Huawei Cloud Kubectl Cce Installer logo

Huawei Cloud Kubectl Cce Installer

Organization
huaweicloud
huawei-cloud-kubectl-cce-installer

Query specific Kubernetes resources in Huawei Cloud CCE clusters through kubectl cce. Trigger when users ask to get, describe, inspect, or view a Pod, workload, Service, ConfigMap, node, namespace, or other CCE Kubernetes resource, or when they ask to install or repair the local kubectl-cce prerequisites. Install kubectl and kubectl-cce only when they are missing locally.

Overview

Publisherhuaweicloud
Repositoryhuaweicloud-skills
Skill namehuawei-cloud-kubectl-cce-installer
Stars
59
Forks
24
Bundled files
4
LicenseMIT
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Huawei Cloud Kubectl Cce Installer 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/huaweicloud/huaweicloud-skills/tree/master/skills/container/cce/huawei-cloud-kubectl-cce-installer
  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/huaweicloud/huaweicloud-skills.git /tmp/huaweicloud-skills
mkdir -p .claude/skills
cp -r /tmp/huaweicloud-skills/skills/container/cce/huawei-cloud-kubectl-cce-installer .claude/skills/huawei-cloud-kubectl-cce-installer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Huawei Cloud Kubectl Cce Installer 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 Huawei Cloud Kubectl Cce Installer 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 Huawei Cloud Kubectl Cce Installer 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.

Huawei Cloud CCE Kubectl Resource Query

Use this skill to retrieve specific CCE Kubernetes resources through kubectl cce. Resource access is the primary task. Local installation is only a prerequisite recovery step when kubectl or the kubectl-cce plugin is unavailable.

Overview And Safety

  • Use kubectl cce; do not use a direct Kubernetes API client.
  • Read-only commands only: get, describe, and logs.
  • Never use -A, --all-namespaces, or another whole-cluster resource query.
  • For namespaced resources, require --namespace <namespace> and, where feasible, a specific resource name.
  • For cluster-scoped resources such as node, namespace, persistentvolume, and storageclass, require an exact resource name. Do not list every instance.
  • Never run apply, create, delete, edit, patch, replace, scale, rollout, cordon, drain, or exec.
  • Do not print credentials, tokens, kubeconfig content, or Secret data.

Prerequisites And Required Context

InputRequirement
cluster_idRequired standard UUID. An exact name may be resolved with hcloud CCE ListClusters, but never run kubectl cce until it resolves to one UUID.
regionRequired. Obtain it from the request or current context, then HW_REGION_NAME; otherwise ask the user.
Resource kindRequired, for example pod, deployment, service, configmap, or node.
Namespace or exact nameAt least one is required. Namespaced resources require a namespace; cluster-scoped resources require an exact name.

Credentials follow the plugin rules in plugin-usage.md. Use explicit --cli-access-key, --cli-secret-key, and optional --cli-security-token only when the caller supplies them. Do not fall back to other credentials in that case.

Workflow

  1. Before any resource query, validate the target region and cluster ID. If either is missing, or the cluster ID cannot be resolved to one existing UUID in that region, do not run kubectl cce; ask the user to provide the correct region and cluster ID.

  2. Confirm the resource kind, namespace, and exact resource name when required.

  3. Check local prerequisites:

    bash
    bash scripts/install_kubectl_cce.sh --check
  4. When both kubectl and kubectl-cce are available, query the requested resource.

  5. When either executable is missing, read installation.md, then show the installer plan. Installation or replacement is an R1 local change and requires explicit confirmation:

    bash
    bash scripts/install_kubectl_cce.sh --bin-dir <directory>
    sudo bash scripts/install_kubectl_cce.sh --execute --bin-dir <directory>
  6. Verify installation with kubectl version --client and kubectl plugin list, then run only the requested read-only resource query.

Core Commands

Use one explicit namespace per namespaced query:

bash
kubectl cce --cluster-id <cluster-id> --region <region> \
  get pod <pod-name> --namespace <namespace> -o yaml

kubectl cce --cluster-id <cluster-id> --region <region> \
  describe deployment <deployment-name> --namespace <namespace>

kubectl cce --cluster-id <cluster-id> --region <region> \
  get service <service-name> --namespace <namespace> -o yaml

For a cluster-scoped resource, use an exact name:

bash
kubectl cce --cluster-id <cluster-id> --region <region> \
  get node <node-name> -o yaml

For credential modes, command forms, and x509 retry behavior, read plugin-usage.md. For Windows usage and installation fallbacks, read installation.md. If a command fails with an x509 upstream TLS validation error, retry that same command once with --cce-insecure-upstream-tls=true immediately after cce.

Risk Levels

OperationLevelGuidance
Resource query and local prerequisite checkR3May run automatically.
Local binary installation, source build, or plugin replacementR1Preview first and require explicit confirmation before --execute.

Output Format

Return the cluster ID, region, resource kind, namespace when applicable, resource name, and requested status or fields. State clearly whether the resource is not found, access is denied, prerequisites are missing, or an installation confirmation is needed.

Parameters And Confirmation

Confirm cluster_id, region, resource kind, namespace, and exact resource name before issuing a query. If cluster_id is missing or cannot be resolved in the supplied region, stop and ask the user for the correct region and cluster ID. Confirm the target installation directory before any --execute action.

Verification

After installation, run kubectl version --client and kubectl plugin list; after a query, verify that the returned resource identity matches the requested cluster, namespace, and name.

Best Practices

Use the narrowest requested resource query and avoid broad list operations even when the caller can access the whole cluster.

Notes

When the requested resource cannot be scoped to a namespace or exact name, ask the user to narrow the target instead of expanding to a cluster-wide query.

References

DocumentUse
Plugin UsageCredentials, command forms, and x509 retry.
InstallationLocal prerequisites, installer parameters, Windows usage, source fallback, confirmation, and troubleshooting.
Acceptance CriteriaResource-query and installation acceptance checks.

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 Huawei Cloud Kubectl Cce Installer AI skill do?

Query specific Kubernetes resources in Huawei Cloud CCE clusters through kubectl cce. Trigger when users ask to get, describe, inspect, or view a Pod, workload, Service, ConfigMap, node, namespace, or other CCE Kubernetes resource, or when they ask to install or repair the local kubectl-cce prerequisites. Install kubectl and kubectl-cce only when they are missing locally.

Why use Huawei Cloud Kubectl Cce Installer on TypingMind?

Because you install it once and use it with any model. Huawei Cloud Kubectl Cce Installer 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 Huawei Cloud Kubectl Cce Installer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/container/cce/huawei-cloud-kubectl-cce-installer. 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 Huawei Cloud Kubectl Cce Installer?

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 Huawei Cloud Kubectl Cce Installer?

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

Is the Huawei Cloud Kubectl Cce Installer AI skill free?

Yes. It is published on GitHub by huaweicloud under the MIT 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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