K8sGPT - Giving Kubernetes Superpowers to Everyone

Overview

Custom analyzers allow you to extend K8sGPT's capabilities by creating your own analysis rules and checks. This enables you to add domain-specific knowledge and custom validation logic to your Kubernetes cluster analysis.

Creating a Custom Analyzer

Custom analyzers are written in Go and follow a simple interface. Here's a basic example:

package analyzers

import (
    "context"
    "fmt"
    "k8s.io/client-go/kubernetes"
)

type CustomAnalyzer struct {
    client *kubernetes.Clientset
}

func (a *CustomAnalyzer) Analyze(ctx context.Context) ([]Analysis, error) {
    // Your analysis logic here
    return []Analysis{
        {
            Name: "custom-check",
            Message: "Custom analysis result",
            Severity: "warning",
        },
    }, nil
}

Analyzer Interface

Every custom analyzer must implement the following interface:

Analyze(ctx context.Context) ([]Analysis, error)
Name() string
Description() string

Configuration

To use your custom analyzer:

Build your analyzer as a Go plugin
Place the plugin in the analyzers directory
Configure K8sGPT to load your analyzer
Enable the analyzer in your analysis configuration

Best Practices

Keep analyzers focused and single-purpose
Include clear documentation and examples
Handle errors gracefully
Use appropriate severity levels
Test thoroughly before deploying

Example Use Cases

Custom resource validation rules
Organization-specific best practices
Integration with internal tools
Specialized security checks
Performance optimization rules

Next Steps

After creating your custom analyzer:

Test it in the playground
Deploy it to your cluster
Monitor its performance
Share it with the community

Custom Analyzers