git:20261011.7c8a67e to git:20261012.7c2d2fd

# Nebius Physical AI

Nebius Physical AI is the workbench and workflow layer for running robotics, simulation, perception, and synthetic-data workloads on Nebius infrastructure. The repo centers on the `npa` CLI/SDK, containerized workbench tools, and SkyPilot workflows that compose those tools through S3 artifacts. The current product shape is a tool marketplace that customers can adapt without hardcoding project-specific infrastructure.

Claude Code should treat this file as a lightweight index. Scan

`skills/index.yaml` first, then load the relevant root `skills/` entry before

making architecture, review, or domain judgments.

## Skill Index

- `skills/tools/marble/SKILL.md`: hosted Marble acquisition, CUDA camera datasets

and spatial scans, and native workflows with factual interactive HTML reports.

- `skills/index.yaml`: root manifest with name, when-to-use, path, and CI smoke

expectations.

- `skills/atomic/architecture/SKILL.md`: platform architecture and validation

state.

- `skills/workflows/first-run-setup/SKILL.md`: zero to a first verified result on

a fresh machine or new project — an ordered, gated path (install → configure →

credential preflight → cheapest-proof workload → validate spec → provision →

image pullability → submit), with a stop condition at every step.

- `skills/atomic/gpu-first-guidance/SKILL.md`: recommending a path, writing

user-facing docs, or answering "what should I run first?" — lead with the

Nebius GPU path, describe hosted inference by what it does, and stay warm and

genuinely helpful when someone asks about cost or has no cluster yet.

- `skills/atomic/health-preflight/SKILL.md`: there is no `npa doctor`. Prove

service credentials with `npa workbench health preflight`, request

`--checks nebius` before provisioning, and verify gated-model access with

`npa workbench health access` before spending GPU time.

- `skills/atomic/debug-failed-run/SKILL.md`: triage a run that failed, hung, or

produced no artifacts — status and pod-level reason, stage logs, S3 evidence,

image pullability, scheduling, and the resume-vs-cancel decision.

- `skills/atomic/teardown-and-cost/SKILL.md`: stop spend safely — the mandatory

cancel-before-destroy ordering, cloud versus local state, and the orphan audit

for leaked clusters, agent VMs, controllers, buckets, and cross-project fleets.

- `skills/workflows/add-workbench-tool/SKILL.md`: add a new workbench tool end to

end — implementation, CLI, SDK, toolRef catalog, container, tests, docs, and

skill, in the order that keeps every CI gate green.

- `skills/atomic/npa-cli-conventions/SKILL.md`: write or modify an npa CLI

command or SDK function — registration, the `--input-path`/`--output-path`

contract, output format, error handling and exit codes, the decorators that are

easy to forget, and config/credential access.

- `skills/atomic/toolref-argv-contract/SKILL.md`: the contract that stops a

workflow stage from rendering cleanly and then crashing in the pod.

- `skills/atomic/pre-pr-validation/SKILL.md`: which of the six PR gates apply to

a change, and the exact local command for each, cheapest first.

- `skills/atomic/audit-container-docs/SKILL.md`: required catalog reconciliation

when a workbench image or solution is added, removed, retagged, reclassified,

published, or otherwise changed; it keeps the public-image table aligned with

repository intent and anonymous GHCR state.

- `skills/atomic/guardrail-failures/SKILL.md`: map a failing guardrail or CI gate

to its cause and fix.

- `skills/atomic/agent-development/SKILL.md`: build, enhance, or test the NPA

chat agent backend (grounded-first routing, cost-aware Token Factory model

selection, embedded-backend mechanism, cheap-token test tiers).

- `skills/atomic/agent-visual-feedback/SKILL.md`: Describe-this viewer feedback

for Rerun / video / image / data panes (multimodal vision tier).

- `skills/atomic/review-checklist/SKILL.md`: review risk classification.

- `skills/atomic/physical-ai-context/SKILL.md`: robotics, sim-to-real,

GPU-routing, Genesis, Isaac Lab, LeRobot, SONIC, GR00T, Cosmos, or BDD100K

context.

- `skills/tools/scenario-gen/SKILL.md`: adversarial scenario generation — an RL

adversary that maximizes failures of a policy-under-test, scenario ranking, and

the adversarial-scenario-hardening workflow.

- `skills/tools/dataset/SKILL.md`: dataset-of-record — ingest, validate, curate,

and query production sensor data as a versioned, lineage-tracked dataset

(FiftyOne curation + LanceDB query index).

- `skills/tools/encord/SKILL.md`: stateless Encord SaaS register, pull, and exact

roundtrip verification with S3-backed lineage artifacts.

- `skills/tools/foxglove/SKILL.md`: Foxglove embedded viewer — the

`@foxglove/embed` TypeScript SDK in the agent UI, MCAP recordings

(convert/inspect/publish), and the `npa-foxglove-embed` container.

- `skills/tools/insights/SKILL.md`: lineage graph + common metrics store over

workflow-run artifacts — non-invasive ingest-run, query, compare, lineage

traversal, and dashboard (CPU-only, append-only S3 JSONL, LanceDB-optional).

- `skills/tools/leisaac/SKILL.md`: LeIsaac browser teleoperation, secure

agent-relay transport, immutable LeRobot datasets, custom bundles, and PAIDF

interoperability.

- `skills/tools/fleet/SKILL.md`: deploy a fleet of Nebius Managed Kubernetes

(k8s-training) clusters across one or many projects in a tenant from an

`npa.fleet/v0.0.1` spec — identical and/or custom clusters, create-on-demand

projects, and a k8s-training source that can consume the latest upstream recipe.

- `skills/tools/token-factory/SKILL.md`: zero-GPU hosted inference (captioning,

batch generation, Cosmos physical-AI reasoning) — the cheapest tier that

produces a real artifact, with no cluster and no provisioning.

- `skills/tools/vlm-eval/SKILL.md`: score rollouts with a VLM and turn the score

into a gate — `run` vs `loop`, rubric/threshold benchmark sweeps, backend

selection, and judging against a plan an earlier stage wrote.

- `skills/tools/golden-eval/SKILL.md`: prove a container image actually works —

per-container hello-world manifest, dry-run/local/serverless tiers, batch runs,

and the offline manifest validation that gates CI.

- `skills/tools/cosmos3-ray-serve/SKILL.md`: deploy and operate persistent

Cosmos3-Nano generation through NVIDIA Cosmos Framework's native dynamically

batched Ray Serve path, with guarded runtime weight fetch and S3 provenance.

- `skills/tools/burst/SKILL.md`: one gang-scheduled multi-node GPU job with

torchrun rendezvous, deliberately not a workflow surface.

- `skills/tools/gpu-cluster-provisioning/SKILL.md`: managed-image vs GPU-Operator

driver strategy (operator mode is unsafe on NVSwitch), the post-apply health

gates, accelerator-name discovery, and triage for nodes whose GPUs do not work.

- `skills/tools/open3d/SKILL.md`: point-cloud registration and surface

reconstruction — real RANSAC/FPFH + ICP, multiway `global_optimization`,

Poisson reconstruction, and a decode-verified `.rrd`. CPU-only by

construction (Open3D's registration APIs have no CUDA path).

- `skills/tools/embodiedgen/SKILL.md`: operator-private EmbodiedGen V2 TRELLIS

image-to-rigid-object generation, generated URDF/collision checks, and

factual PyBullet evidence.

- `skills/tools/detection-training/SKILL.md`: Faster R-CNN detectors trained from

LanceDB materialized views (BDD100K failure-mode slices).

- `skills/tools/artifact-viz-share/SKILL.md`: sim demos → LeRobotDataset →

`.rrd`/MP4, and time-boxed presigned Rerun share links.

- `skills/tools/mjlab/SKILL.md`: MJLab locomotion evaluation and SONIC checkpoint

scoring.

- `skills/tools/retargeting/SKILL.md`: motion retargeting in SONIC locomotion

workflows.

- `skills/workflows/sim-to-real/SKILL.md`: generic sim-to-real workflow

planning.

- `skills/workflows/sim2real-operate/SKILL.md`: run, monitor, and debug the

compositional Sim2Real `npa.workflow` through the standard SkyPilot runtime

(validate/plan/submit, durable S3 resume, health checks, job monitoring).

- `skills/workflows/agent-fresh-operate/SKILL.md`: npa-driven agent teardown,

fresh-setup, tiered verify gates, and deploy failure recovery on the

operator/dev VM.

- `skills/workflows/author-npa-workflow/SKILL.md`: author and validate

declarative `npa.workflow/v0.0.1` specs (toolRef catalog, validate/plan/run CLI).

- `skills/workflows/antioch-workbench/SKILL.md`: Antioch CLI authentication,

XR1 collection and evaluation, signed Nebius S3 transfers, and fine-tuning.

- `skills/workflows/generate-npa-workflow/SKILL.md`: design new creative

npa.workflow pipelines from the workbench tool catalog.

- `skills/workflows/diagram-to-npa-workflow/SKILL.md`: turn an architecture

diagram + step write-up into a working npa.workflow/v0.0.1 YAML (boxes, arrows,

decision diamonds, and loop back-edges → states, loops, gates, catalog

toolRefs); generalizes across sim2real, AV, RL, and Cosmos pipelines.

- `skills/workflows/physical-ai-data-factory/SKILL.md`: author, run, submit, or

view the NVIDIA Physical AI Data Factory blueprint on Nebius + SkyPilot (no

OSMO/Airflow): annotate → Cosmos Transfer augment → Cosmos Evaluator gate →

re-label → Cosmos Curator + FiftyOne curate → Rerun visualize. The official

ecosystem and scaled-orchestration relationship is recorded in

`skills/NOTICE-NVIDIA-PAIDF`; executable evaluator/curator boundaries are in

`skills/NOTICE-NVIDIA-COSMOS-OSS`.

- `skills/workflows/nvidia-osmo-to-npa-workflow/SKILL.md`: translate pinned

NVIDIA OSMO workflow definitions, or related authoritative Airflow DAGs, into

semantic `npa.workflow/v0.0.1` graphs while preserving real components, typed

handoffs, retry/failure behavior, evidence, licensing, and attribution without

claiming control-plane equivalence.

- `skills/workflows/neural-reconstruction/SKILL.md`: NuRec/NRE neural

reconstruction on Nebius — NCore V4 capture (including deriving the

`rig → world` pose edge NRE requires) → 3DGUT Gaussian training → renderable

USDZ → rig-offset novel views → `reports/sim2real.rrd`. RT-core GPU only

(L40S / RTX PRO 6000, never H100/H200).

- `skills/workflows/onboard-world-model/SKILL.md`: generic playbook for

onboarding and containerizing a world model (learned action-conditioned

simulator) as a multi-GPU BYOF registry candidate — containerize, stage a real

dataset, encode the train→tokenize→dynamics→dream→visualize loop as capability

smokes, and validate on real GPUs (Open Dreamer is the reference example).

- `skills/atomic/real-components/SKILL.md`: ensure every advertised workbench

pipeline stage invokes the real component (Cosmos Transfer, FiftyOne, VLM),

not an echo/manifest stub masquerading as real work.

- `skills/atomic/solution-licensing/SKILL.md`: when adding a solution, tool,

image, model, or dataset — classify what the artifact actually bakes, decide

whether it may be redistributed (`public` vs `restricted`), and record it in

the packaging contract where the guards enforce it. Verify the claim against the

BUILT image with `npa/scripts/scan_image_omniverse_payload.py`, not by reading

the Dockerfile: the Isaac images were cleared that way, and two of the three

problems it found were invisible in the Dockerfile.

- `skills/workbench/sim2real-engine/SKILL.md`: canonical 14-stage Sim2Real graph,

stateless stage adapters, parallel lane joins, ComponentRecords, and durable

standard-runtime resume. The preamble/inner/outer/finalize controller is

finite legacy compatibility, never the canonical execution path.

Compatibility symlinks exist at `.claude/skills` and `.agents/skills`; do not

create a new split skill tree.

### Partner Capability Roadmap

Onboarding NVIDIA Physical AI / Omniverse capabilities is tracked in `docs/architecture/partner-skills-roadmap.md`. **NuRec/NRE and video data augmentation have landed**, and the PAIDF skill now covers native DIG Day-1 manual-ROI, IAA, and EVG specs. Their Workbench guide distinguishes implementation from completed live acceptance. Remaining DIG Day-0/PCBA alignment, CAD-to-SimReady, USD tooling, and SDG infrastructure capabilities retain their documented roadmap scope. Add a remaining capability as a real skill only when its solution lands on Nebius + SkyPilot, with tests.

## Project Instructions

- Do not hardcode project IDs, tenant IDs, registry IDs, bucket names, or secrets. Credentials live in `~/.npa/credentials.yaml`; machine-managed config lives in `~/.npa/config.yaml`.

- Unit tests must not touch real infrastructure. Mock SSH, S3, Nebius APIs, GPUs, and network calls at the call site.

- Do not import GPU-heavy packages such as `torch`, `genesis`, or `lerobot` at module level in unit tests; use targeted imports or `pytest.importorskip()`.

- CLI tests use `typer.testing.CliRunner` against `npa.cli.main:app`.

- The repo's current operational context is the workbench architecture, not the older LeRobot-only VM research-script flow.