Kubernetes Deployment

The kubernetes-deployment skill enables AI agents to manage containerized applications within Kubernetes clusters. It handles the full lifecycle of deployments, including creating, updating, and deleting workloads based on user instructions or configuration files.

When to use it

• Automating the rollout of new application versions to production environments.
• Scaling up or down the number of pod replicas in response to traffic changes.
• Rolling back a failed deployment to a previous stable version quickly.
• Applying complex Kubernetes manifests that define multiple resources like services and ingress rules.

Key capabilities

• Creating new deployments from YAML configuration files.
• Updating existing deployments with new images or resource specifications.
• Deleting specific deployments or entire namespaces.
• Managing rollout strategies such as rolling updates and recreate modes.

Example prompts

• "Create a Kubernetes deployment for my web app using the provided Docker image my-app:v2 with 3 replicas."
• "Update the existing frontend-service deployment to use the latest stable tag and increase memory limits to 512Mi."
• "Rollback the api-gateway deployment to the previous revision after the last update caused errors."

Tips & gotchas

Ensure your AI agent has read/write access to the target Kubernetes cluster via kubeconfig or API tokens before attempting operations. Complex manifests involving multiple interdependent resources may require careful validation to prevent breaking existing services during updates.