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 sim2real-enginegit 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/sim2real-engine)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/sim2real-engine"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/sim2real-engine/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/sim2real-engine"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/sim2real-engine.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 | $0.00039 | $0.00888 |
| Opus 5 | $0.00019 | $0.00444 |
| Sonnet 5 | $0.00008 | $0.00178 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
sim2real-engine 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compositional Sim2Real engine
The canonical engine surface is the ordinary workflow
workflows/main/sim2real.yaml. Its leaf states call
npa.workflows.sim2real.workflow_stage; S3/content-addressed evidence primitives
live in workflow_io. The shared npa_workflow interpreter/runtime owns loops,
parallel waves, SkyPilot jobs, ledger persistence, and resume.
The small engine.py facade and bounded legacy_* modules remain compatibility
surfaces for archived runs. New canonical states must never call
run_preamble, run_inner_loop, run_single_outer_iteration, run_finalize,
or spawn sibling Jobs.
Stage map
| Stage | Canonical record | Adapter boundary |
|---|---|---|
| 1 | stage_01_trigger |
task-aligned trigger/seed validation |
| 2 | stage_02_assets |
task/assets/camera/strict-success contract |
| 3 | stage_03_augment |
real Cosmos Transfer 2.5 |
| 4 | stage_04_envs_raw |
parallel raw EnvGen shards |
| 5 | stage_05_envs_train |
sealed train/validation/gold split |
| 6 | stage_06_tokens |
explicit S3 token/scenario manifest |
| 7 | stage_07_actions_train |
real Isaac multi-camera rollout |
| 8 | stage_08_vlm_eval_train |
single hosted Token Factory CPU evaluator |
| 9 | stage_09_training_signal |
temporal merge, real PPO, validation selection |
| 10 | stage_10_eval_heldout |
exact-checkpoint untouched-gold Isaac eval |
| 11 | stage_11_outer_loop |
strict metric + standard decision artifact |
| 12 | stage_12_external_validation |
designed external SEAM |
| 13 | stage_13_retrigger |
retrigger/loop record |
| 14 | stage_14_rerun_viz |
final report, RRD, and MCAP |
Invariants
- Use named
{{loop.*}}tokens for iteration-scoped S3 paths. - Every real stage publishes
WORKS; Stage 12 alone publishesSEAM. Canonical pointers have immutable content-addressed history. - GPU images are immutable and source-attested. Isaac rollout/train/eval use
NPA_SIM2REAL_INLINE_TASK=1inside the workflow-owned GPU task. - The hosted evaluator uses
MiniMaxAI/MiniMax-M3through Token Factory, receivesNEBIUS_TOKEN_FACTORY_KEYby secret name, requests no GPU, and publishes evaluator tokens/latency/retries/request IDs/cost separately from model-agent accounting. Stage 9 must reject missing, duplicate, or extra evaluations or a mismatched configured model/family by comparing that single result with the authoritative Stage 7 rollout set before PPO or checkpoint selection. - The
cosmos3_modelconfig key,cosmos3.jsonartifact, and evaluator schema/lane names are retained for compatibility; the actual model andreason_familyidentify MiniMax or an explicitly selected legacy Cosmos3 endpoint. Changing the model or upgrading the evaluator contract requires a new run. - Train, validation, and gold digests are disjoint. Checkpoint ranking reads validation only; Stage 10 reads gold only and preserves exact render lineage.
- Temporal rewards remain bounded and simulator-grounded. Strict success remains stable placement within 5 cm. Pipeline success and policy quality are distinct.
- Final RRD/MCAP use configured capture FPS and contain non-empty multi-camera, progress, policy, and evaluation evidence.
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 0c5161e12a90
- 5d ago Changed · +4 lines 8fd138611af4
- 8d ago First seen · 63 lines · 39 tokens per session scan A 88bcde68bb38
sim2real-engine is a skill published in the GitHub repository nebius/nebius-physical-ai (28 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 888 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.
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