Vector logo

Vector

OrganizationPopular
kubesphere
vector

Use when installing or configuring the WizTelemetry Data Pipeline (vector) extension for KubeSphere, which provides data collection, transformation, and routing for observability data including logs, auditing, events, and notifications

Overview

Publisherkubesphere
Repositorykubesphere
Skill namevector
Stars
17.1K
Forks
2.8K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Vector 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/vector .claude/skills/vector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vector 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 Vector 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 Vector 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.

WizTelemetry Data Pipeline (Vector)

Overview

WizTelemetry Data Pipeline is an extension based on vector (https://vector.dev/) that provides the ability to collect, transform, and route observability data. It is a core dependency for other WizTelemetry extensions like Logging, Auditing, Events, and Notification.

When to Use

  • Installing or configuring the WizTelemetry Data Pipeline extension
  • Setting up data collection for logs, auditing, events, and notifications
  • Configuring Vector sinks (OpenSearch)
  • Managing Vector agent components

Installation

Prerequisites

REQUIRED: Complete all steps in order before generating InstallPlan.

Step 1: Get Available Clusters and Confirm Target

⚠️ CRITICAL: DO NOT proceed until target clusters are determined.

Step 1.1: Get available clusters

bash
kubectl get clusters -o jsonpath='{.items[*].metadata.name}'

Step 1.2: Determine target clusters

  • If user explicitly specified target clusters in the request → Use those clusters directly, proceed to Step 2
  • If user did NOT specify target clusters → Ask user to confirm which clusters to deploy to, then proceed to Step 2

Ask user (if not specified):

Available clusters: host, dev
Which clusters do you want to deploy Vector to?
Step 2: Get OpenSearch Endpoint and Credentials (MUST DO)
  • If user already provided OpenSearch endpoint and credentials in the request → Use those directly, proceed to Step 3
  • If user did NOT provideYou MUST ask user for OpenSearch endpoint and credentials

Ask user for (if not provided):

  1. OpenSearch endpoint URL (required)

    • Example: http://<node-ip>:30920 or https://opensearch.example.com:9200
  2. OpenSearch credentials (required)

    • Username (default: admin)
    • Password

DO NOT proceed to Step 3 until user provides both endpoint and credentials.

Step 3: Get Latest Vector Version (if not provided by user)

MUST do this to get the latest version:

bash
kubectl get extensionversions -l kubesphere.io/extension-ref=vector -o jsonpath='{range .items[*]}{.spec.version}{"\n"}{end}' | sort -V | tail -1

This outputs the latest version (e.g., 1.1.4). Note this down - you'll use it in the InstallPlan.

Install Vector Extension

⚠️ IMPORTANT: Complete prerequisite steps (1-3) BEFORE this step.

⚠️ CRITICAL: InstallPlan metadata.name MUST be vector. DO NOT use any other name.

Based on your selections:

  • Target clusters: Use the user-confirmed cluster names
  • OpenSearch endpoint: User-provided endpoint
  • OpenSearch credentials: User-provided username and password

⚠️ CRITICAL: config field is YAML format. You MUST:

  • Use the config structure exactly as shown in the template
  • DO NOT add configuration fields that are not shown in the template
  • DO NOT modify the structure or hierarchy

⚠️ CRITICAL: All placeholders MUST be replaced with actual values. DO NOT leave them as placeholders.

Template
yaml
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
  name: vector
spec:
  extension:
    name: vector
    version: <VECTOR_VERSION>  # From Step 3
  enabled: true
  upgradeStrategy: Manual
  config: |
    agent:
      sinks:
        opensearch:
          auth:
            strategy: basic
            user: <OPENSEARCH_USER>
            password: <OPENSEARCH_PASSWORD>
          endpoints:
            - <OPENSEARCH_ENDPOINT>
  clusterScheduling:
    placement:
      clusters:
        - <TARGET_CLUSTERS>

Replace placeholders:

  • <VECTOR_VERSION>: From Step 2 (e.g., 1.1.4)
  • <OPENSEARCH_ENDPOINT>: User-provided endpoint (e.g., http://<node-ip>:30920)
  • <OPENSEARCH_USER>: User-provided username (default: admin)
  • <OPENSEARCH_PASSWORD>: User-provided password
  • <TARGET_CLUSTERS>: User-confirmed cluster names

⚠️ DO NOT generate InstallPlan until all placeholders have real values.

Wait for Deployment

After applying InstallPlan, you MUST wait for deployment to complete:

bash
# Wait for Vector pods to be ready (on each cluster)
kubectl wait --for=condition=Ready pods -n kubesphere-logging-system -l app.kubernetes.io/instance=vector --timeout=300s

# Verify deployment status
kubectl get pods -n kubesphere-logging-system -l app.kubernetes.io/instance=vector

Show deployment summary to user:

  • Which clusters Vector was deployed to
  • OpenSearch endpoint used
  • Pod status (Ready/Total)
Enable Metrics Export
yaml
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
  name: vector
spec:
  extension:
    name: vector
    version: <VECTOR_VERSION>  # From Step 2
  enabled: true
  upgradeStrategy: Manual
  config: |
    agent:
      sinks:
        opensearch:
          auth:
            strategy: basic
            user: <OPENSEARCH_USER>
            password: <OPENSEARCH_PASSWORD>
          endpoints:
            - <OPENSEARCH_ENDPOINT>
      exportMetrics:
        enabled: true
  clusterScheduling:
    placement:
      clusters:
        - <TARGET_CLUSTERS>

Configuration Parameters

Agent Parameters

ParameterTypeDefaultDescription
agent.rolestring"Agent"Role identifier
agent.image.tagstring"0.53.0-debian"Vector image tag
agent.resources.requests.cpustring"100m"CPU request
agent.resources.requests.memorystring"100Mi"Memory request
agent.resources.limits.cpustring"2000m"CPU limit
agent.resources.limits.memorystring"2000Mi"Memory limit
agent.service.portslistsee values.yamlService ports
agent.exportMetrics.enabledboolfalseEnable metrics export

Agent Sinks OpenSearch Parameters

ParameterTypeRequiredDescription
agent.sinks.opensearch.endpointslistYesOpenSearch endpoint URLs
agent.sinks.opensearch.auth.strategystringYesAuthentication strategy (set to basic)
agent.sinks.opensearch.auth.userstringYesUsername for authentication
agent.sinks.opensearch.auth.passwordstringYesPassword for authentication
agent.sinks.opensearch.tls.verifyboolNoEnable TLS verification (default: false)

Example:

yaml
agent:
  sinks:
    opensearch:
      endpoints:
        - http://<node-ip>:30920
      auth:
        strategy: basic
        user: admin
        password: admin
      tls:
        verify: false

Docker Root Directory Configuration

If Docker root directory is not /var/lib:

yaml
agent:
  extraVolumes:
    - name: docker-root
      hostPath:
        path: /path/to/docker
        type: ''
  extraVolumeMounts:
    - name: docker-root
      mountPath: /path/to/docker

Extension Operations

Check Extension Status

bash
# View extension installation status
kubectl get installplan vector

# View extension version
kubectl get extensionversions -l kubesphere.io/extension-ref=vector

Check Pod Status

bash
# View all Vector pods
kubectl get pods -n kubesphere-logging-system -l app.kubernetes.io/name=vector

# View agent pods
kubectl get pods -n kubesphere-logging-system -l app.kubernetes.io/name=vector,app.kubernetes.io/component=agent

View Logs

bash
# View agent logs
kubectl logs -n kubesphere-logging-system -l app.kubernetes.io/name=vector,app.kubernetes.io/component=agent --tail=100

Update Configuration

yaml
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
  name: vector
spec:
  extension:
    name: vector
    version: <VECTOR_VERSION>
  enabled: true
  upgradeStrategy: Manual
  config: |
    agent:
      sinks:
        opensearch:
          auth:
            strategy: basic
            user: <OPENSEARCH_USER>
            password: <OPENSEARCH_PASSWORD>
          endpoints:
            - <OPENSEARCH_ENDPOINT>
  clusterScheduling:
    placement:
      clusters:
        - <TARGET_CLUSTERS>

Uninstall Extension

Uninstall from all clusters:

bash
kubectl delete installplan vector

Uninstall from specific cluster:

To remove Vector from a specific cluster, update the InstallPlan by removing that cluster from clusterScheduling.placement.clusters:

yaml
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
  name: vector
spec:
  extension:
    name: vector
    version: <VECTOR_VERSION>
  enabled: true
  upgradeStrategy: Manual
  config: |
    agent:
      sinks:
        opensearch:
          auth:
            strategy: basic
            user: <OPENSEARCH_USER>
            password: <OPENSEARCH_PASSWORD>
          endpoints:
            - <OPENSEARCH_ENDPOINT>
  clusterScheduling:
    placement:
      clusters:
        - <REMAINING_CLUSTERS>  # Remove the cluster you want to uninstall from

Important Notes

  1. Dependency: Vector is a core dependency for WizTelemetry extensions. Install it first before installing Logging, Auditing, Events, or Notification.
  2. OpenSearch Required: User must provide OpenSearch endpoint and credentials.
  3. Multicluster: The extension uses installationMode: Multicluster:
    • agent (tag: agent) is deployed to all selected member clusters
  4. Agent Scheduling: Agent pods have affinity to avoid edge nodes and tolerate all taints.
  5. Cross-cluster Access: Ensure OpenSearch endpoint is accessible from all Vector clusters.

Troubleshooting

Check Vector Configuration

bash
# View Vector configmap
kubectl get configmap -n kubesphere-logging-system -l app.kubernetes.io/name=vector

# View specific config
kubectl get configmap -n kubesphere-logging-system vector-config -o yaml

Verify Sinks

bash
# Check if sinks are configured correctly
kubectl get secret -n kubesphere-logging-system vector-sinks -o yaml

Common Issues

IssueSolution
Pods not startingCheck if OpenSearch is accessible
Data not flowingVerify sink configuration and network connectivity
Agent not on member clusterCheck multicluster installation settings
Out of memoryIncrease resource limits in configuration

Frequently asked questions

What does the Vector AI skill do?

Use when installing or configuring the WizTelemetry Data Pipeline (vector) extension for KubeSphere, which provides data collection, transformation, and routing for observability data including logs, auditing, events, and notifications

Why use Vector on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kubesphere/kubesphere/tree/master/skills/vector. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vector?

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 Vector?

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

Is the Vector 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇