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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

Install / Use

npx skills add kubesphere/kubesphere --skill vector

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

97/100

Category

Automation

Supported Platforms

Universal

Tags

Our assessment of vector

vector scores 97/100 on our quality scale, 109th of 1,264 Automation skills we index (top 9%).

Its SKILL.md is 10 KB long, well organised into 37 sections with 16 code examples: a thorough specification that gives an agent plenty to work with.

With 17,059 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 2 months ago, so vector is actively maintained.
  • No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
  • Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-26. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

vector compared with similar skills

All 4 of these similar skills score higher than vector; compare them before choosing.

SkillScoreStarsUpdatedFormat
vector (this skill)by kubesphere9717.1k2mo agoSKILL.md
Agent-Reachby Panniantong10085.5k10d agoCLAUDE.md
rufloby ruvnet10073.3k1d agoCLAUDE.md
Scraplingby D4Vinci10083.7ktodayMCP Server
algorithmic-artby anthropics100177.9k3d agoSKILL.md

Frequently asked questions

How do I install vector?
Run npx skills add kubesphere/kubesphere --skill vector. The install tabs above show the steps for each supported agent.
Which AI agents does vector work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is vector safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is vector still maintained?
The repository was last updated about 2 months ago, so vector is actively maintained.

name: vector description: 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

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

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 provide → You 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:

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

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:

# 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

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

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | agent.role | string | "Agent" | Role identifier | | agent.image.tag | string | "0.53.0-debian" | Vector image tag | | agent.resources.requests.cpu | string | "100m" | CPU request | | agent.resources.requests.memory | string | "100Mi" | Memory request | | agent.resources.limits.cpu | string | "2000m" | CPU limit | | agent.resources.limits.memory | string | "2000Mi" | Memory limit | | agent.service.ports | list | see values.yaml | Service ports | | agent.exportMetrics.enabled | bool | false | Enable metrics export |

Agent Sinks OpenSearch Parameters

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | agent.sinks.opensearch.endpoints | list | Yes | OpenSearch endpoint URLs | | agent.sinks.opensearch.auth.strategy | string | Yes | Authentication strategy (set to basic) | | agent.sinks.opensearch.auth.user | string | Yes | Username for authentication | | agent.sinks.opensearch.auth.password | string | Yes | Password for authentication | | agent.sinks.opensearch.tls.verify | bool | No | Enable TLS verification (default: false) |

Example:

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:

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

Extension Operations

Check Extension Status

# View extension installation status
kubectl get installplan vector

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

Check Pod Status

# 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

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

Update Configuration

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:

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:

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

# 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

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

Common Issues

| Issue | Solution | |-------|----------| | Pods not starting | Check if OpenSearch is accessible | | Data not flowing | Verify sink configuration and network connectivity | | Agent not on member cluster | Check multicluster installation settings | | Out of memory | Increase resource limits in configuration |

Related Skills

View on GitHub
GitHub Stars17.1k
CategoryAutomation
Updated2mo ago
Forks2.8k

Languages

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Trust signals

88/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 medium