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 nebius/nebius-physical-ai --skill sim-to-realgit clone --depth 1 https://github.com/nebius/nebius-physical-aiWrote 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/nebius/nebius-physical-ai/sim-to-real)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/sim-to-real"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/sim-to-real/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/nebius/nebius-physical-ai/sim-to-real"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/sim-to-real.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.00572 |
| Opus 5 | $0.00017 | $0.00286 |
| Sonnet 5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
sim-to-real 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 4d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sim-To-Real
When To Use
Use this skill for robotics teams that need configurable data and artifact flow through simulation, policy training, synthetic data generation, evaluation, and iteration without customer-specific names or infrastructure baked into source.
Procedure
- Define a run ID and run-scoped S3 prefixes before launching.
- Import source robot, scene, or task data into the run prefix.
- Generate or augment simulation data with configured workbench tools.
- Train or fine-tune the policy against the run-scoped dataset and write checkpoints to S3.
- Evaluate the policy with deterministic metrics or a configured VLM backend.
- Decide whether to stop, continue, or route artifacts for review based on configurable thresholds.
Three-Tier Contract
- CLI: use
npa workbench workflow,npa workbench trigger, and tool commands such as Genesis, LeRobot, SONIC, MJLab, Retargeting, LanceDB, Cosmos, and VLM-eval. - SDK: keep workflow submission and config materialization in shared helpers so notebooks and services use the same artifact paths.
- Workflows:
workflows/testing/vlm-eval-loop.yamlis the executable reference for the VLM-eval gating loop (scores a whole rollout set and writestask_success_report.json). The staged engine's single maintained end-to-end YAML isworkflows/main/sim2real.yaml; similarly named npa.workflow files are explicitly demo-only DSL fixtures.sim-to-real-pipeline.yamlandsim-to-real-trigger.yamlare retired — the first rannpa.workflows.sim_to_real real-loop, which raises a DeprecationWarning pointing here.
Gotchas
- Do not hardcode customer names, event names, personal names, tenant IDs, registry IDs, bucket names, VM IPs, or private endpoints.
- Prefer dry-run plans for data movement, autoscaling, and external service calls when validating workflow shape.
- Keep artifacts partitioned by run ID so repeated experiments do not overwrite each other.
- Use existing workbench tools for S3 sync, model inference, training, and VLM evaluation instead of one-off scripts.
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.
- 4d ago Changed 470c8592e77c
- 9d ago First seen · 59 lines · 34 tokens per session scan A 108387c9a48a
sim-to-real is a skill published in the GitHub repository nebius/nebius-physical-ai (29 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 572 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 2026-09-03.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
minicpm5-deploy-vllm-ascend
Deploy MiniCPM5-2B with vLLM on Huawei Ascend NPU using vLLM-Ascend. Use when the user mentions vLLM-Ascend, Ascend NPU, Huawei Ascend, CANN, torchnpu, davinci devices, or wants an OpenAI-compatible MiniCPM5 server on Ascend hardware.
minicpm5-deploy-litert
Run MiniCPM5-2B or MiniCPM5-1B on-device with Google's LiteRT-LM runtime — the litert-lm CLI or its OpenAI-compatible server on a desktop, the Kotlin API or the AI Edge Gallery app on Android, the same .litertlm bundle on CPU or GPU. Use when the user says "LiteRT", "LiteRT-LM", "litertlm", ".litertlm", "Android"…