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Whizard Logging

OrganizationPopular
kubesphere
whizard-logging

Use when working with WizTelemetry Logging extension for KubeSphere, including installation, configuration, and log query API

Overview

Publisherkubesphere
Repositorykubesphere
Skill namewhizard-logging
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 Whizard Logging 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/whizard-logging .claude/skills/whizard-logging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Whizard Logging 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 Whizard Logging 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 Whizard Logging 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 Logging

Overview

WizTelemetry Logging is an extension component in the KubeSphere Observability Platform for log collection, processing, and storage.

When to Use

  • Installing or configuring the WizTelemetry Logging extension
  • Understanding log collection architecture (container logs + disk log collection)
  • Using the log query API to query logs

Components

ComponentDescriptionDefault Enabled
vector-loggingContainer log collection (collects stdout/stderr from Docker/Containerd)true
logsidecar-injectorDisk log collection (collects logs from files inside containers)false

Dependencies

  • WizTelemetry Platform Service (whizard-telemetry): Required
  • WizTelemetry Data Pipeline (vector): Required
  • OpenSearch (opensearch): Required

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 WizTelemetry Logging to?
Step 2: Get Latest Version (if not provided by user)

MUST do this to get the latest version:

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

This outputs the latest version (e.g., 1.4.0). Note this down - you'll use it in the InstallPlan.

Install WizTelemetry Logging

⚠️ IMPORTANT: Complete prerequisite steps BEFORE this step.

Based on your selections:

  • Target clusters: User-confirmed cluster names

⚠️ CRITICAL: InstallPlan metadata.name MUST be whizard-logging. DO NOT use any other name.

⚠️ 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: whizard-logging
spec:
  extension:
    name: whizard-logging
    version: <VERSION>  # From Step 2
  enabled: true
  upgradeStrategy: Manual
  clusterScheduling:
    placement:
      clusters:
        - <TARGET_CLUSTERS>

Replace placeholders:

  • <VERSION>: From Step 2 (e.g., 1.4.0)
  • <TARGET_CLUSTERS>: User-confirmed cluster names

Note: OpenSearch sink configuration (endpoints, auth) is provided by the vector extension. Make sure vector is installed and configured with OpenSearch before installing logging.

Enable Disk Log Collection

To enable disk log collection, add logsidecar-injector to the config:

yaml
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
  name: whizard-logging
spec:
  extension:
    name: whizard-logging
    version: <VERSION>  # From Step 2
  enabled: true
  upgradeStrategy: Manual
  config: |
    logsidecar-injector:
      enabled: true
    vector-logging:
      filter:
        extraLabelSelector: "app.kubernetes.io/name!=kube-events-exporter"
      calico:
        enabled: true
      systemd:
        docker:
          enabled: true
        kubelet:
          enabled: true
      sinks:
        opensearch:
          enabled: true
          index:
            prefix: "{{ .cluster }}-logs"
            timestring: "%Y.%m.%d"
  clusterScheduling:
    placement:
      clusters:
        - <TARGET_CLUSTERS>

Configuration Parameters

Logsidecar Injector Parameters

ParameterTypeDefaultDescription
logsidecar-injector.enabledboolfalseEnable disk log collection
logsidecar-injector.sidecar.sidecarTypestringvectorSidecar type
logsidecar-injector.resources.limits.cpustring100mCPU limit
logsidecar-injector.resources.limits.memorystring100MiMemory limit
logsidecar-injector.resources.requests.cpustring10mCPU request
logsidecar-injector.resources.requests.memorystring10MiMemory request

Vector Logging Parameters

Filter Parameters
ParameterTypeDefaultDescription
vector-logging.filter.extraLabelSelectorstring"app.kubernetes.io/name!=kube-events-exporter"Extra label selector
vector-logging.filter.extraNamespaceLabelSelectorstring""Extra namespace label selector
vector-logging.filter.includeNamespaceslist[]List of namespaces to collect
vector-logging.filter.excludeNamespaceslist[]List of namespaces to exclude
Calico Log Parameters
ParameterTypeDefaultDescription
vector-logging.calico.enabledbooltrueEnable Calico log collection
vector-logging.calico.logPathlist["/var/log/calico/cni/cni*.log"]Calico log paths
Systemd Log Parameters
ParameterTypeDefaultDescription
vector-logging.systemd.docker.enabledbooltrueEnable Docker systemd log collection
vector-logging.systemd.kubelet.enabledbooltrueEnable Kubelet systemd log collection
vector-logging.systemd.directorystring/var/log/journalSystemd journal directory
OpenSearch Sink Parameters
ParameterTypeDefaultDescription
vector-logging.sinks.opensearch.enabledbooltrueEnable OpenSearch sink
vector-logging.sinks.opensearch.index.prefixstring"{{ .cluster }}-logs"Index prefix
vector-logging.sinks.opensearch.index.timestringstring"%Y.%m.%d"Index time format
ISM Policy Parameters
ParameterTypeDefaultDescription
vector-logging.ism_policy.enableboolfalseEnable Index State Management policy
vector-logging.ism_policy.min_index_agestring"7d"Minimum index retention period

Log Query API

Query Logs

bash
curl -X GET "http://whizard-telemetry-apiserver.extension-whizard-telemetry.svc:80/kapis/logging.kubesphere.io/v1alpha2/logs?operation=query&log_query=error&size=10&cluster=host&sort=desc" \
  -H "X-Remote-User: admin"

Query Parameters:

ParameterTypeDefaultDescription
operationstringqueryOperation type: query/statistics/histogram/export
namespacesstringComma-separated list of namespaces
namespace_querystringFuzzy match namespace names
workloadsstringComma-separated list of workloads
workload_querystringFuzzy match workload names
podsstringComma-separated list of pods
pod_querystringFuzzy match pod names
containersstringComma-separated list of containers
container_querystringFuzzy match container names
log_querystringLog content keywords (case-insensitive)
intervalstring15mTime interval for histogram (e.g., 15m, 1h, 1d)
start_timestringStart time (seconds since epoch)
end_timestringEnd time (seconds since epoch)
sortstringdescSort order: asc/desc
fromint0Offset
sizeint10Number of results
clusterstringhostCluster name
exportLineLimitintMax lines for export

Extension Operations

Check Extension Status

bash
kubectl get installplan whizard-logging
kubectl get extensionversions -l kubesphere.io/extension-ref=whizard-logging

Uninstall Extension

Uninstall from all clusters:

bash
kubectl delete installplan whizard-logging

Uninstall from specific cluster:

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

yaml
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
  name: whizard-logging
spec:
  extension:
    name: whizard-logging
    version: <VERSION>
  enabled: true
  upgradeStrategy: Manual
  clusterScheduling:
    placement:
      clusters:
        - <REMAINING_CLUSTERS>  # Remove the cluster you want to uninstall from

Frequently asked questions

What does the Whizard Logging AI skill do?

Use when working with WizTelemetry Logging extension for KubeSphere, including installation, configuration, and log query API

Why use Whizard Logging on TypingMind?

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

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

Which AI models can use Whizard Logging?

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 Whizard Logging?

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

Is the Whizard Logging 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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