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 skills add dtunai/agent-skills-for-compute --skill omniverse-simreadygit clone --depth 1 https://github.com/dtunai/agent-skills-for-computeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dtunai/agent-skills-for-compute/omniverse-simready)<a href="https://agentmods.dev/skills/dtunai/agent-skills-for-compute/omniverse-simready"><img src="https://agentmods.dev/badge/skills/dtunai/agent-skills-for-compute/omniverse-simready/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dtunai/agent-skills-for-compute/omniverse-simready"><img src="https://agentmods.dev/badge/skills/dtunai/agent-skills-for-compute/omniverse-simready.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.03212 |
| Opus 5 | $0.00017 | $0.01606 |
| Sonnet 5 | $0.00007 | $0.00642 |
| Haiku 4.5 | $0.00003 | $0.00321 |
Grade A, and why
omniverse-simready 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 11d 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 — 513 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NVIDIA Omniverse SimReady Skill
Standard and ecosystem for physically accurate 3D assets built on OpenUSD with semantic labeling, physics properties, and metadata for robotics simulation, digital twins, and synthetic data generation.
Official Sources:
- SimReady Overview
- SimReady Specification
- Asset Creation Guide
- Semantic Labeling
- Physics Best Practices
- Isaac Sim Integration
What is SimReady?
Definition:
"Simulation-ready assets are physically accurate 3D objects that incorporate real-world properties, behaviors, and data bindings."
Built on OpenUSD:
- Universal Scene Description platform
- Modular, flexible composition
- Cross-platform compatibility
More Than Visual:
- Semantic labeling (WikiData integration)
- Physics properties (USDPhysics schema)
- Non-visual sensor attributes
- Dense captions for context
Core Requirements
Modeling Standards
Scale:
- Real-world scale
- Z-up orientation
- Export in meters
- Pivot at origin
Geometry:
- Optimal poly count
- Clean topology
- Named hierarchies
- Forward-facing in viewport
UVs:
- Non-overlapping islands
- UV Channel 1 only
- 0-1 UV space
- 512+ pixels/meter texel density
Material Standards
Workflow:
- PBR Metal-Rough mandatory
- UsdPreviewSurface for portability
- OmniPBR/OmniGlass/SimPBR for Omniverse
Textures:
- 4K or 8K maximum resolution
- One material per object
Shaders:
- UsdPreviewSurface (cross-platform)
- Omniverse MDL materials (advanced)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 513 lines · 35 tokens per session scan A 3a131325a095
omniverse-simready is a skill published in the GitHub repository dtunai/agent-skills-for-compute (2 stars, last pushed 6mo ago), licensed MIT. It adds 35 tokens to every session and 3,212 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 2026-08-31.
Other skills, from other repositories
gameobject-component-destroy
Destroy one or more Components from a target GameObject. Missing (null) components are skipped — they cannot be destroyed. Use 'gameobject-find' and 'gameobject-component-get' to identify the components first.
unity-version-split
Split a C# file into Unity 6.5+ and pre-Unity 6.5 variants. Use when a file needs different implementations for different Unity versions due to API changes (e.g., EntityId vs int, GetEntityId vs GetInstanceID).
godot-signals-groups
Build event-driven, decoupled Godot 4.7 gameplay with signals and node groups: declare and emit custom signals, connect with Callables (incl. bind/one-shot), and broadcast to many nodes via groups and callgroup. Use when wiring node communication in a Godot project, replacing tight references with signals…
unity-addressables
Manage Addressables groups, entries, profiles and content builds (com.unity.addressables, reflection-based).
motion
How an agent turns a character mesh into a usable animated FBX — and how to judge whether the result is shippable.
threejs-exposure-color-grading
Build a measured exposure and grading path in Three.js. Use for a 64x36 encoded luminance meter, asynchronous readback, weighted log-average exposure, asymmetric adaptation, single tone-map ownership, and a generated 32-cube post-tone-map LUT.