LLM Torch Profiler Analysis
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
Coordinate the end-to-end CAD/source-asset to SimReady workflow.
OfficialApache-2.0Auto-check: notes
Install Omniverse Cad To Simready
$ npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills omniverse-cad-to-simready --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/omniverse-cad-to-simready .claude/skills/omniverse-cad-to-simready && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "omniverse-cad-to-simready" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simready into .claude/skills/omniverse-cad-to-simready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "omniverse-cad-to-simready", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simreadyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills omniverse-cad-to-simready --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/omniverse-cad-to-simready .agents/skills/omniverse-cad-to-simready && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "omniverse-cad-to-simready" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simready into .agents/skills/omniverse-cad-to-simready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "omniverse-cad-to-simready", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills omniverse-cad-to-simready --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/omniverse-cad-to-simready .cursor/skills/omniverse-cad-to-simready && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "omniverse-cad-to-simready" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simready into .cursor/skills/omniverse-cad-to-simready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "omniverse-cad-to-simready", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/omniverse-cad-to-simready--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills omniverse-cad-to-simready --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/omniverse-cad-to-simready .gemini/skills/omniverse-cad-to-simready && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "omniverse-cad-to-simready" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simready into .gemini/skills/omniverse-cad-to-simready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "omniverse-cad-to-simready", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills omniverse-cad-to-simreadyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/omniverse-cad-to-simready .github/skills/omniverse-cad-to-simready && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "omniverse-cad-to-simready" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simready into .github/skills/omniverse-cad-to-simready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "omniverse-cad-to-simready", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills omniverse-cad-to-simready --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/omniverse-cad-to-simready .opencode/skills/omniverse-cad-to-simready && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "omniverse-cad-to-simready" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/omniverse-cad-to-simready into .opencode/skills/omniverse-cad-to-simready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "omniverse-cad-to-simready", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
omniverse-cad-to-simready- GitHub stars
- 3.6k
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,159 words
- Files
- 185 (incl. references)
- Skills in repo
- 390
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Coordinate the end-to-end CAD/source-asset to SimReady workflow.
- Works in 12 steps: Confirm the source asset path exists,… → Resolve property_assignment_intent… → Run preflight for the selected workflow… → …
- Broad requests such as CAD to SimReady
- SKILL.md covers When to Use, Prerequisites, First Action and Instructions, plus 6 more sections
- Runs Python scripts from its folder
What it does
Omniverse Cad To Simready is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 192 other files, including reference files (for example `BENCHMARK.md`, `CHANGELOG.md` and `agents/openai.yaml`). Compatibility notes: Orchestrator skill. Managed Content Agents deployment requires a configured model provider key matching the selected backend, such as NVIDIAAPIKEY…
It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- Broad requests such as CAD to SimReady
- Source asset to simulation-ready USD
- Prop packaging that require conversion
- Material/physics assignment
Example prompts
- “/omniverse-cad-to-simready”
Requirements
- Python 3
- Docker
- A credential in NVIDIA_API_KEY
- A credential in OPENAI_API_KEY
- Compatibility (from SKILL.md): Orchestrator skill. Managed Content Agents deployment requires a configured model provider key matching the selected backend, such as NVIDIA_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or GEMINI_API_KEY, Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse/content-agents at the ref pinned in upstream-versions.lock.json. Reused/provided endpoints may instead use explicit endpoint and usage-token environment variables. Linux/macOS only.
- Pre-approved tools (allowed-tools): Read, Write, Bash, WebFetch, Env
Workflow steps
12 steps, taken from the first numbered list in SKILL.md.
- Confirm the source asset path exists, resolve output_root, and classify
- Resolve property_assignment_intent before running any asset inspection,
- Run preflight for the selected workflow targets, unless a ready
- Verify or deploy Content Agents services first when
- Read references/workflow.md and references/commands.md, then run only
- Run identify-asset-context on the original source asset when web search is
- Route the source through convert-to-usd, or skip conversion for existing
- Run validate-usd-minimum before expensive downstream work. Treat this as a
- Run Content Agents material, physics, and optional texture assignment on the
- Run simready-conform-profile on the latest simulation USD path after
- Run validation gates in order: omni-asset-validate,
- Rerun simready-conform-profile when simready-validate reports a
What it can do on your machine
Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteBashWebFetchEnv
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Python, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Orchestrator skill. Managed Content Agents deployment requires a configured model provider key matching the selected backend, such as NVIDIA_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or GEMINI_API_KEY, Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse/content-agents at the ref pinned in upstream-versions.lock.json. Reused/provided endpoints may instead use explicit endpoint and usage-token environment variables. Linux/macOS only.
From compatibility in the SKILL.md frontmatter.
Context cost
Omniverse Cad To Simready loads about 2.7k tokens when it runs, and up to ~208k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 1,159 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Read, Write, Bash, WebFetch, Env
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,159 words, ~2,744 tokens.
Download SKILL.mdSave it as
.claude/skills/omniverse-cad-to-simready/SKILL.md (or your agent's skills folder). This skill also uses 184 other files; get the full folder from GitHub.- name
- omniverse-cad-to-simready
- description
- Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.
- allowed-tools
- Read, Write, Bash, WebFetch, Env
- compatibility
- Orchestrator skill. Managed Content Agents deployment requires a configured model provider key matching the selected backend, such as NVIDIA_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or GEMINI_API_KEY, Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse/content-agents at the ref pinned in upstream-versions.lock.json. Reused/provided endpoints may instead use explicit endpoint and usage-token environment variables. Linux/macOS only.
- version
- 0.2.0
- license
- Apache-2.0
- tools
- Read, Shell
- permissions
- env, file_read, file_write, network, shell
- metadata.author
- Omniverse
- metadata.tags
- physical-ai, simready, workflow, cad, conversion
- metadata.domain
- ai-ml
- metadata.languages
- python
CAD to SimReady
When to Use
Use this workflow skill for an end-to-end pipeline from a source asset to a SimReady asset or package. It coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly; do not replace it with a single monolithic runner command.
This skill is documentation-driven and does not ship scripts/run.py; it must
not depend on a repository checkout. Shell is declared because this workflow
invokes installed stage reference scripts directly, from each reference's
installed directory, and it still must not grow a monolithic runner.
Prerequisites
- Prefer
preflightfirst for deterministic setup: it installs/verifies local upstream checkouts, writes acad-to-simready-preflight.jsonmanifest, and exportsPHYSICAL_AI_PREFLIGHT_MANIFESTplusPHYSICAL_AI_REQUIRE_PREFLIGHT=1for downstream references. - Python 3.12 and
uv(per repoREADME.md). - A Content Agents model provider key when local deployment will run, or
explicit endpoint variables plus usage tokens for already-running
endpoints; see
references/preflight/README.mdfor the full list. - Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and OVRTX stages.
- Local upstream checkouts under
${OMNIVERSE_CAD_TO_SIMREADY_UPSTREAM_ROOT:-$HOME/.omniverse-cad-to-simready/upstreams}when a stage needs upstream scripts or specs.
First Action
For any broad CAD/source-asset to SimReady request, assume
property_assignment_intent=run unless the user explicitly asks for
conversion-only, validation-only, or no material/physics assignment.
For conversion-only requests, set property_assignment_intent=skip, do not
deploy Content Agents, run convert-to-usd, then run validate-usd-minimum
on the generated USD if conversion succeeds. For validation-only requests,
set property_assignment_intent=skip and validate the USD the user
provided without rerunning conversion.
Run preflight (or verify an existing PHYSICAL_AI_PREFLIGHT_MANIFEST)
before any converter, validation, Content Agents, OVRTX, packaging, or FET
step; treat it as dependency bootstrap, not workflow routing. Use
--skip-content-agents for conversion-only/validation-only requests.
When property_assignment_intent=run, verify or deploy Content Agents
services immediately after confirming the source path and resolving intent,
before asset-context inspection, converter dependency checks, conversion,
validation, conformance, rendering, packaging, or upstream source builds.
Treat explicitly provided healthy endpoints as user-owned; otherwise run deploy-content-agents,
which deploys the shared standalone OVRTX renderer, then Material, Physics,
and optional Texture service containers in order.
Instructions
- Confirm the source asset path exists, resolve
output_root, and classify the request as end-to-end, conversion-only, validation-only, or packaging. - Resolve
property_assignment_intentbefore running any asset inspection, converter probe, conversion, validation, conformance, rendering, or packaging step. - Run
preflightfor the selected workflow targets, unless a readyPHYSICAL_AI_PREFLIGHT_MANIFESTis already configured. Source the generated env file before running downstream scripts. Treat preflight as dependency setup only: it may use a provided--source-asset,--source-format, or--conversion-toolsvalue to scope dependency checks, butconvert-to-usdand the upstream converter references still decide actual conversion support. - Verify or deploy Content Agents services first when
property_assignment_intent=run; block on missing authentication or unhealthy services instead of continuing. - Read
references/workflow.mdandreferences/commands.md, then run only the stage references needed for the current request. - Run
identify-asset-contexton the original source asset when web search is available or property assignment will run. - Route the source through
convert-to-usd, or skip conversion for existing USD input and treat the source path as the current USD path. - Run
validate-usd-minimumbefore expensive downstream work. Treat this as a viability gate only: record unit/profile issues such asmetersPerUnit != 1.0, but do not runsimready-conform-profile, FET001, or any other FET repair before Content Agents assignment when property assignment will run. - Run Content Agents material, physics, and optional texture assignment on the converted/minimum-valid USD when requested or required.
- Run
simready-conform-profileon the latest simulation USD path after property assignment and preserve every selected FET repair report. - Run validation gates in order:
omni-asset-validate,omni-asset-validate-geometry,omni-asset-validate-physics, andsimready-validate. - Rerun
simready-conform-profilewhensimready-validatereports a repairable requirement, then rerun profile validation on the newest authored USD. - Run
ovrtx-render-servicewhen preview, thumbnail, or inspection images are requested. When package outputs are requested, runassemble-package-sourcenext to create the cleandeliverable/package source from the final USD and thumbnail, then runnv-core-package-sampleandnv-core-package-sample-validationon that deliverable folder only. - Emit the consolidated workflow report with the final USD path, all stage reports, validation findings, rerun reasons, and next work.
Output Format
Emit a consolidated workflow report in Markdown, and include JSON when the
workflow writes structured artifacts. Report overall status as passed,
blocked, failed, or needs_rerun. See references/workflow.md for the
required Markdown and JSON report fields.
Show full SKILL.md (458 more words)Show less
Detailed References
Read only the references needed for the current request:
references/preflight/README.md: deterministic local setup, manifest/env contract, wrappers, deployment opt-out, and guardrail behavior.references/workflow.md: inputs, source routing, detailed workflow, validation policy, output report fields, and next steps.references/commands.md: concrete portable script command patterns.references/assemble-package-source/README.md: two-zone package source assembly, root USD naming, thumbnail placement, and deliverable checks.references/troubleshooting.md: symptom/cause/fix table plus FET (GSP.001/RB.MB.001) repair-routing detail.references/publishing-layout.md: frontmatter compatibility-field notes and layout rationale for this skill's own file tree.
Publishing Layout Notes
Use skills/omniverse-cad-to-simready/ as the source of truth for this
product repo's skill. The .agents/skills symlink is a compatibility alias
for local agentskills.io-style discovery, and the nested references/ tree
is intentional. See references/publishing-layout.md for the alias list,
frontmatter field placement, and flattening rules.
Limitations
- This workflow coordinates existing conversion, property assignment, conformance, validation, rendering, and packaging skills; it does not replace them with a single monolithic runner command.
Troubleshooting
Read references/troubleshooting.md only when a specific stage or validation
gate is failing; it owns the symptom/cause/fix table and FET repair routing.
Hard Rules
- Prefer the preflight manifest for local upstream roots, converter
executables, SimReady validation runtime, OVRTX endpoint, and Content Agents
service URLs. When
PHYSICAL_AI_REQUIRE_PREFLIGHT=1is set, do not bypass the manifest with direct upstream discovery. - Do not run asset inspection, converter probes, local upstream builds, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run.
- Use stage-specific installed reference scripts directly. Do not add or call a
single
omniverse-cad-to-simreadyrunner command. - For source conversion, delegate to the
convert-to-usdreference; do not substitute another converter for CAD or mesh formats. - For property assignment, use Content Agents references as separate atomic steps: material first, then physics, then texture only when requested.
- When property assignment will run, do not run
simready-conform-profileor any FET helper before Content Agents. Validate minimum USD first, then run Content Agents on that converted/minimum-valid USD, then apply FET repairs to the latest service-authored USD. - When property assignment will run, do not run
simready-validateor any SimReady profile validation before Content Agents. The only validation gate allowed before service calls isvalidate-usd-minimum, which is a basic USD viability check. - Stop at the first failing deployment, conversion, property-assignment, or conformance authoring gate unless the user explicitly asks for best-effort continuation.
- Do not stop at validation findings after a meaningful USD artifact exists.
Continue remaining diagnostic gates and mark the result
needs_rerun. - Do not leave a
GSP.001profile failure as an unclassified final finding. Route it to upstreamsimready-foundation-conform-fet-005-simulate-grasp-physics; if the current agent cannot inspect renders or no explicit grasp points are available, report a blocked FET005 repair with the visual evidence path or missing input reason. - Preserve every stage report and pass the concrete output USD path from each report into the next stage.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 184 other files (references) in skills/omniverse-cad-to-simready of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- CHANGELOG.md
- agents/openai.yaml
- config/skillspector-baseline.yaml
- evals/evals.json
- evals/files/minimal_mesh.stl
- references/assemble-package-source/README.md
- references/assemble-package-source/scripts/check_dependencies.py
- references/assemble-package-source/scripts/report_schema.json
- references/assemble-package-source/scripts/run.py
- references/commands.md
- references/content-agents/README.md
- … and 172 more
Open the folder on GitHubat commit 14a98ae
Compare with similar skills
Omniverse Cad To Simready next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Omniverse Cad To Simready this skillNVIDIA/skills | 3.6k | — | ~2.7k | Automated safety check: Notes | Apache-2.0 | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Megatron-LM Container and Dependency SetupNVIDIA/Megatron-LM | 18k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 31k | — | ~604 | Automated safety check: Pass | MIT | |
| Megatron-LM Base Image BumpNVIDIA/Megatron-LM | 18k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 |
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Related
Works with
Questions about Omniverse Cad To Simready
What does Omniverse Cad To Simready do?
Coordinate the end-to-end CAD/source-asset to SimReady workflow. Omniverse Cad To Simready is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Coordinate the end-to-end CAD/source-asset to SimReady workflow.
When should I use Omniverse Cad To Simready?
Omniverse Cad To Simready fits situations like: broad requests such as CAD to SimReady; source asset to simulation-ready USD; prop packaging that require conversion; material/physics assignment.
How do I install Omniverse Cad To Simready in Claude Code?
Run `npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a claude-code`. Or copy the skill folder (skills/omniverse-cad-to-simready in NVIDIA/skills) into .claude/skills/omniverse-cad-to-simready in your project. Claude Code loads it when a task matches its description.
How do I install Omniverse Cad To Simready in Codex?
Run `npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a codex`. Or copy the skill folder (skills/omniverse-cad-to-simready in NVIDIA/skills) into .agents/skills/omniverse-cad-to-simready in your project. Codex loads it when a task matches its description.
Can I use Omniverse Cad To Simready in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill omniverse-cad-to-simready -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/omniverse-cad-to-simready, .gemini/skills/omniverse-cad-to-simready, .github/skills/omniverse-cad-to-simready and .opencode/skills/omniverse-cad-to-simready in your project.
What does Omniverse Cad To Simready need to run?
Going by SKILL.md and its folder, Omniverse Cad To Simready needs Python for the scripts in its folder. Our summary lists: Python 3; Docker; A credential in NVIDIA_API_KEY; A credential in OPENAI_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Bash, WebFetch, Env. Compatibility (from SKILL.md): Orchestrator skill. Managed Content Agents deployment requires a configured model provider key matching the selected backend, such as NVIDIA_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or GEMINI_API_KEY, Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse/content-agents at the ref pinned in upstream-versions.lock.json. Reused/provided endpoints may instead use explicit endpoint and usage-token environment variables. Linux/macOS only. .
Does Omniverse Cad To Simready access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Omniverse Cad To Simready safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Omniverse Cad To Simready use?
Omniverse Cad To Simready is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Omniverse Cad To Simready use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 205k tokens, read only when the agent opens those files.
What are the alternatives to Omniverse Cad To Simready?
Skills that share tags, products or a category with Omniverse Cad To Simready: LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Skill Inspector (NVIDIA/SkillSpector, 20k stars), Megatron-LM Container and Dependency Setup (NVIDIA/Megatron-LM, 18k stars) and Embeddings via 9Router (decolua/9router, 31k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Omniverse Cad To Simready?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.