Helm Chart

You are a Kubernetes and Helm packaging expert. The user wants to create a production-ready Helm chart for their application.

What to check first

• Run helm version to confirm Helm 3.x is installed
• Verify your application has a working container image in a registry (e.g., Docker Hub, ECR, or local)
• Confirm you have kubectl configured and a target Kubernetes cluster accessible

Steps

1. Create the chart scaffold with helm create myapp — this generates the standard directory structure
2. Edit Chart.yaml and set name, description, type: application, version, and appVersion fields
3. Modify values.yaml to expose all configurable parameters: image repository/tag, replicas, resources (cpu/memory limits), service type, and ingress settings
4. Update templates/deployment.yaml to reference values using {{ .Values.image.repository }}:{{ .Values.image.tag }} and add environment variables via env: section
5. Create templates/service.yaml with selector: app: {{ include "myapp.selectorLabels" . }} to match pod labels
6. Add templates/ingress.yaml if exposing via HTTP, using {{ .Values.ingress.hosts }} from values
7. Test the chart locally with helm template myapp . --values values.yaml to validate YAML output
8. Lint the chart with helm lint . to catch formatting and best-practice violations
9. Package the chart with helm package . to create a .tgz archive
10. Install to a test cluster using helm install myapp . --namespace default --values values.yaml

Code

# Chart.yaml
apiVersion: v2
name: myapp
description: Production Helm chart for myapp
type: application
version: 1.0.0
appVersion: "1.0"
maintainers:
  - name: DevOps Team
    email: devops@example.com

---
# values.yaml
replicaCount: 3

image:
  repository: myregistry/myapp
  pullPolicy: IfNotPresent
  tag: "1.0.0"

service:
  type: ClusterIP
  port: 80
  targetPort: 8080

ingress:
  enabled: true
  className: nginx
  hosts:
    - host: myapp.example.com
      paths:
        - path: /
          pathType: Prefix

resources:
  limits:
    cpu: 500m
    memory: 512Mi
  requests:
    cpu: 250m
    memory: 256Mi

autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80

---
# templates/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name:

Note: this example was truncated in the source. See the GitHub repo for the latest full version.

Common Pitfalls

• Treating this skill as a one-shot solution — most workflows need iteration and verification
• Skipping the verification steps — you don't know it worked until you measure
• Applying this skill without understanding the underlying problem — read the related docs first

When NOT to Use This Skill

• When a simpler manual approach would take less than 10 minutes
• On critical production systems without testing in staging first
• When you don't have permission or authorization to make these changes

How to Verify It Worked

• Run the verification steps documented above
• Compare the output against your expected baseline
• Check logs for any warnings or errors — silent failures are the worst kind

Production Considerations

• Test in staging before deploying to production
• Have a rollback plan — every change should be reversible
• Monitor the affected systems for at least 24 hours after the change