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Nodegroup

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kubesphere
nodegroup

NodeGroup operation Skill for the edgewize nodegroup project. Use this whenever the user wants to query, create, update, delete, bind, unbind, or troubleshoot NodeGroup resources, including node binding, namespace binding, workspace binding, and deployment/config inspection for nodegroup.

Overview

Publisherkubesphere
Repositorykubesphere
Skill namenodegroup
Stars
17.1K
Forks
2.8K
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Nodegroup 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.
  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/kubesphere/kubesphere.git /tmp/kubesphere
mkdir -p .claude/skills
cp -r /tmp/kubesphere/skills/nodegroup .claude/skills/nodegroup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nodegroup 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 Nodegroup 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 Nodegroup 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.

NodeGroup Operations

Purpose

Use this skill to perform real nodegroup-related operations in the edgewize-io/nodegroup environment.

This skill should help with:

  • listing and inspecting NodeGroup
  • creating, updating, patching, and deleting NodeGroup
  • binding and unbinding nodes
  • binding and unbinding namespaces
  • binding and unbinding workspaces
  • troubleshooting failed or incomplete nodegroup sync
  • checking deployment config under config/nodegroup

Prefer using the bundled script scripts/nodegroup_api.py for authenticated KAPI operations. Use kubectl only as a verification or fallback tool when the API path is unavailable.

Prerequisites

Use the bundled script:

  • scripts/nodegroup_api.py

Authentication model:

  • logs in against /oauth/token
  • stores token in ~/.kubesphere_token
  • uses KUBESPHERE_HOST, KUBESPHERE_USERNAME, KUBESPHERE_PASSWORD, and KUBESPHERE_TOKEN

Setup:

bash
cd scripts
pip install requests
export KUBESPHERE_HOST="http://<kubesphere-host>"
python nodegroup_api.py login --username admin --password <password>

Token helpers:

bash
python nodegroup_api.py clear-cache
python nodegroup_api.py request GET /kapis/infra.kubesphere.io/v1alpha1/nodegroups

Source of Truth

When you need to confirm how an operation works, read these files first from the nodegroup source repository root:

  • pkg/kapis/infra/v1alpha1/register.go
  • pkg/kapis/infra/v1alpha1/handler.go
  • pkg/kapis/infra/v1alpha1/workspace_handler.go
  • pkg/controller/nodegroup/nodegroup_controller.go
  • pkg/constants/constants.go

Do not invent unsupported operations. Base the workflow on the source repository.

Main Resources

NodeGroup

Cluster-scoped resource.

Important fields:

  • spec.alias
  • spec.description
  • spec.manager
  • status.state

Safe Operating Rules

  • Read-only requests: prefer python nodegroup_api.py ... query commands.
  • Write requests: perform only the exact requested change.
  • Do not delete resources unless the user explicitly asks for deletion.
  • For destructive or potentially disruptive actions such as node unbind or delete, confirm impact in your response if the request is ambiguous.
  • When changing bindings, verify the current state first, then apply the change, then verify the result.

Preferred Operation Flow

For any write operation, follow this order:

  1. Inspect the current resource.
  2. Apply the minimal required change.
  3. Verify the resulting resource and any affected node, namespace, or workspace.
  4. If sync looks wrong, inspect labels, annotations, and controller behavior.

Core Commands

1. Query NodeGroups

bash
python nodegroup_api.py nodegroup list
python nodegroup_api.py nodegroup get <name>

2. Create a NodeGroup

bash
python nodegroup_api.py nodegroup create \
  --name <nodegroup-name> \
  --alias "<alias>" \
  --description "<description>" \
  --manager "<manager>"
python nodegroup_api.py nodegroup get <nodegroup-name>

3. Update or Patch a NodeGroup

Prefer patching for small changes.

bash
python nodegroup_api.py nodegroup patch <name> \
  --alias "<new-alias>" \
  --description "<new-description>" \
  --manager "<manager>"
python nodegroup_api.py nodegroup get <name>

For unsupported fields or ad hoc testing, use raw request mode.

For PATCH, send a JSON Patch array:

bash
python nodegroup_api.py request PATCH /kapis/infra.kubesphere.io/v1alpha1/nodegroups/<name> '[{"op":"add","path":"/spec/alias","value":"<new-alias>"}]'

4. Delete a NodeGroup

Only do this when explicitly requested.

bash
python nodegroup_api.py nodegroup delete <name>

If delete hangs, inspect finalizers:

bash
python nodegroup_api.py nodegroup get <name>

Relevant finalizer:

  • finalizers.nodegroups.kubesphere.io

5. Bind or Unbind a Node

Node binding is implemented through nodegroup APIs and reflected with label:

  • apps.edgewize.io/nodegroup=<nodegroup-name>

Read current state:

bash
kubectl get node <node-name> --show-labels
kubectl get nodes -l apps.edgewize.io/nodegroup=<nodegroup-name>

Operate through the bundled script:

bash
python nodegroup_api.py bind node --nodegroup <nodegroup-name> --node <node-name>
python nodegroup_api.py unbind node --nodegroup <nodegroup-name> --node <node-name>

# Verify
kubectl get nodes -l apps.edgewize.io/nodegroup=<nodegroup-name>
kubectl get node <node-name> -o yaml

6. Bind or Unbind a Namespace

Read current state:

bash
kubectl get ns <namespace> --show-labels

Operate through the bundled script:

bash
python nodegroup_api.py bind namespace --nodegroup <nodegroup-name> --namespace <namespace>
python nodegroup_api.py unbind namespace --nodegroup <nodegroup-name> --namespace <namespace>

# Verify
python nodegroup_api.py nodegroup get <nodegroup-name>
kubectl get ns <namespace> -o yaml

7. Bind or Unbind a Workspace

bash
python nodegroup_api.py bind workspace --nodegroup <nodegroup-name> --workspace <workspace>
python nodegroup_api.py unbind workspace --nodegroup <nodegroup-name> --workspace <workspace>

Troubleshooting Checklist

When an operation appears to succeed but state is wrong, check these in order:

  1. Does the resource exist?
  2. Does it still have a finalizer?
  3. Are node labels correct?
  4. Are namespace or workspace labels/annotations correct?
  5. Did IPPool annotations or labels fail to sync?
  6. Is the feature enabled in nodegroup config?

Useful checks:

bash
python nodegroup_api.py nodegroup get <name>
kubectl get node <node-name> -o yaml
kubectl get ns <namespace> -o yaml
kubectl get cm nodegroup-config -n kubesphere-system -o yaml
kubectl get pods -n kubesphere-system | grep nodegroup

Important constants:

  • apps.edgewize.io/nodegroup
  • apps.edgewize.io/namespace-
  • nodegroup.infra.kubesphere.io/parent
  • infra.kubesphere.io/ippool-

Deployment and Config

If the user asks how nodegroup is deployed or configured, inspect these files from the nodegroup source repository root:

  • config/nodegroup/templates/nodegroup-apiserver.yml
  • config/nodegroup/templates/nodegroup-controller-manager.yaml
  • config/nodegroup/templates/nodegroup-config.yaml
  • config/nodegroup/values.yaml

ConfigMap key:

  • nodegroup.yaml

Notable config sections:

  • scheduling
  • policy
  • rbac
  • ippool

API-First Rule

If the user asks for an operation that already has a dedicated nodegroup API, prefer the bundled script and the dedicated nodegroup API semantics over manual label hacking.

Examples:

  • node bind/unbind: use python nodegroup_api.py bind node ...
  • namespace bind/unbind: use python nodegroup_api.py bind namespace ...
  • workspace bind/unbind: use python nodegroup_api.py bind workspace ...
  • ad hoc API call: use python nodegroup_api.py request ...

Only fall back to direct manifest edits or label repair when the request is explicitly about low-level repair.

Response Style For This Skill

When using this skill:

  • tell the user what operation you are performing
  • verify before and after state
  • mention any side effects or risks
  • if blocked by missing cluster credentials or missing API access, say exactly what is missing
  • if the operation is supported in code but cannot be executed in the current environment, provide the exact nodegroup_api.py command or API path to run next

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 Nodegroup AI skill do?

NodeGroup operation Skill for the edgewize nodegroup project. Use this whenever the user wants to query, create, update, delete, bind, unbind, or troubleshoot NodeGroup resources, including node binding, namespace binding, workspace binding, and deployment/config inspection for nodegroup.

Why use Nodegroup on TypingMind?

Because you install it once and use it with any model. Nodegroup 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 Nodegroup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kubesphere/kubesphere/tree/master/skills/nodegroup. 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 Nodegroup?

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 Nodegroup?

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

Is the Nodegroup AI skill free?

It is published on GitHub by kubesphere. Check the repository for licensing terms. 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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