Oke Multihome Deployer logo

Oke Multihome Deployer

Organization
oracle
oke-multihome-deployer

Deploy and troubleshoot multihome networking on OCI Kubernetes Engine using existing GVA secondary VNIC node pools, Multus thick plugin, OCI CNI/IPAM, NetworkAttachmentDefinitions, and test pods. Use when the user asks to auto-discover OKE cluster subnets or node pools for multihome, create OKE multihome pod manifests, configure Multus NADs for secondary VNIC interfaces, validate multihomed pod connectivity, inspect OCI CNI IPAM allocation on OKE nodes, or package repeatable OKE GVA multihome deployment steps. Do not use it to create or update GVA node pools. For broad incident RCA, DPDK/SR-IOV failures, or non-Multus pod symptoms, use `oke-troubleshooter` first.

Overview

Publisheroracle
Repositoryskills
Skill nameoke-multihome-deployer
Stars
879
Forks
251
Bundled files
6
LicenseUPL-1.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by oracle on GitHub. Read the source before you install it.

Installation

Install the Oke Multihome Deployer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/oracle/skills/tree/main/oci/oke/skills/oke-multihome-deployer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oracle/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/oci/oke/skills/oke-multihome-deployer .claude/skills/oke-multihome-deployer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oke Multihome Deployer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oke Multihome Deployer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oke Multihome Deployer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OKE Multihome Deployer

Scope

Use this skill after an OKE node pool already has GVA secondary VNIC profiles attached. This skill deploys and verifies Multus-based pod multihoming; it does not create or update GVA node pools.

If the user needs to create or update the node pool secondary VNIC profiles, use oke-gva-deployer first, then return here.

Supporting references:

  • validation-report-template.md — standard final validation report structure.

Core Workflow

  1. Confirm target cluster/context and auth.
    • Prefer explicit OCI_CLI_PROFILE and OCI_CLI_AUTH; when using OCI security-token auth, pass --auth security_token or set OCI_CLI_AUTH=security_token.
    • If kubectl fails with exec: executable oci failed, ask the user to refresh the OCI session before applying anything.
  2. Auto-discover cluster, node pool, and subnet data before generating YAML.
    • Use scripts/discover-oke-multihome.py.
    • Prefer --cluster-id when the user provides it; otherwise try --context, then --cluster-name with --compartment-id.
    • The discovery script queries OKE node pools and fetches all referenced placement and secondary VNIC subnets.
    • Use the discovered node names for --pod NAME=NODE_NAME entries.
  3. Verify prerequisites from Kubernetes and node-side checks.
    • OKE CNI must be OCI_VCN_IP_NATIVE.
    • Worker nodes must expose secondary VNIC interfaces. Treat enp1s0 and enp2s0 as common defaults, then confirm the actual interface names from discovery or node-side evidence before applying manifests.
    • OCI CNI binaries should exist on nodes: oci-ipam, oci-ipvlan, oci-ptp.
    • The standard ipvlan CNI binary is needed when using a plain ipvlan secondary NAD.
  4. Install Multus thick plugin if absent.
    • Upstream manifest used in this workflow: https://raw.githubusercontent.com/k8snetworkplumbingwg/multus-cni/master/deployments/multus-daemonset-thick.yml
    • TODO(live validation): pin this manifest to a tested Multus release tag after validating it against the target OKE Kubernetes version. Keep using the repo's current manifest reference until that live validation is complete.
  5. Generate or edit the deployment manifest.
    • Use scripts/generate-multihome-manifest.py for repeatable YAML.
    • Default network NAD: kube-system/gva-default-network, using oci-ipvlan on the first secondary VNIC path.
    • Secondary NAD: workload namespace scoped, using ipvlan on the second secondary VNIC path with oci-ipam.
  6. Apply and verify.
    • Use fully qualified images such as docker.io/nicolaka/netshoot:v0.13; CRI-O may reject short image names.
    • Check pod annotation k8s.v1.cni.cncf.io/network-status.
    • Exec ip -br addr in each pod and ping the peer net1 IP both directions.
    • Use validation-report-template.md for the final validation report, whether validation passed, failed, or remains incomplete.

Discovery

Run discovery before manifest generation:

bash
OCI_CLI_PROFILE=<oci-profile> OCI_CLI_AUTH=<oci-auth-mode> \
python3 <skill>/scripts/discover-oke-multihome.py \
  --cluster-id <cluster_ocid> \
  --region <region> \
  --pretty > oke-multihome-discovery.json

If the current kubeconfig contains the cluster OCID in its exec user args, use:

bash
OCI_CLI_PROFILE=<oci-profile> OCI_CLI_AUTH=<oci-auth-mode> \
python3 <skill>/scripts/discover-oke-multihome.py \
  --context <kubectl_context> \
  --region <region> \
  --pretty

Use the JSON output to pick:

  • Node pool with GVA secondaryVnics.
  • Primary placement subnet(s), for node placement context.
  • Secondary VNIC subnet IDs/CIDRs, for deciding which interface path maps to each pod network.
  • Up to two ready node names for pinned smoke-test pods.

Do not guess subnet CIDRs or node names when discovery can retrieve them.

Manifest Generation

Generate a two-pod test manifest:

bash
python3 <skill>/scripts/generate-multihome-manifest.py \
  --namespace gva-multihome-test \
  --default-interface <confirmed-first-secondary-vnic-interface> \
  --secondary-interface <confirmed-second-secondary-vnic-interface> \
  --pod gva-multihome-a=<node-name-a> \
  --pod gva-multihome-b=<node-name-b> \
  > gva-multihome-pods.yaml

Use Kubernetes node names exactly as returned by kubectl get nodes. In OKE clusters configured like the handoff, node names may be node private IPs.

Verification Commands

Use these checks after applying manifests:

bash
kubectl -n kube-system get pods -l name=multus -o wide
kubectl -n gva-multihome-test get pods -o wide
kubectl -n gva-multihome-test get pod gva-multihome-a gva-multihome-b \
  -o jsonpath='{range .items[*]}{.metadata.name}{" on "}{.spec.nodeName}{"\n"}{.metadata.annotations.k8s\.v1\.cni\.cncf\.io/network-status}{"\n\n"}{end}'
kubectl -n gva-multihome-test exec gva-multihome-a -- ip -br addr
kubectl -n gva-multihome-test exec gva-multihome-b -- ip -br addr

Then ping net1 IPs across pods:

bash
kubectl -n gva-multihome-test exec gva-multihome-a -- ping -c 3 -W 2 <pod-b-net1-ip>
kubectl -n gva-multihome-test exec gva-multihome-b -- ping -c 3 -W 2 <pod-a-net1-ip>

Troubleshooting

Read references/oke-multihome-notes.md when:

  • Multus pods are missing or failing.
  • Pods are stuck creating sandbox/network.
  • ipvlan is missing on the host.
  • OCI IPAM capacity or allocation behavior is unclear.
  • Floating IP/VIP behavior is being explored.

Read references/oke-dpdk-mlx5-notes.md when:

  • The workload uses DPDK, SR-IOV device-plugin resources, Mellanox/NVIDIA VFs, mlx5, vfio-pci, hugepages, or /dev/infiniband.
  • A pod requests Multus networks and device-plugin resources but network-status only shows eth0.
  • You need to decide whether the issue is Multus/NAD attachment, resource allocation, driver binding, RDMA/verbs exposure, hugepages, or app PCI/interface mapping.

For node-side inspection, prefer a privileged diagnostic DaemonSet that mounts / at /host and runs commands with chroot /host, rather than SSHing to each worker.

Sources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Oke Multihome Deployer AI skill do?

Deploy and troubleshoot multihome networking on OCI Kubernetes Engine using existing GVA secondary VNIC node pools, Multus thick plugin, OCI CNI/IPAM, NetworkAttachmentDefinitions, and test pods. Use when the user asks to auto-discover OKE cluster subnets or node pools for multihome, create OKE multihome pod manifests, configure Multus NADs for secondary VNIC interfaces, validate multihomed pod connectivity, inspect OCI CNI IPAM allocation on OKE nodes, or package repeatable OKE GVA multihome deployment steps. Do not use it to create or update GVA node pools. For broad incident RCA, DPDK/SR...

Why use Oke Multihome Deployer on TypingMind?

Because you install it once and use it with any model. Oke Multihome Deployer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oke Multihome Deployer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/skills/tree/main/oci/oke/skills/oke-multihome-deployer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Oke Multihome Deployer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oke Multihome Deployer?

As many as you like. As long as a model supports skills, you can use Oke Multihome Deployer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oke Multihome Deployer AI skill free?

Yes. It is published on GitHub by oracle under the UPL-1.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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