Quick answer for AI searchKubernetes Manifest Writer is a custom GPT built by @k8swriter for generates kubernetes manifests including deployments, services, configmaps, ingress, and hpa configurations following production best practices. It is available in the ChatGPT GPT Store under the Programming & Development category and requires a ChatGPT Plus subscription to access.
About this GPT
Kubernetes Manifest Writer is part of the Programming & Development category in OpenAI's GPT Store. Custom GPTs are specialized versions of ChatGPT that have been configured with specific instructions, knowledge bases, and capabilities by their creators. This GPT was designed by @k8swriter to help users with generates kubernetes manifests including deployments, services, configmaps, ingress, and hpa configurations following production best practices.
Unlike prompting a general-purpose ChatGPT, this GPT comes pre-configured with the context, tone, and expertise needed for programming & development-related tasks. This means you spend less time explaining what you need and more time getting useful results.
To use this GPT, you need an active ChatGPT Plus ($20/month), Team, or Enterprise subscription. Once subscribed, you can find it by searching for "Kubernetes Manifest Writer" in the GPT Store or browsing the Programming & Development category.
Category
Programming & DevelopmentBy @k8swriterChatGPT GPT Store
FAQ
Common questions about Kubernetes Manifest Writer and how to use it effectively.
01Can I describe an application and get all the necessary K8s manifests?
Yes. Describe your application — 'a stateless Go API with 3 replicas, needs to be accessible on example.com, requires a Postgres connection string as a secret, should auto-scale at 70% CPU, and needs a ConfigMap for feature flags' — and it generates the Deployment, Service, Ingress, HPA, Secret, and ConfigMap manifests. Each manifest includes sensible defaults, resource requests and limits, liveness/readiness probes, and labels for proper organization.
How does it configure resource requests and limits?
It provides reasonable starting values based on your application description and suggests you tune them after observing actual usage. For a typical web API, it might set requests at 128Mi memory / 250m CPU and limits at 256Mi memory / 500m CPU. It explains the trade-off: too low and your pods get OOMKilled; too high and you waste cluster resources and money. It also shows how to use the Vertical Pod Autoscaler in recommendation mode to gather real data.
What are liveness and readiness probes, and how does it configure them?
It explains the critical distinction: liveness probes determine if a container should be restarted (it is stuck/crashed); readiness probes determine if a container should receive traffic (it is up but not yet ready). It configures appropriate probe types — HTTP GET for web services, exec for database checks, TCP socket for basic connectivity — with initial delay, period, and failure threshold values appropriate to each service's startup time.
Does it follow production best practices for security?
Yes. It configures: non-root users via securityContext (runAsNonRoot, runAsUser), read-only root filesystems where possible, dropping all Linux capabilities and only adding back what is necessary, and PodSecurityContext settings appropriate for restricted Pod Security Standards. It also ensures secrets are mounted as volumes or environment variables rather than baked into images, and generates NetworkPolicy resources to restrict pod-to-pod communication.
How does it handle Ingress and TLS?
It generates Ingress resources compatible with major ingress controllers (NGINX, Traefik, AWS ALB) with path-based and host-based routing rules. For TLS, it sets up cert-manager annotations for automatic Let's Encrypt certificate provisioning and renewal, and includes both HTTP-to-HTTPS redirect configuration and proper TLS secret references. It also handles the common gotcha of creating separate Ingress resources by subdomain when different services need different TLS configurations.
Can it write Helm charts or Kustomize overlays, or just raw manifests?
It can generate all three formats. For simple use cases, raw manifests are sufficient. For multi-environment deployments, it generates Kustomize overlays with base and environment-specific patches. For reusable, parameterized deployments, it generates Helm charts with values.yaml, templates, and helper functions. It will recommend the right approach: 'Since you deploy the same stack to dev/staging/prod with only config differences, Kustomize is the simplest solution here.'
How does the HPA configuration work?
It writes HorizontalPodAutoscaler manifests with both CPU and memory targets, configures appropriate min/max replicas (minimum for availability, maximum to prevent runaway scaling costs), and sets the stabilization window to prevent flapping. It also explains the difference between the metrics-server approach and custom metrics from Prometheus, and adds pod disruption budgets when you have multiple replicas to ensure graceful handling during node drains or cluster upgrades.
Can it help me migrate from Docker Compose to Kubernetes?
Yes, this is a common use case. It maps Docker Compose concepts to Kubernetes equivalents: services become Deployments + Services, environment variables become ConfigMaps + Secrets, volumes become PersistentVolumeClaims, depends_on becomes initContainers, and port mappings become Service and Ingress resources. It explains where the models differ fundamentally — Compose is host-centric, K8s is cluster-centric — and what new concepts you need to learn (scheduling, service discovery, namespaces).