Sim Helm
simstudioai/sim
Install, upgrade, and operate the Sim Helm chart on Kubernetes.
Install NVIDIA NIM Operator on Kubernetes with prerequisite checks, optional NVIDIA GPU Operator dependency installation, public or local Helm chart selection, optional Dynamo support, and optional…
$ npx skills add NVIDIA/k8s-nim-operator --skill nim-operator-install -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/k8s-nim-operator nim-operator-install --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/k8s-nim-operator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/nim-operator-install .claude/skills/nim-operator-install && 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 "nim-operator-install" agent skill from https://github.com/NVIDIA/k8s-nim-operator/tree/main/.agents/skills/nim-operator-install into .claude/skills/nim-operator-install/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nim-operator-install", 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/k8s-nim-operator/tree/main/.agents/skills/nim-operator-installType 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/k8s-nim-operator --skill nim-operator-install -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/k8s-nim-operator nim-operator-install --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/k8s-nim-operator.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/nim-operator-install .agents/skills/nim-operator-install && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "nim-operator-install" agent skill from https://github.com/NVIDIA/k8s-nim-operator/tree/main/.agents/skills/nim-operator-install into .agents/skills/nim-operator-install/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nim-operator-install", 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/k8s-nim-operator --skill nim-operator-install -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/k8s-nim-operator nim-operator-install --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/k8s-nim-operator.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/nim-operator-install .cursor/skills/nim-operator-install && 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 "nim-operator-install" agent skill from https://github.com/NVIDIA/k8s-nim-operator/tree/main/.agents/skills/nim-operator-install into .cursor/skills/nim-operator-install/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nim-operator-install", 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/k8s-nim-operator.git --path .agents/skills/nim-operator-install--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/k8s-nim-operator --skill nim-operator-install -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/k8s-nim-operator nim-operator-install --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/k8s-nim-operator.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/nim-operator-install .gemini/skills/nim-operator-install && 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 "nim-operator-install" agent skill from https://github.com/NVIDIA/k8s-nim-operator/tree/main/.agents/skills/nim-operator-install into .gemini/skills/nim-operator-install/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nim-operator-install", 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/k8s-nim-operator nim-operator-installInstalls 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/k8s-nim-operator --skill nim-operator-install -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/k8s-nim-operator.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/nim-operator-install .github/skills/nim-operator-install && 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 "nim-operator-install" agent skill from https://github.com/NVIDIA/k8s-nim-operator/tree/main/.agents/skills/nim-operator-install into .github/skills/nim-operator-install/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nim-operator-install", 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/k8s-nim-operator --skill nim-operator-install -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/k8s-nim-operator nim-operator-install --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/k8s-nim-operator.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/nim-operator-install .opencode/skills/nim-operator-install && 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 "nim-operator-install" agent skill from https://github.com/NVIDIA/k8s-nim-operator/tree/main/.agents/skills/nim-operator-install into .opencode/skills/nim-operator-install/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nim-operator-install", 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.
nim-operator-installInstall NVIDIA NIM Operator on Kubernetes with prerequisite checks, optional NVIDIA GPU Operator dependency installation, public or local Helm chart selection, optional Dynamo support, and optional…
Nim Operator Install is an agent skill from NVIDIA/k8s-nim-operator, published by the product's own GitHub organization. Install NVIDIA NIM Operator on Kubernetes with prerequisite checks, optional NVIDIA GPU Operator dependency installation, public or local Helm chart selection, optional Dynamo support, and optional KServe compatibility verification. Use when a customer wants to install or upgrade the NIM Operator itself, with or without Dynamo and KServe, but does not want to deploy a NIM inference model yet.
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/validation.md` and `scripts/validate-nim-operator-install.sh`).
It sits in DevOps & Cloud, covering Container orchestration. It works with NVIDIA AI Platform, Kubernetes and Helm. The repository describes itself as: An Operator for deployment and maintenance of NVIDIA NIMs and NeMo microservices in a Kubernetes environment. The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 315bf38. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
helmkubectlsshFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
helm.ngc.nvidia.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Nim Operator Install loads about 4.7k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 1,546 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.
The automated check found no risky patterns in SKILL.md.
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); the scripts in this folder are not scanned.
The full file from NVIDIA/k8s-nim-operator at commit 315bf38, republished under its Apache-2.0 licence (© NVIDIA). 1,546 words, ~4,718 tokens.
.claude/skills/nim-operator-install/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Use this skill to install or upgrade the NVIDIA NIM Operator on an existing Kubernetes cluster. The skill installs the operator and its CRDs only. Do not deploy NIMService, NIMCache, NIMPipeline, or NeMo service custom resources unless the user explicitly asks for that as a separate task.
This is the canonical, agent-neutral skill folder. If another agent framework needs a specific discovery path, create a thin adapter or symlink to this folder instead of duplicating the workflow.
Assume commands run from the root of the k8s-nim-operator repository unless the user gives another working directory. Before using repo-relative paths such as .agents/skills/... or deployments/helm/k8s-nim-operator, verify the current directory:
pwd
test -f deployments/helm/k8s-nim-operator/Chart.yaml
test -f .agents/skills/nim-operator-install/SKILL.mdIf these checks fail, ask for the correct repository root or cd to it before continuing.
Run read-only discovery before proposing any cluster-changing command. Before running any mutating command, print the exact commands, summarize the expected impact, and ask for confirmation.
Read-only examples: kubectl get, kubectl describe, kubectl auth can-i, helm version, helm list, helm search repo, helm status, helm get values.
Mutating examples: helm repo add, helm repo update, helm dependency update, helm upgrade --install, kubectl create, kubectl apply, kubectl patch, kubectl delete.
nim-operatornim-operatornvidiahttps://helm.ngc.nvidia.com/nvidiadeployments/helm/k8s-nim-operatorgpu-operatorreferences/validation.md..agents/skills/nim-operator-install/scripts/validate-nim-operator-install.sh.End users do not need to know the internal file layout. They should ask the agent for the outcome they want. Recognize and support these prompt patterns:
Dry run only:
Use the NIM Operator install skill to dry-run installation on my Kubernetes cluster. Show preflight checks, available chart versions, selected version, and Helm dry-run output. Do not install anything.Install latest public chart:
Use the NIM Operator install skill to install NIM Operator from the public NVIDIA Helm repo. Check prerequisites first, ask me which chart version to use, and do not run mutating commands until I approve.Install a specific version:
Use the NIM Operator install skill to install NIM Operator version <version>. Verify that version exists in the NVIDIA Helm repo before installing.Install from the local chart:
Use the NIM Operator install skill to install from the local chart in this repo. Show me the local chart version and ask before installing.Install with Dynamo:
Use the NIM Operator install skill to install NIM Operator with Dynamo enabled. Ask before enabling any Dynamo sub-options.Validate an existing install:
Use the NIM Operator install skill to validate the current NIM Operator installation. Run only read-only checks and summarize release, pods, CRDs, GPU Operator, cert-manager, and KServe status.Upgrade:
Use the NIM Operator install skill to upgrade my existing NIM Operator release. Show the current version, available versions, selected target version, preserved Helm values, and ask before upgrading.Remote cluster through SSH:
Use the NIM Operator install skill against my remote Kubernetes host <user>@<host>. Run commands over SSH, show every command before running it, and do not install until I approve.This section is for humans, CI jobs, and reviewers who want to run the same workflow without an agent. Run local commands from the repository root and ensure kubectl points at the target cluster before running any Helm command.
Validation only:
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shValidation with overrides:
NIM_OPERATOR_RELEASE=nim-operator \
NIM_OPERATOR_NAMESPACE=nim-operator \
GPU_OPERATOR_NAMESPACE=gpu-operator \
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shPublic chart install or upgrade:
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm search repo nvidia/k8s-nim-operator --versions
selected_version="REPLACE_WITH_VERSION_FROM_SEARCH_OUTPUT"
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.sh
helm upgrade --install nim-operator nvidia/k8s-nim-operator \
--namespace nim-operator \
--create-namespace \
--version "${selected_version}" \
--set operator.admissionController.enabled=false
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shTo dry-run instead of installing, add --dry-run --debug to the helm upgrade --install command. To enable Dynamo, append --set dynamo.enabled=true and only add Dynamo sub-options if they are intentionally selected.
Local chart install or upgrade:
helm show chart deployments/helm/k8s-nim-operator
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.sh
helm upgrade --install nim-operator deployments/helm/k8s-nim-operator \
--namespace nim-operator \
--create-namespace
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shRemote SSH usage if the skill folder has been copied to the remote host:
ssh <user>@<host> '~/.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.sh'
ssh <user>@<host> 'helm repo add nvidia https://helm.ngc.nvidia.com/nvidia'
ssh <user>@<host> 'helm repo update'
ssh <user>@<host> 'helm search repo nvidia/k8s-nim-operator --versions'
ssh <user>@<host> 'selected_version="REPLACE_WITH_VERSION_FROM_SEARCH_OUTPUT"; helm upgrade --install nim-operator nvidia/k8s-nim-operator --namespace nim-operator --create-namespace --version "${selected_version}" --set operator.admissionController.enabled=false'
ssh <user>@<host> '~/.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.sh'Ask only for missing choices that materially affect the install:
If the user wants a quick default install, use public repo, latest available chart version, namespace nim-operator, release nim-operator, Dynamo disabled, KServe verification disabled, and GPU Operator install only if the user approves after the prerequisite check. Tell the user which version will be installed and ask whether they want a different version before running Helm.
For public chart installs and upgrades, never leave <selected-version> unresolved. Discover versions first:
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm search repo nvidia/k8s-nim-operator --versionsUse the first version returned by helm search repo ... --versions as the latest candidate, then ask:
I found latest NIM Operator chart version <latest-version>. Do you want to install this version, or should I use a different version?If the user accepts the latest version, set selected_version=<latest-version>. If the user provides another version, verify that version appears in the helm search repo output before using it. If it does not appear, stop and ask the user to choose one of the available versions.
For local chart installs and upgrades, inspect the local chart:
helm show chart deployments/helm/k8s-nim-operatorTell the user the local chart version and appVersion, then ask whether to proceed with that local chart or switch to the public chart flow.
When the user asks for a dry run or demo, do not install anything. Start by calling the bundled validation helper so preflight evidence is captured before chart discovery or Helm rendering. Use this sequence:
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.sh
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm search repo nvidia/k8s-nim-operator --versions
helm template nim-operator nvidia/k8s-nim-operator \
--namespace nim-operator \
--version <selected-version> \
<approved-values>
helm upgrade --install nim-operator nvidia/k8s-nim-operator \
--namespace nim-operator \
--create-namespace \
--version <selected-version> \
<approved-values> \
--dry-run --debugBefore rendering or dry-running Helm, resolve <selected-version> through the Version Selection flow and replace <approved-values> with the exact values the user approved, such as --set operator.admissionController.enabled=false or --set dynamo.enabled=true.
Start prerequisite discovery by calling the bundled validation helper:
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shThis is the canonical preflight call site for the skill. It checks client tools, cluster access, RBAC, nodes, GPU availability, GPU Operator status, cert-manager status, KServe presence, current NIM Operator state, and NIM Operator CRDs.
If the helper is unavailable or a narrower manual check is needed, run these read-only checks before proposing install commands:
kubectl config current-context
kubectl cluster-info
kubectl auth can-i create customresourcedefinitions.apiextensions.k8s.io
kubectl auth can-i create clusterroles.rbac.authorization.k8s.io
helm version
kubectl get nodes
kubectl get nodes -o custom-columns=NAME:.metadata.name,GPUS:.status.allocatable.nvidia\.com/gpu
kubectl get ns gpu-operator
kubectl get pods -n gpu-operator
kubectl get clusterpolicies.nvidia.com
kubectl get ns cert-manager
kubectl get pods -n cert-managerInterpret the results:
nvidia.com/gpu capacity and allocatable resources.cert-manager is required when operator.admissionController.enabled=true and operator.admissionController.tls.mode=cert-manager, which are NIM Operator chart defaults. If cert-manager is absent, either stop and ask the user to install it, or propose --set operator.admissionController.enabled=false only if the customer accepts disabling the admission controller.For KServe verification, also run:
kubectl get crd inferenceservices.serving.kserve.io
kubectl get pods -n kserveIf KServe is absent, do not install it. Tell the user KServe-backed NIMService resources will not work until KServe is installed.
If GPU Operator is already installed and clusterpolicies.nvidia.com reports ready, do not reinstall it.
If GPU Operator is absent, ask before installing it. Use the public NVIDIA Helm repo unless the user provides a different source:
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm upgrade --install gpu-operator nvidia/gpu-operator \
--namespace gpu-operator \
--create-namespaceAfter installing GPU Operator, verify:
kubectl rollout status deployment/gpu-operator -n gpu-operator --timeout=300s
kubectl get pods -n gpu-operator
kubectl get clusterpolicies.nvidia.com
kubectl describe node <gpu-node-name>Treat confidential computing, MIG partitioning, DRA, driver preinstallation, proxy settings, and air-gapped installation as advanced GPU Operator scenarios. Pause and ask for the customer's desired mode before adding chart values for those cases.
Build one command block for the selected path, then ask before executing.
Discover available versions first:
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm search repo nvidia/k8s-nim-operator --versionsTell the user which chart version appears latest and ask whether to use it or a specific version.
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm upgrade --install nim-operator nvidia/k8s-nim-operator \
--namespace nim-operator \
--create-namespace \
--version <selected-version>For local chart installs, read the local chart version and app version:
helm show chart deployments/helm/k8s-nim-operatorTell the user the local chart version and ask whether to proceed with the local checkout or use the public chart instead.
These local chart commands assume the shell is running from the repository root.
helm upgrade --install nim-operator deployments/helm/k8s-nim-operator \
--namespace nim-operator \
--create-namespaceIf local install uses Dynamo, run this first because Dynamo is a chart dependency:
helm dependency update deployments/helm/k8s-nim-operatorFor basic Dynamo support, append:
--set dynamo.enabled=trueExpose these Dynamo sub-options only when the customer asks for advanced Dynamo configuration:
--set dynamo.grove.enabled=true
--set dynamo.kai-scheduler.enabled=trueDo not enable Dynamo or its sub-options by default.
For OpenShift clusters, enable OpenShift-specific ClusterRole permissions (SCC, Routes, OpenShift config APIs):
--set openshift.enabled=trueLeave openshift.enabled false (the default) on vanilla Kubernetes.
If cert-manager is missing and the user wants to proceed without it, append:
--set operator.admissionController.enabled=falseMake it clear that this disables the NIM Operator admission controller. Do not silently add this flag.
After install or upgrade, call the bundled validation helper again to collect post-change evidence:
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shThen run rollout-specific verification:
helm status nim-operator -n nim-operator
helm get values nim-operator -n nim-operator
kubectl get deployment -n nim-operator -l app.kubernetes.io/instance=nim-operator,app.kubernetes.io/name=k8s-nim-operator
kubectl rollout status deployment/<deployment-name-from-previous-command> -n nim-operator --timeout=180s
kubectl get pods -n nim-operator
kubectl get crd | grep -E 'apps.nvidia.com|nvidia.com'Do not hardcode the deployment name. Helm renders it from the release name and chart name, so the default release usually creates nim-operator-k8s-nim-operator.
Verify these NIM Operator CRDs are present:
nimservices.apps.nvidia.comnimcaches.apps.nvidia.comnimpipelines.apps.nvidia.comnimbuilds.apps.nvidia.comnemodatastores.apps.nvidia.comnemoentitystores.apps.nvidia.comnemocustomizers.apps.nvidia.comnemoevaluators.apps.nvidia.comnemoguardrails.apps.nvidia.comIf Dynamo is enabled, also verify:
kubectl get crd | grep -i dynamo
kubectl get pods -n nim-operatorIf KServe compatibility was requested, repeat:
kubectl get crd inferenceservices.serving.kserve.io
kubectl get pods -n kserveUse the same helm upgrade --install command for both fresh installs and upgrades. Before upgrading, call the validation helper to capture the pre-upgrade baseline:
.agents/skills/nim-operator-install/scripts/validate-nim-operator-install.shThen inspect the current release and available target versions:
helm status nim-operator -n nim-operator
helm get values nim-operator -n nim-operator
helm list -n nim-operator
helm search repo nvidia/k8s-nim-operator --versionsThen explain:
For public chart upgrades, include --version <selected-version>. For local chart upgrades, use the local chart path. Preserve user-approved values such as --set operator.admissionController.enabled=false or --set dynamo.enabled=true unless the user asks to change them.
After the upgrade, run the normal verification flow and compare the new Helm status and controller rollout with the pre-upgrade state.
helm search repo nvidia/<chart-name> --versions after helm repo update and ask whether to use an available version.kubectl describe node; do not proceed to model deployment.NIMService resources using spec.inferencePlatform: kserve need KServe installed first.helm upgrade --install and keep operator.upgradeCRD=true unless the user requests otherwise.© 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
SKILL.md and 3 other files (scripts, references) in .agents/skills/nim-operator-install of NVIDIA/k8s-nim-operator.
Open the folder on GitHubat commit 315bf38
Nim Operator Install 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 |
|---|---|---|---|---|---|---|
| Nim Operator Install this skillNVIDIA/k8s-nim-operator | 159 | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Sim Helmsimstudioai/sim | 30k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Helm Chart ScaffoldingCybereason-Public/owLSM | 280 | 13 repos | ~381 | Automated safety check: Pass | GPL-2.0 | |
| NGINX Ingress Controller Feature Checklistsnginx/kubernetes-ingress | 5.1k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Kubernetes SpecialistJeffallan/claude-skills | 12k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| KubeShark for KubernetesLukasNiessen/kubernetes-skill | 444 | — | ~1.2k | Automated safety check: Pass | MIT |
simstudioai/sim
Install, upgrade, and operate the Sim Helm chart on Kubernetes.
Cybereason-Public/owLSM
Comprehensive guidance for creating, organizing, and managing Helm charts for packaging and deploying Kubernetes applications.
nginx/kubernetes-ingress
Gives step-by-step checklists for adding Ingress annotations, VirtualServer fields and Helm values to the NGINX Kubernetes Ingress Controller, with common gotchas.
Jeffallan/claude-skills
Creates and checks Kubernetes manifests, Helm charts, RBAC and network policies, and helps debug pod problems, with kubectl checks and rollback steps.
LukasNiessen/kubernetes-skill
Keeps Kubernetes manifests, Helm charts and policies grounded by diagnosing six failure modes, such as insecure defaults and API drift, and loading only matching references.
astronomer/astronomer
A skill your agent uses when writing, editing, reviewing, or running Helm chart tests for the Astronomer APC repository.
NVIDIA/k8s-nim-operator
Safely uninstall NVIDIA NIM Operator from Kubernetes with inventory checks, explicit approval gates for destructive actions, optional custom resource cleanup, optional CRD removal, and…
Works with
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Install NVIDIA NIM Operator on Kubernetes with prerequisite checks, optional NVIDIA GPU Operator dependency installation, public or local Helm chart selection, optional Dynamo support, and optional…. Nim Operator Install is an agent skill from NVIDIA/k8s-nim-operator, published by the product's own GitHub organization. Install NVIDIA NIM Operator on Kubernetes with prerequisite checks, optional NVIDIA GPU Operator dependency installation, public or local Helm chart selection, optional Dynamo support, and optional KServe compatibility verification.
Nim Operator Install fits situations like: A customer wants to install; upgrade the NIM Operator itself; without Dynamo and KServe; but does not want to deploy a NIM inference model yet.
Run `npx skills add NVIDIA/k8s-nim-operator --skill nim-operator-install -a claude-code`. Or copy the skill folder (.agents/skills/nim-operator-install in NVIDIA/k8s-nim-operator) into .claude/skills/nim-operator-install in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/k8s-nim-operator --skill nim-operator-install -a codex`. Or copy the skill folder (.agents/skills/nim-operator-install in NVIDIA/k8s-nim-operator) into .agents/skills/nim-operator-install in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/k8s-nim-operator --skill nim-operator-install -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nim-operator-install, .gemini/skills/nim-operator-install, .github/skills/nim-operator-install and .opencode/skills/nim-operator-install in your project.
Going by SKILL.md and its folder, Nim Operator Install needs a shell for the scripts in its folder and the command-line tools its instructions call (helm, kubectl and ssh). Our summary lists: A Bash shell.
SKILL.md names 1 domain. In commands or code: helm.ngc.nvidia.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Nim Operator Install is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Nim Operator Install: Sim Helm (simstudioai/sim, 30k stars), Helm Chart Scaffolding (Cybereason-Public/owLSM, 280 stars), NGINX Ingress Controller Feature Checklists (nginx/kubernetes-ingress, 5.1k stars) and Kubernetes Specialist (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/k8s-nim-operator, which has 159 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 5, 2026.
Source: NVIDIA/k8s-nim-operator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.