Physical AI Infrastructure Setup And Resilient Scaling

Canonical skill for the Physical AI infrastructure stack. Use it to compose cluster, inference, OSMO, and workload stages into a reproducible Physical AI SDG environment, then keep the environment observable and recoverable.

Operating Rules

• Read only the component references needed for the selected target. Do not load every component by default.
• Keep the repo as the durable artifact. Fix checked-in config or scripts, then rerun. Do not recover a failed install with untracked one-off changes.
• Run mutating cluster, OSMO, Helm, Terraform, or Azure operations through checked-in scripts when a script exists. Read-only diagnostics are allowed.
• Stop at the first red gate. Fix the lowest owning layer in this order: config, script, then skill guidance.
• Derive values from the environment when possible. Ask only for values that cannot be inferred, such as API keys, target choice, or quota tradeoffs.
• Store secrets in ${REPO_ROOT}/.env. Cluster-derived values such as storage, database, Redis, and endpoint names come from Terraform outputs or platform queries, not .env.
• Preflight means no deployed state: no cluster API, Terraform outputs, Helm releases, OSMO pools, or workflow state. Those belong to deploy/verify gates.
• Never print, echo, or paste raw keys into commands, YAML, logs, or transcripts. Prefer credential handles, Kubernetes secretKeyRef, and runtime-only secret injection. Scan raw transcript exports with scripts/scan_transcript_secrets.py before sharing.
• Use absolute paths. Derive repo root with git rev-parse --show-toplevel.

Component References

Each component lives inside this skill so the stack has one canonical trigger. Load the component reference only when the selected target needs that slice.

OSMO CLI Support Files

The OSMO CLI component has second-level support files because its command and workflow surface is large. Load these directly only for the stated case.

Target Selection

Pick exactly one option per stage. Stage 2 follows stage 1.

1. Kubernetes: MicroK8s or Azure
2. OSMO: MicroK8s OSMO when Kubernetes is MicroK8s, Azure OSMO when Kubernetes is Azure
3. Inference: NIM Operator, NVCF, Azure AI Foundry, or None
4. Workload: Video Data Augmentation, Defect Image Generation, NuRec Carline Adaptation, NRE, NCore, Asset Harvester, or custom workflow YAML

Reject invalid combinations before provisioning:

For OpenClaw or any chat-only environment that cannot open a browser, read components/openclaw-azure-login/reference.md before Azure prerequisites. For any Azure target, read components/azure-access/reference.md before Azure component preflights.

Inference Discovery

Avoid over-deploying expensive endpoints.

1. Scan the chosen workflow spec and default values for endpoint references: *.osmo-nims.svc.cluster.local, api.nvcf.nvidia.com/*, *.inference.ai.azure.com, or *.cognitiveservices.azure.com.
2. Map each reference to the selected backend:
   • NIM Operator: service name must match a directory under components/inference-nim-operator/nims/.
   • NVCF: function URL or function ID must be supplied by the environment.
   • Azure AI Foundry: endpoint name must be deployed through components/inference-azure/scripts/install.sh.
3. If the workflow needs a capability the selected backend lacks, stop and report the mismatch. Do not silently substitute another model.

Verification Gates

Each stage has its own Verify section in the component reference. These gates are mandatory:

Resilient Scaling

• Size the cluster from workload needs before provisioning. For Azure, check CPU and GPU quota for the selected VM families before terraform apply.
• For NIM Operator, deploy only the NIMServices referenced by the workload. Each service pins GPU and model-cache storage for the lifetime of the cluster.
• Keep OSMO storage URL schemes aligned with the active backend. Local MicroK8s uses MinIO, Azure uses Blob-backed configuration.
• Treat Pending, Unknown, ImagePullBackOff, unbound PVCs, or 0 Ready replicas as layer failures. Investigate scheduling, storage, image credentials, and adjacent platform state before retrying the same command.
• For long deploys or workflow watches, provide heartbeat updates with current state, elapsed time, last useful observation, and next check.

Workload Routing

• Video Data Augmentation: use skills/physical-ai-video-data-augmentation/SKILL.md.
• Defect Image Generation: use skills/physical-ai-defect-image-generation/SKILL.md.
• NuRec carline adaptation: use skills/carline-adaptation/SKILL.md.
• NRE, NCore, and Asset Harvester live in the canonical NuRec catalog listed in skills/INDEX.md.
• Custom workload: submit the provided workflow YAML through OSMO after checking resource requests, image credentials, data credentials, and inference URLs.

Evaluation Prompts And Results

• Positive trigger: "Set up resilient Physical AI infrastructure for VDA on Azure AKS with NIM Operator." Expected: use this skill.
• Negative trigger: "Summarize recent OSMO workflow logs for this workflow ID." Expected: do not use this infrastructure setup skill unless the request also involves setup, scaling, validation, or recovery of the infrastructure stack.

Latest static review: 2026-05-26, description keywords match the expected routes above.

npx skills add https://github.com/openai/plugins --skill physical-ai-infrastructure-setup-and-resilient-scaling

Setup Flow

1. Confirm target choices and workload compute requirements.
2. Load the selected component references.
3. Resolve prerequisites up front, including API keys, Azure access, caller CIDR, GPU quota, storage class, and OSMO login requirements.
4. Run scripts/preflight.sh for every selected infrastructure component plus any OSMO CLI/workload preflight before provisioning; build the implementation plan from the results and stop on red preflight.
5. Deploy Kubernetes first. Nothing else starts until the cluster gate is green.
6. Deploy OSMO and inference after Kubernetes. These can proceed in parallel once the cluster exists, but workload submission waits for both selected gates.
7. Submit the workload only after OSMO, storage credentials, compute pool, and selected inference endpoints are verified. For VDA, this includes preflight_credentials.sh, pre_submit_guard.py with resolved --set values, non-empty model-cache prefixes, and workflow-namespace endpoint smoke checks.
8. Monitor through completion. On failed workflow state, inspect events and logs from components/osmo-cli/reference.md; do not resubmit blindly.