# 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/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 HF/NGC/S3/Token Factory credentials and gated-model access with `npa workbench health preflight` / `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/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/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/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): annotate → Cosmos Transfer augment → Cosmos Evaluator gate → re-label → Cosmos Curator + FiftyOne curate → Rerun visualize. The evaluator and curator are the real Apache-2.0 NVIDIA projects, wrapped as `npa workbench cosmos-evaluator` and `npa workbench cosmos-curate`; see `skills/NOTICE-NVIDIA-COSMOS-OSS` for which upstream code runs where. - `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 (CAD-to-SimReady, USD tooling, defect-image SDG, SDG infrastructure) is tracked in `docs/architecture/partner-skills-roadmap.md`; those are not yet implemented in the workbench. **NuRec/NRE has landed** (`skills/workflows/neural-reconstruction/SKILL.md`), as has video data augmentation (`skills/workflows/physical-ai-data-factory/SKILL.md`). Add each 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.