Using Ovnewton
NVIDIAOfficialCreated by Jan CariusDistributed by NVIDIA
- Skill
- Tool integration
- Automation and integrations
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The ovnewton skill provides a bridge between the Newton physics engine and Omniverse stages. It enables developers to import populated stages into Newton, execute physics simulations, and synchronize state updates back to the stage for rendering or further processing. The tool manages the integration of physics solvers, collision handling, and simulation loops while maintaining consistency between the physics model and the USD-based stage representation.
What it does
- Imports populated ovstage data into the Newton physics engine
- Registers USD schemas for physics compatibility
- Supports integration with MuJoCo and VBD solvers
- Synchronizes simulation states back to the Omniverse stage
- Manages physics simulation loops and state updates
- Provides mechanisms for publishing simulation results for rendering
When to use it
- When integrating Newton physics into an Omniverse-based application
- When you need to run physics simulations on a populated USD stage
- When synchronizing physics simulation outputs with Omniverse rendering
- When managing complex physics state updates between a solver and a stage
Advantages
- Enables direct physics simulation of USD-based scenes
- Supports multiple solver types including MuJoCo and VBD
- Provides structured workflows for state synchronization and publication
- Allows for custom attribute registration within the simulation model
Usage examples
- “How do I initialize a basic Newton simulation with an Omniverse stage?”
- You install the package, call register_usd_schemas, populate your stage, and use the ModelBuilder to import the stage into Newton before finalizing the simulation.
- “How can I synchronize my physics simulation state back to the stage?”
- After each simulation step, you use the binding.update_to_ovstage method to publish the current state and seal it for the renderer.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/using-ovnewton/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip using-ovnewton.zip -d .agents/skills
Documentation by Jan Carius
Using ovnewton
Use ovnewton for the Newton ↔ ovstage bridge. The application owns model finalization, the solver, controls, collision handling, and the simulation loop. ovnewton reads a populated ovstage; Newton does not parse USD in this workflow. Standalone Newton simulations and generic Omniverse authoring do not need this skill.
Start from the supported examples
Install ovnewton in the application's environment. This workflow requires
Newton 1.7 or newer, OVStage 0.3, and OVRTX 0.6 when rendering. Let its package metadata
select compatible dependencies; do not replace the qualified ovstage dependency
with an arbitrary build.
pip install ovnewton
python -m ovnewton.examples.example_ovnewton_basic --device cpu --no-render --num-frames 8For rendering or MuJoCo-Warp, install "ovnewton[examples]". The basic example
accepts --solver mujoco or --solver vbd; VBD also supports CPU simulation.
Rendering needs a supported NVIDIA GPU and its own device selection.
Read the getting-started guide and adapt the basic example. For multiple robots, use the robot example. Use documentation and examples corresponding to the installed ovnewton release; repository main may describe a newer API.
Construct and bind the simulation
Call
ovnewton.register_usd_schemas()before the first USD schema read. Create anovstage.Stage()and populate the application-supplied USD withpopulation.open_usd_with_desc(stage, scene_path, ordinal=1, time=population.DEFAULT_TIME, descs=(ovnewton.population_desc(),)). Add apopulation.Desc(domains=PopulationDomain.RENDERING)descriptor when rendering.ovnewton.population_desc()preserves composed schema names so unsupported physics remains detectable even without its schema plugin. Seal population withstage.advance_write_floor(1).wait()before importing. For rendering, create stages withovstage.StageConfigselectingruntime_default_hierarchy_computation_model=ovstage.HierarchyComputationModel.GPU_INCREMENTAL, including CPU physics with a GPU renderer. This setting is process-wide; coexisting stages must use a consistent configuration. CPU-only runs do not need this setting. An existing populated stage must preserve the same composed schema metadata; attachment cannot recover schema names discarded during population. If the application already supplies a populated stage, reuse it and its sealed ordinal; do not reload the scene or assume its ordinal is 1.Create an empty
newton.ModelBuilder(). Configure defaults and register any solver-owned custom attributes before callingovnewton.add_ovstage(builder, stage, ordinal=population_ordinal). Compose all scene assets into one ovstage first: this entry point currently requires a builder without model entities and supports one stage per builder.For MuJoCo, register
SolverMuJoCo.register_custom_attributes(builder)before callingovnewton.add_ovstage(). For rigid-body VBD, registerSolverVBD.register_custom_attributes(builder)beforeovnewton.add_ovstage()and callbuilder.color()after that import, before finalization. UseSolverVBD(model, rigid_compliant_alm=True)for compliant rigid-body contacts, following the example's solver settings and contact-material tuning.Finalize with
builder.finalize(skip_validation_joints=import_result.has_orphan_joints), whereimport_resultis the result ofadd_ovstage. Construct the solver and bind withovnewton.attach_ovstage(stage, model=model, ordinal=population_ordinal).attach_ovstage(stage)is the convenience path when no builder customization is needed. When binding an existing model, body and joint labels must match stage paths.
Step and synchronize
Allocate two model states, controls, and contacts. Each substep clears forces, computes contacts, calls the solver, and swaps the states. Start from the example's collision pipeline and solver configuration rather than assuming every solver has identical preparation requirements.
After stepping, publish the current state with
binding.update_to_ovstage(state, ordinal=output_ordinal), then seal it with
stage.advance_write_floor(output_ordinal).wait(). Coordinate increasing write
ordinals with the application's other stage writers.
To consume application edits, seal them first and call
binding.update_from_ovstage(state, control) before stepping, using the same
control passed to the solver. This reads the latest sealed supported state and
drive-target fields. An explicit read ordinal is a consistency ceiling, not
historical playback. Selected Newton-native output can instead be read through
binding.query() and binding.read() without stage publication.
For rendering, publish and seal Newton output before the renderer consumes the stage. Newton's simulation device and OVRTX's rendering device are independent; follow the example's synchronization and device-selection options.
Check the support guide before promising support for authored physics. Unsupported physics may fail attachment; do not silently replace it with a different physical model. See the concepts guide and output-read guide for synchronization, diagnostics, read layouts, and lifetimes.
Files
- ATTRIBUTION.mdAdded by AutoGineer904 B
- BENCHMARK.md8.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md6.0 KiB
- evals/EVAL.md1.8 KiB
- evals/config.yml198 B
- evals/evals.json5.5 KiB
- skill-card.md5.4 KiB
- skill.oms.sig4.9 KiB
BENCHMARK.md
Skill Benchmark: using-ovnewton
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
using-ovnewtonEvaluation date: 2026-10-19
Evaluator version:
1.5.6Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)Tasks: 7 evaluation tasks (5 positive, 2 negative)
Dataset digest:
sha256:229164b02575b0e70ff657dae1616a1289f9db36e537a0fbb9465d6694922319(skill-evaluator-dataset-snapshot/1)Attempts per task: 1
Environment:
k8s-sandboxTier 2 evidence: required for publication
Tier 3 evidence: required for publication
Each task attempt ran in its own isolated sandbox pod.
What This Report Answers
The three-tier evaluation checks whether the skill:
is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.
Results at a Glance
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 95.2% — baseline ran, but no comparable score was available; uplift unavailable | 83.3% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 57.1% → 100.0% (+42.9 points) | 100.0% → 85.7% (-14.3 points) |
| Correctness | 71.4% → 94.3% (+22.9 points) | 85.7% → 88.6% (+2.9 points) |
| Discoverability | 100.0% — baseline ran, but no comparable score was available; uplift unavailable | 94.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 55.0% → 87.9% (+32.9 points) | 58.2% → 79.3% (+21.1 points) |
| Efficiency | 93.9% — baseline ran, but no comparable score was available; uplift unavailable | 69.1% — baseline ran, but no comparable score was available; uplift unavailable |
How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.
Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.
A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.
Token Usage
Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 1,485,116 | 7,071,032 | -5,585,916 | -79.00% | skill 7/7; base 7/7 |
| claude-code | using-ovnewton-basic | 215,800 | 2,920,898 | -2,705,098 | -92.61% | skill 1/1; base 1/1 |
| claude-code | using-ovnewton-existing-stage | 63,726 | 160,755 | -97,029 | -60.36% | skill 1/1; base 1/1 |
| claude-code | using-ovnewton-newton-only | 986,312 | 1,140,901 | -154,589 | -13.55% | skill 1/1; base 1/1 |
| claude-code | using-ovnewton-omniverse-authoring | 31,404 | 35,063 | -3,659 | -10.44% | skill 1/1; base 1/1 |
| claude-code | using-ovnewton-rendering | 62,969 | 919,649 | -856,680 | -93.15% | skill 1/1; base 1/1 |
| claude-code | using-ovnewton-stage-edits | 62,110 | 139,415 | -77,305 | -55.45% | skill 1/1; base 1/1 |
| claude-code | using-ovnewton-vbd | 62,795 | 1,754,351 | -1,691,556 | -96.42% | skill 1/1; base 1/1 |
| codex | All cases | 800,187 | 1,376,483 | -576,296 | -41.87% | skill 7/7; base 7/7 |
| codex | using-ovnewton-basic | 169,217 | 655,583 | -486,366 | -74.19% | skill 1/1; base 1/1 |
| codex | using-ovnewton-existing-stage | 113,244 | 92,087 | +21,157 | +22.98% | skill 1/1; base 1/1 |
| codex | using-ovnewton-newton-only | 372,250 | 320,070 | +52,180 | +16.30% | skill 1/1; base 1/1 |
| codex | using-ovnewton-omniverse-authoring | 19,430 | 19,614 | -184 | -0.94% | skill 1/1; base 1/1 |
| codex | using-ovnewton-rendering | 41,084 | 58,568 | -17,484 | -29.85% | skill 1/1; base 1/1 |
| codex | using-ovnewton-stage-edits | 40,854 | 84,070 | -43,216 | -51.40% | skill 1/1; base 1/1 |
| codex | using-ovnewton-vbd | 44,108 | 146,491 | -102,383 | -69.89% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 2,285,303 | 8,447,515 | -6,162,212 | -72.95% | skill 14/14; base 14/14 |
Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 10 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 7 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (
skills/using-ovnewton/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (
skills/using-ovnewton/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (
skills/using-ovnewton/SKILL.md)MEDIUM SECURITY/Unsafe Defaults (TM3): Tool Misuse: skip_validation (
SKILL.md:69)LOW QUALITY/quality_discoverability: Description very long (243 chars, recommend 50-150) (
skills/using-ovnewton/SKILL.md)5 additional finding(s) are available in the full evaluation artifacts.
</details>
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did it avoid wasted tool calls and token usage? | skill_efficiency (50%) + token_efficiency (50%) |
Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (
goal_accuracy) and expected workflow adherence (behavior_check).Efficiency is 50% tool-call productivity (the backward-compatible
skill_efficiencywire id) and 50%token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.
Signals present in this run:
security(Security): unsafe operations, secret leakage, and unauthorized access.skill_execution(Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.skill_efficiency(Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).accuracy(Accuracy): final-answer correctness against the reference answer.goal_accuracy(Goal Accuracy): whether the user's goal was achieved.behavior_check(Behavior Check): whether the expected workflow behavior was followed.token_efficiency(Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
</details>
Freshness
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Jan Carius
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/ovnewton-usage · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Jan Carius or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download using-ovnewton.zip