQ in Update — allocates enumerators and closures.
- Instantiate/Destroy — heavyweight. Pool for frequent use.
- Physics.Raycast (many per frame) — use NonAlloc variants, limit ray length.
- SendMessage / BroadcastMessage — reflection-based. Use direct calls or events.
Cheap:
- transform.position (cached by engine)
- Comparing tags (CompareTag, not ==)
- Null checks on Unity objects (but NOT ?? or ?. — those bypass Unity's null check)
- Static method calls, struct operations
- Fixed-size arrays, pre-allocated lists
GPU Cost Model
Draw calls (CPU→GPU overhead per rendered object):
- Each unique material = at least one draw call.
- Reduce by: batching (static/dynamic/GPU instancing/SRP batcher), atlasing textures.
- Target: < 100 on mobile, < 2000 on console, < 5000 on PC.
Fill rate (per-pixel cost):
- Transparent objects drawn back-to-front, no early-Z rejection.
- Overdraw: overlapping transparent objects multiply pixel cost.
- Reduce by: smaller particles, opaque where possible, LOD.
Shader complexity:
- Per-vertex operations are cheap (scale with mesh complexity).
- Per-pixel operations are expensive (scale with screen resolution).
- Texture samples per pixel: each costs bandwidth. Combine maps.
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.
- 2d ago First seen · 136 lines · 33 tokens per session scan A 21a4e74fa9bb
unity-performance is a skill published in the GitHub repository TheArcForge/UniClaude (51 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 1,192 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
unity-ai-behavior
Use when implementing AI and NPC behavior — state machines, behavior trees, GOAP, NavMesh navigation, decision-making patterns, and when to use each approach.
unity-addressables
Use when managing asset loading — Addressables vs Resources vs direct references, async loading patterns, memory management, group strategies, and content update workflows.
unity-audio
Use when implementing audio systems — audio manager architecture, AudioMixer setup, spatial audio, event-driven audio, music systems, and sound effect management patterns.
unity-performance
Use when analyzing or optimizing Unity performance — CPU/GPU/RAM profiling, object pooling, batching strategies, physics optimization, memory management, and platform-specific budgets.
unity-testing
Use when writing or structuring tests — EditMode vs PlayMode test decisions, test architecture, what to test in Unity, mocking strategies, and CI integration for Unity tests.
animation-workflow
Use when setting up animations — Animator Controllers, Animation Clips, state machines, blend trees, animation events, and Avatar configuration.