physical-ai-infrastructure-setup-and-resilient-scaling

What this skill does

# 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.

| Concern | Load | Assets |
| --- | --- | --- |
| Stage matrix and old driver notes | `components/driver/reference.md` | None |
| MicroK8s cluster | `components/cluster-microk8s/reference.md` | `components/cluster-microk8s/scripts/`, `components/cluster-microk8s/runtimeclass-nvidia-runc.yaml` |
| Azure AKS cluster | `components/cluster-azure/reference.md` | `components/cluster-azure/scripts/`, `components/cluster-azure/terraform/` |
| NIM Operator inference | `components/inference-nim-operator/reference.md` | `components/inference-nim-operator/scripts/`, `components/inference-nim-operator/nims/` |
| NVCF inference | `components/inference-nvcf/reference.md` | `components/inference-nvcf/scripts/` |
| Azure AI Foundry inference | `components/inference-azure/reference.md` | `components/inference-azure/scripts/` |
| MicroK8s OSMO | `components/osmo-k8s/reference.md` | `components/osmo-k8s/scripts/`, upstream OSMO deploy scripts |
| Azure OSMO | `components/osmo-azure/reference.md` | `components/osmo-azure/scripts/`, upstream OSMO deploy scripts plus Azure TF outputs |
| Azure access setup | `components/azure-access/reference.md` | None |
| OSMO CLI and workflow operations | `components/osmo-cli/reference.md` | `components/osmo-cli/scripts/`, `components/osmo-cli/references/`, `components/osmo-cli/agents/`, `components/osmo-cli/tests/` |
| OpenClaw Azure device login | `components/openclaw-azure-login/reference.md` | None |

### 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.

| File | Read when |
| --- | --- |
| `components/osmo-cli/agents/workflow-expert.md` | Spawning a workflow-generation or workflow-failure subagent. |
| `components/osmo-cli/agents/logs-reader.md` | Spawning a log summarization subagent for OSMO workflow failures. |
| `components/osmo-cli/references/cli-commands.md` | Exact OSMO CLI flags, payloads, or command syntax are needed. |
| `components/osmo-cli/references/workflow-spec.md` | Workflow YAML schema, credentials, outputs, or provider fields are needed. |
| `components/osmo-cli/references/workflow-patterns.md` | Multi-task, data dependency, Jinja, serial, or parallel workflow design is needed. |
| `components/osmo-cli/references/advanced-patterns.md` | Checkpointing, retry/exit behavior, or node exclusion is needed. |
| `components/osmo-cli/tests/orchestrator-runtime-failure.md` | Validating or debugging the OSMO orchestration review pattern. |

## 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:

| Cluster | NIM Operator | NVCF | Azure AI Foundry |
| --- | --- | --- | --- |
| MicroK8s | yes | yes | no, Foundry requires Azure identities |
| Azure | yes | yes | yes |

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.

## 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.

## 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:

| Stage | Gate |
| --- | --- |
| Kubernetes | Cluster API reachable, nodes Ready, GPU capacity advertised for GPU paths, and CPU+NVCF paths have `runtimeclass/nvidia` mapped to `runc`. |
| Inference | Every endpoint referenced by the workload is reachable. NIM readiness uses `/v1/health/ready`; NVCF and Foundry still need task-specific authenticated checks. |
| OSMO | OSMO pods Ready, pool ONLINE, port-forward watchdogs alive, storage credentials configured, and verify-hello workflow COMPLETED. |
| Workload | Selected workload pre-submit guards pass before submit. `osmo workflow query <id>` reports `COMPLETED` and every task is green. Failed termi