Agent skill · Security

omniverse-usd-performance-tuning

Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexships scriptsApache-2.0
Install
npx skills add NVIDIA/skills --skill omniverse-usd-performance-tuning --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 106
SKILL.md size: 14 KB
Bundled scripts: yes
Version: 0.1.0
Declared author: NVIDIA Omniverse
Requires: > Orchestrator skill. Downstream phases may require Kit, Usd Optimize, usd-validation-nvidia, USD Python, writable…
Path: skills/omniverse-usd-performance-tuning/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Omniverse USD Performance Tuning ## Scope Use for broad USD performance work: slow loading, low FPS/interactivity, high GPU or system memory, GPU crash/device lost, validation failures, CAD/conversion-quality triage, profiling, or requests to optimize a scene. This skill owns the user-facing workflow; setup, authentication, profiling, validation, mutation, and reporting are executed by phase ref

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About this skill
What does the omniverse-usd-performance-tuning skill do?

Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.

How do I install it?

Run `npx skills add NVIDIA/skills --skill omniverse-usd-performance-tuning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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