Environment Deployment
microsoft/physical-ai-toolchain
Generate, transfer, and consume environment-specific Azure, AKS, OSMO, ACR, and Azure ML deployment bundles.
Official agent skill
Physical AI Infrastructure Setup And Resilient Scaling
by NVIDIA in NVIDIA/skills
A skill your agent uses when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS…
Install Physical AI Infrastructure Setup And Resilient Scaling
$ npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills physical-ai-infrastructure-setup-and-resilient-scaling --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/physical-ai-infrastructure-setup-and-resilient-scaling .claude/skills/physical-ai-infrastructure-setup-and-resilient-scaling && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "physical-ai-infrastructure-setup-and-resilient-scaling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scaling into .claude/skills/physical-ai-infrastructure-setup-and-resilient-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physical-ai-infrastructure-setup-and-resilient-scaling", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scalingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills physical-ai-infrastructure-setup-and-resilient-scaling --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/physical-ai-infrastructure-setup-and-resilient-scaling .agents/skills/physical-ai-infrastructure-setup-and-resilient-scaling && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "physical-ai-infrastructure-setup-and-resilient-scaling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scaling into .agents/skills/physical-ai-infrastructure-setup-and-resilient-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physical-ai-infrastructure-setup-and-resilient-scaling", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills physical-ai-infrastructure-setup-and-resilient-scaling --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/physical-ai-infrastructure-setup-and-resilient-scaling .cursor/skills/physical-ai-infrastructure-setup-and-resilient-scaling && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "physical-ai-infrastructure-setup-and-resilient-scaling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scaling into .cursor/skills/physical-ai-infrastructure-setup-and-resilient-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physical-ai-infrastructure-setup-and-resilient-scaling", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/physical-ai-infrastructure-setup-and-resilient-scaling--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills physical-ai-infrastructure-setup-and-resilient-scaling --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/physical-ai-infrastructure-setup-and-resilient-scaling .gemini/skills/physical-ai-infrastructure-setup-and-resilient-scaling && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "physical-ai-infrastructure-setup-and-resilient-scaling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scaling into .gemini/skills/physical-ai-infrastructure-setup-and-resilient-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physical-ai-infrastructure-setup-and-resilient-scaling", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills physical-ai-infrastructure-setup-and-resilient-scalingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/physical-ai-infrastructure-setup-and-resilient-scaling .github/skills/physical-ai-infrastructure-setup-and-resilient-scaling && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "physical-ai-infrastructure-setup-and-resilient-scaling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scaling into .github/skills/physical-ai-infrastructure-setup-and-resilient-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physical-ai-infrastructure-setup-and-resilient-scaling", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills physical-ai-infrastructure-setup-and-resilient-scaling --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/physical-ai-infrastructure-setup-and-resilient-scaling .opencode/skills/physical-ai-infrastructure-setup-and-resilient-scaling && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "physical-ai-infrastructure-setup-and-resilient-scaling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/physical-ai-infrastructure-setup-and-resilient-scaling into .opencode/skills/physical-ai-infrastructure-setup-and-resilient-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physical-ai-infrastructure-setup-and-resilient-scaling", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
physical-ai-infrastructure-setup-and-resilient-scaling- GitHub stars
- 3.6k
- Token cost
- ~2.8k tokens
- SKILL.md length
- 1,122 words
- Files
- 99
- Skills in repo
- 390
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS…
- Works in 4 steps: Kubernetes: MicroK8s or Azure → OSMO: MicroK8s OSMO when Kubernetes is… → Inference: NIM Operator, NVCF, Azure AI… → …
- The user wants to set up
- SKILL.md covers Operating Rules, Component References, Target Selection and Setup Flow, plus 5 more sections
- Runs Shell scripts from its folder; calls git and terraform
What it does
Physical AI Infrastructure Setup And Resilient Scaling is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log…
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 103 other files (for example `BENCHMARK.md`, `components/azure-access/reference.md` and `components/cluster-azure/reference.md`). Compatibility notes: Requires the selected component prerequisites, usually kubectl plus either MicroK8s or Azure CLI/Terraform, and OSMO or inference credentials for the chosen…
It sits in DevOps & Cloud, covering Container orchestration, Deployment and Test data and fixtures. It works with NVIDIA AI Platform, Azure Kubernetes Service, Kubernetes and Terraform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- The user wants to set up
- Harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s
- Including Kubernetes clusters
- Inference endpoint deployment
Example prompts
- “/physical-ai-infrastructure-setup-and-resilient-scaling”
Requirements
- A Bash shell
- Compatibility (from SKILL.md): Requires the selected component prerequisites, usually kubectl plus either MicroK8s or Azure CLI/Terraform, and OSMO or inference credentials for the chosen target.
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Kubernetes: MicroK8s or Azure
- OSMO: MicroK8s OSMO when Kubernetes is MicroK8s, Azure OSMO when
- Inference: NIM Operator, NVCF, Azure AI Foundry, or None
- Workload: Video Data Augmentation, Defect Image Generation, NuRec Carline
What it can do on your machine
Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Shell, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
gitterraform
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Requires the selected component prerequisites, usually kubectl plus either MicroK8s or Azure CLI/Terraform, and OSMO or inference credentials for the chosen target.
From compatibility in the SKILL.md frontmatter.
Context cost
Physical AI Infrastructure Setup And Resilient Scaling loads about 2.8k tokens when it runs. Until then it costs about 172 tokens; SKILL.md has 1,122 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NoteMentions a .env fileSKILL.md:58
- Store secrets in `${REPO_ROOT}/.env`. Cluster-derived values such as storage, - NoteMentions a .env fileSKILL.md:60
queries, not `.env`.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,122 words, ~2,780 tokens.
Download SKILL.mdSave it as
.claude/skills/physical-ai-infrastructure-setup-and-resilient-scaling/SKILL.md (or your agent's skills folder). This skill also uses 98 other files; get the full folder from GitHub.- name
- physical-ai-infrastructure-setup-and-resilient-scaling
- description
- Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.
- compatibility
- Requires the selected component prerequisites, usually kubectl plus either MicroK8s or Azure CLI/Terraform, and OSMO or inference credentials for the chosen target.
- license
- Apache-2.0
- version
- 1.0.0
- tools
- Read, Shell
- metadata.author
- NVIDIA Physical AI
- metadata.tags
- physical-ai, infrastructure, kubernetes, azure, microk8s, osmo, nim-operator, scaling
- metadata.domain
- ai-ml
- metadata.languages
- bash, hcl, yaml
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 withscripts/scan_transcript_secrets.pybefore 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.
- Kubernetes:
MicroK8sorAzure - OSMO:
MicroK8s OSMOwhen Kubernetes is MicroK8s,Azure OSMOwhen Kubernetes is Azure - Inference:
NIM Operator,NVCF,Azure AI Foundry, orNone - 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
- Confirm target choices and workload compute requirements.
- Load the selected component references.
- Resolve prerequisites up front, including API keys, Azure access, caller CIDR, GPU quota, storage class, and OSMO login requirements.
- Run
scripts/preflight.shfor every selected infrastructure component plus any OSMO CLI/workload preflight before provisioning; build the implementation plan from the results and stop on red preflight. - Deploy Kubernetes first. Nothing else starts until the cluster gate is green.
- Deploy OSMO and inference after Kubernetes. These can proceed in parallel once the cluster exists, but workload submission waits for both selected gates.
- 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.pywith resolved--setvalues, non-empty model-cache prefixes, and workflow-namespace endpoint smoke checks. - Monitor through completion. On failed workflow state, inspect events and logs
from
components/osmo-cli/reference.md; do not resubmit blindly.
Show full SKILL.md (422 more words)Show less
Inference Discovery
Avoid over-deploying expensive endpoints.
- 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. - 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.
- NIM Operator: service name must match a directory under
- 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 terminal states require events and logs before retry. |
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.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 98 other files in skills/physical-ai-infrastructure-setup-and-resilient-scaling of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- components/azure-access/reference.md
- components/cluster-azure/.gitignore
- components/cluster-azure/reference.md
- components/cluster-azure/scripts/helmfile.yaml
- components/cluster-azure/scripts/main.tf
- components/cluster-azure/scripts/outputs.tf
- components/cluster-azure/scripts/preflight.sh
- components/cluster-azure/scripts/setup.sh
- components/cluster-azure/scripts/storage-class-nfs.yaml
- components/cluster-azure/scripts/system_node_capacity_test.sh
- components/cluster-azure/scripts/terraform.tfvars.example
- components/cluster-azure/scripts/variables.tf
- components/cluster-azure/scripts/versions.tf
- components/cluster-azure/terraform/main.tf
- … and 83 more
Open the folder on GitHubat commit 14a98ae
Compare with similar skills
Physical AI Infrastructure Setup And Resilient Scaling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Physical AI Infrastructure Setup And Resilient Scaling this skillNVIDIA/skills | 3.6k | — | ~2.8k | Automated safety check: Notes | Apache-2.0 | |
| Environment Deploymentmicrosoft/physical-ai-toolchain | 126 | — | ~5.9k | Automated safety check: Pass | MIT | |
| Aks Deployment Skilltimothywarner/chatgptclass | 143 | — | ~916 | Automated safety check: Pass | Custom licence | |
| Deploy Controllerai-runway/airunway | 102 | — | ~927 | Automated safety check: Pass | Apache-2.0 | |
| KubernetesEliasOulkadi/shokunin | 114 | — | ~3.3k | Automated safety check: Notes | MIT | |
| Supercheck Infrastructure Deploymentsupercheck-io/supercheck | 215 | — | ~1.4k | Automated safety check: Notes | AGPL-3.0 |
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NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
- Official
Isaac Sim Mission Control Showcase
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
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Defect Image Generation with Cosmos AnomalyGen
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
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Physical AI Video Augmentation on OSMO
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
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TAO Detection KPI Analysis
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
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Questions about Physical AI Infrastructure Setup And Resilient Scaling
What does Physical AI Infrastructure Setup And Resilient Scaling do?
A skill your agent uses when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS…. Physical AI Infrastructure Setup And Resilient Scaling is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery.
When should I use Physical AI Infrastructure Setup And Resilient Scaling?
Physical AI Infrastructure Setup And Resilient Scaling fits situations like: the user wants to set up; harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s; including Kubernetes clusters; inference endpoint deployment.
How do I install Physical AI Infrastructure Setup And Resilient Scaling in Claude Code?
Run `npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a claude-code`. Or copy the skill folder (skills/physical-ai-infrastructure-setup-and-resilient-scaling in NVIDIA/skills) into .claude/skills/physical-ai-infrastructure-setup-and-resilient-scaling in your project. Claude Code loads it when a task matches its description.
How do I install Physical AI Infrastructure Setup And Resilient Scaling in Codex?
Run `npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a codex`. Or copy the skill folder (skills/physical-ai-infrastructure-setup-and-resilient-scaling in NVIDIA/skills) into .agents/skills/physical-ai-infrastructure-setup-and-resilient-scaling in your project. Codex loads it when a task matches its description.
Can I use Physical AI Infrastructure Setup And Resilient Scaling in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill physical-ai-infrastructure-setup-and-resilient-scaling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/physical-ai-infrastructure-setup-and-resilient-scaling, .gemini/skills/physical-ai-infrastructure-setup-and-resilient-scaling, .github/skills/physical-ai-infrastructure-setup-and-resilient-scaling and .opencode/skills/physical-ai-infrastructure-setup-and-resilient-scaling in your project.
What does Physical AI Infrastructure Setup And Resilient Scaling need to run?
Going by SKILL.md and its folder, Physical AI Infrastructure Setup And Resilient Scaling needs a shell for the scripts in its folder and the command-line tools its instructions call (git and terraform). Our summary lists: A Bash shell. Compatibility (from SKILL.md): Requires the selected component prerequisites, usually kubectl plus either MicroK8s or Azure CLI/Terraform, and OSMO or inference credentials for the chosen target..
Does Physical AI Infrastructure Setup And Resilient Scaling access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Physical AI Infrastructure Setup And Resilient Scaling safe to install?
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Physical AI Infrastructure Setup And Resilient Scaling use?
Physical AI Infrastructure Setup And Resilient Scaling is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Physical AI Infrastructure Setup And Resilient Scaling use?
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Physical AI Infrastructure Setup And Resilient Scaling?
Skills that share tags, products or a category with Physical AI Infrastructure Setup And Resilient Scaling: Environment Deployment (microsoft/physical-ai-toolchain, 126 stars), Aks Deployment Skill (timothywarner/chatgptclass, 143 stars), Deploy Controller (ai-runway/airunway, 102 stars) and Kubernetes (EliasOulkadi/shokunin, 114 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Physical AI Infrastructure Setup And Resilient Scaling?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.