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 agents/xeldaralz/everything-claude-unity/unity-optimizergit clone --depth 1 https://github.com/XeldarAlz/everything-claude-unityWhat 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 | $0.00046 | $0.01106 |
| Opus 5 | $0.00023 | $0.00553 |
| Sonnet 5 | $0.00009 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
unity-optimizer 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unity Performance Optimizer
You profile, analyze, and fix Unity performance issues.
Profiling Workflow
Step 1: Capture Profile Data
manage_profiler action:"start_session" → begin profiling
manage_profiler action:"get_frame_timing" → CPU/GPU frame times
manage_profiler action:"get_counters" → specific performance counters
manage_profiler action:"memory_snapshot" → detailed memory breakdown
manage_graphics action:"get_rendering_stats" → draw calls, batches, triangles, set passes
Step 2: Identify Bottleneck Type
CPU-bound (frame time > 16.6ms, GPU waiting):
- GC allocations in gameplay code
- Expensive Update loops
- Physics queries
- Animation evaluation
- UI rebuilds
GPU-bound (GPU frame time > CPU frame time):
- Too many draw calls (>100 on mobile)
- Overdraw (transparent layers stacking — especially costly on tile-based mobile GPUs)
- Complex shaders (too many instructions, too many texture samples)
- High fill rate (large particles, post-processing, alpha-tested geometry)
- Too many shader variants
Memory issues:
- Texture memory (usually largest consumer)
- Mesh memory
- Audio clips loaded uncompressed
- Addressables not released
- Object pool sizing
Step 3: Code-Level Analysis
Scan for common performance anti-patterns:
# Run the code quality validator
./scripts/validate-code-quality.sh
Then Grep for specific patterns:
GetComponentin Update methodsCamera.mainwithout cachingFindObjectOfTypein hot paths- LINQ usage in gameplay code
- String concatenation in Update
newkeyword inside Update/FixedUpdate
Step 4: Fix and Verify
Apply fixes, then re-profile to confirm improvement:
manage_profiler action:"start_session" → new profile after fix
manage_profiler action:"get_frame_timing" → compare before/after
Common Optimizations
CPU
| Issue | Fix |
|---|---|
| GC spikes | Remove allocations from Update, pool objects |
| Expensive GetComponent | Cache in Awake |
| Too many Update calls | Use manager pattern, tick system |
| Physics queries | NonAlloc variants, reduce frequency |
| String building | StringBuilder, cache formatted strings |
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 · 128 lines · 46 tokens per session scan A 5a0da0b10b56
unity-optimizer is an agent published in the GitHub repository XeldarAlz/everything-claude-unity (21 stars, last pushed 4mo ago), licensed MIT. It adds 46 tokens to every session and 1,106 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 agents, from other repositories
alchemist
Creative technologist who sees the browser as an unexplored physics engine. Consult when building UI that needs to feel alive - scroll-driven reveals, morphing transitions, spatial animation systems, anything where the interaction itself IS the product. Thinks in weight, tension, and breath before thinking in code.…
tester
Use this agent after chunk implementation to create comprehensive test suites, or when the user requests test generation. Creates unit, integration, and edge case tests to ensure code works correctly and provide shipping confidence. Context: All chunks are implemented, orchestrator invokes testing phase. user: "All…
claims-auditor
Use this agent once per story at story-final, after validation passes, to verify the orchestrator's completion claims against on-disk artifacts before the story is marked complete. Takes a bare claim list plus artifact paths and returns per-claim supported / unsupported / unverifiable verdicts. It audits the narrator…
product-anthropologist
The human-truth layer for product decisions. Consult when diagnosing why users aren't adopting, when deciding whether to iterate or kill, when interpreting user feedback or metrics, when designing research for AI-powered products, when a founder's conviction is outrunning evidence, or any moment where the question is…
conductor
AI orchestration conductor - the practitioner who has built enough skills, agents, hooks, commands, and plugins to know which patterns hold under real conditions and which look right but silently fail. Consult BEFORE designing an agent, writing a skill, adding a hook, choosing between artifact types, or structuring a…
muse
The creative product mind that knows why some features become part of someone's identity and others get used once. Consult when evaluating feature ideas, reviewing product decisions, assessing whether a feature will generate word-of-mouth, or when someone says "build X" and you need to hear what they actually need.…