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TKE Skill

Tencent Cloud TKE (Tencent Kubernetes Engine) operations Skill. No MCP Server required — manage TKE clusters directly through the Skill mechanism of AI Coding Agents.

Supports OpenClaw, CodeBuddy, Claude Code, Gemini CLI, and other mainstream AI Coding Agents.

Prerequisites

pip install tencentcloud-sdk-python-tke

Credential Configuration

Two methods are supported (CLI arguments take precedence):

Method 1: Environment Variables (Recommended)

export TENCENTCLOUD_SECRET_ID=YourSecretId
export TENCENTCLOUD_SECRET_KEY=YourSecretKey

Method 2: CLI Arguments

python tke_cli.py clusters --secret-id AKIDxxx --secret-key xxxxx --region ap-guangzhou

Install Skill

Copy SKILL.md and tke_cli.py to the Skills directory of your Agent. Here are the installation instructions for common Agents:

OpenClaw

# Place SKILL.md and tke_cli.py into the OpenClaw skills directory
mkdir -p skills/tke/
cp SKILL.md tke_cli.py skills/tke/

CodeBuddy

# Project-level (current project only, can be distributed via git)
mkdir -p <your-project>/.codebuddy/skills/tke/
cp SKILL.md tke_cli.py <your-project>/.codebuddy/skills/tke/

# User-level (global)
mkdir -p ~/.codebuddy/skills/tke/
cp SKILL.md tke_cli.py ~/.codebuddy/skills/tke/

Claude Code

# Project-level
mkdir -p <your-project>/.claude/skills/tke/
cp SKILL.md tke_cli.py <your-project>/.claude/skills/tke/

# User-level
mkdir -p ~/.claude/skills/tke/
cp SKILL.md tke_cli.py ~/.claude/skills/tke/

Other Agents

Refer to your Agent's Skill/Prompt loading mechanism. Load SKILL.md as a system prompt and ensure tke_cli.py is executable by the Agent.

Usage

After installation, in your AI Coding Agent:

• Auto-trigger: The Agent will automatically use this Skill when you mention TKE, clusters, or container services
• Manual trigger: Type /tke followed by your request (supported by some Agents)

Example Conversations

List all clusters in the Guangzhou region
Check the status of cluster cls-xxx
Get the kubeconfig for cluster cls-xxx

Supported Commands

Command	Description	Key Parameters
clusters	List clusters	--cluster-ids, --cluster-type, --limit
cluster-status	Query cluster status	--cluster-ids
cluster-level	Query cluster specifications	--cluster-id
endpoints	Query cluster access endpoints	--cluster-id (required)
endpoint-status	Query endpoint status	--cluster-id (required), --is-extranet
kubeconfig	Get kubeconfig	--cluster-id (required), --is-extranet
node-pools	Query node pools	--cluster-id (required), --limit
create-endpoint	Enable cluster access endpoint	--cluster-id (required), --is-extranet, --subnet-id, --security-group, --existed-lb-id, --domain, --extensive-parameters
delete-endpoint	Disable cluster access endpoint	--cluster-id (required), --is-extranet

All commands support --region (default: ap-guangzhou) and --secret-id / --secret-key parameters.

create-endpoint and delete-endpoint are write operations. All other commands are read-only queries.

Use with Kubernetes Specialist Skill

This Skill focuses on cloud-side management of TKE clusters (querying clusters, node pools, getting kubeconfig, etc.). For in-cluster Kubernetes operations (deploying workloads, configuring Services/Ingress, troubleshooting Pods, writing YAML manifests, Helm deployments, etc.), it is recommended to install the Kubernetes Specialist Skill alongside:

npx skills add https://github.com/jeffallan/claude-skills --skill kubernetes-specialist

Typical workflow:

1. Use TKE Skill to query cluster info and obtain kubeconfig
2. Use Kubernetes Specialist Skill for in-cluster resource deployment, troubleshooting, and security hardening

Together, the two Skills cover the full operations spectrum from TKE cluster management to in-cluster K8s operations.

Standalone CLI Usage

Can also be used as a standalone CLI tool without any AI Agent:

# List clusters
python tke_cli.py clusters --region ap-guangzhou

# Query cluster status
python tke_cli.py cluster-status --region ap-guangzhou --cluster-ids cls-xxx

# Query node pools
python tke_cli.py node-pools --region ap-guangzhou --cluster-id cls-xxx

# Get kubeconfig
python tke_cli.py kubeconfig --region ap-guangzhou --cluster-id cls-xxx