k8s-manifest-generator

Overview of k8s-manifest-generator skill

What k8s-manifest-generator does

The k8s-manifest-generator skill helps an agent produce Kubernetes YAML for common workload building blocks: Deployment, Service, ConfigMap, Secret, and PersistentVolumeClaim. Its value is not just “write some YAML.” It pushes the output toward production-shaped manifests with labels, rollout settings, health checks, resource controls, and security defaults that many quick prompts forget.

Who should use this skill

This skill is best for people who already know the application they want to run, but do not want to hand-author every manifest detail from scratch. It fits:

• platform and DevOps engineers standardizing app deployments
• developers deploying one service at a time
• teams that want a solid Kubernetes starting point for review and refinement

It is especially useful if you need k8s-manifest-generator for Deployment plus a matching Service and config objects in one pass.

The real job-to-be-done

Most users are not looking for generic Kubernetes explanations. They want a usable first draft that is structurally sound, reviewable, and closer to “safe by default” than a normal LLM prompt. The practical job is: turn app requirements into manifests that can survive team review, not merely compile as YAML.

Why this skill is better than a plain prompt

The repository includes reusable templates in assets/ and spec references in references/, which makes the skill more grounded than a freeform “generate Kubernetes manifests” request. The deployment template bakes in details users often miss, such as:

• rolling update strategy
• readiness for zero-downtime rollout
• pod security context
• consistent labels and annotations
• probes, service account, and resource-thinking

That makes the k8s-manifest-generator skill a better install candidate if you care about output structure and not just speed.

Main limitations to know before installing

k8s-manifest-generator is a manifest authoring aid, not a cluster-specific deployment framework. It does not replace:

• Helm chart design
• Kustomize overlays
• policy validation
• provider-specific networking decisions
• GitOps packaging conventions

If your main need is environment orchestration, template reuse across dozens of services, or CRD-heavy platforms, this skill is more of a drafting layer than the final system.

How to Use k8s-manifest-generator skill

Install context for k8s-manifest-generator

The repository excerpt does not expose a built-in install command inside SKILL.md, so use your normal skill installation flow for the wshobson/agents repository and select k8s-manifest-generator. If your tool supports direct GitHub skill installs, target:

https://github.com/wshobson/agents/tree/main/plugins/kubernetes-operations/skills/k8s-manifest-generator

For install decisions, what matters most is that this skill is self-contained and supported by concrete files:

• SKILL.md
• assets/deployment-template.yaml
• assets/service-template.yaml
• assets/configmap-template.yaml
• references/deployment-spec.md
• references/service-spec.md

Read these files first

If you want the fastest path to effective k8s-manifest-generator usage, read in this order:

1. SKILL.md for the workflow and required inputs
2. assets/deployment-template.yaml for the opinionated production baseline
3. assets/service-template.yaml to choose exposure type correctly
4. assets/configmap-template.yaml for config shape patterns
5. references/deployment-spec.md and references/service-spec.md when you need field-level justification

This path gives you both the “what” and the “why” before you ask the agent to generate anything.

What inputs the skill needs

The skill works best when you supply workload facts, not just a resource kind. Useful inputs include:

• app name and namespace
• container image and tag
• ports exposed by the app
• stateless or stateful workload type
• desired replica count
• CPU and memory requests/limits
• health endpoints for liveness/readiness
• whether traffic is internal or external
• config values that belong in ConfigMap
• secrets that must stay in Secret
• storage needs and mount paths

If you omit these, the agent can still draft YAML, but you will get more placeholders, weaker probe choices, and generic networking decisions.

How to ask for a Deployment well

Weak prompt:

• “Create Kubernetes manifests for my app.”

Stronger prompt:

• “Use k8s-manifest-generator to create a production-ready Deployment, internal ClusterIP Service, ConfigMap, and Secret for a stateless API named billing-api in namespace payments. Image: ghcr.io/acme/billing-api:1.4.2. Container listens on 8080. Readiness endpoint /ready, liveness endpoint /health. Start with 3 replicas. Requests: 250m CPU, 256Mi; limits: 1 CPU, 512Mi. Inject non-secret env via ConfigMap, database credentials via Secret, and use secure pod/container settings.”

That version gives the skill enough context to produce something materially better.

Best prompt shape for k8s-manifest-generator for Deployment

When generating a Deployment, include these five blocks in your request:

1. workload identity: name, namespace, image, version
2. runtime behavior: ports, commands, env vars, health checks
3. scaling and rollout: replicas, downtime tolerance
4. security: non-root requirement, service account, secret handling
5. connectivity and storage: service type, PVC needs, config mounts

This mirrors the skill’s own workflow and reduces back-and-forth.

Workflow that gets the best output

A practical k8s-manifest-generator guide looks like this:

1. describe the app in plain operational terms
2. ask for only the needed resource set
3. review labels, selectors, ports, and probes first
4. review security context and secret placement second
5. then adapt for your environment, ingress model, and deployment tooling

Do not start by tuning minor annotations. The highest-risk errors are usually mismatched selectors, bad port mapping, missing probes, or incorrect exposure type.

Choosing the right Service type

The service template includes multiple exposure patterns, which is useful because this is where many generated manifests go wrong:

• use ClusterIP for internal-only app-to-app traffic
• use LoadBalancer when your cloud should provision external access
• use NodePort mainly for simple dev or constrained environments

If you do not specify this in your prompt, the agent may choose a type that is valid YAML but wrong for your network model.

What the templates tell you about the skill's defaults

The included templates show that k8s-manifest-generator tends toward production-minded defaults:

• multiple replicas
• rolling updates with low disruption
• stable labels and metadata
• pod security context
• metrics annotations
• named ports and service selectors
• config split into dedicated resources

That is good for realism, but it also means you should explicitly ask for dev-friendly simplifications if you do not want production assumptions.

Practical review checklist before applying YAML

Before using generated output, verify:

• selector.matchLabels equals pod template labels
• Service.spec.selector matches the workload labels
• targetPort matches a real container port or named port
• probes hit valid endpoints
• requests and limits fit the app
• secrets are not placed in ConfigMap
• namespace and service account actually exist
• PVC only appears if storage is truly required

This is where the k8s-manifest-generator skill saves time, but not where it removes responsibility.

When this skill works especially well

Use k8s-manifest-generator usage when you need:

• a first-pass baseline for a new service
• a reviewable manifest set for an internal API
• better defaults than a generic chat prompt
• a quick conversion from app requirements to Kubernetes objects

It is a strong fit for single-service or small multi-resource generation.

When not to rely on it alone

Do not expect this skill by itself to solve:

• Helm value abstraction
• multi-environment overlays
• ingress controller specifics
• autoscaling policy design
• PodDisruptionBudget, NetworkPolicy, or RBAC design unless you ask for them explicitly
• cluster-policy compliance in restricted environments

Those concerns usually need extra prompts or downstream tooling.

k8s-manifest-generator skill FAQ

Is k8s-manifest-generator good for beginners?

Yes, if you already know basic Kubernetes object names. The templates and references give a safer starting point than a blank prompt. But beginners should still validate every field, because the skill is optimized for generation quality, not step-by-step teaching.

Does k8s-manifest-generator only generate Deployments?

No. The repository explicitly supports Deployment, Service, ConfigMap, Secret, and PersistentVolumeClaim workflows. Still, k8s-manifest-generator for Deployment is the clearest strength because the deployment template is the most opinionated and operationally rich file.

How is this different from asking an LLM for Kubernetes YAML?

A plain prompt often produces syntactically valid but operationally thin manifests. This skill has a clearer workflow, stronger defaults, and supporting reference files. That usually means fewer missing labels, better probe handling, and more realistic deployment settings.

Is k8s-manifest-generator install worth it for experienced Kubernetes users?

Usually yes, if you want speed and consistency. Experienced users can treat it as a drafting accelerator, then layer in organization-specific rules. If you already have mature Helm charts or Kustomize bases, the value is lower.

Can I use it for cloud-specific setups?

Partly. The service template includes cloud-flavored LoadBalancer annotations, which shows the skill can lean provider-aware. But you should still provide your platform details explicitly, because provider networking and ingress conventions vary a lot.

Will it generate production-ready manifests without edits?

Not safely in every case. The output can be production-shaped, but “production-ready” depends on your cluster policies, observability, secret management, storage class, ingress, and security controls. Treat the first output as a strong draft, not an auto-apply artifact.

Is this the right skill for Helm or Kustomize repos?

It is useful upstream of them. Generate the raw manifests first, then translate into Helm templates or Kustomize bases if needed. If your main problem is reusable packaging rather than manifest content, this skill is only part of the answer.

How to Improve k8s-manifest-generator skill

Give the skill operational facts, not app marketing

The biggest improvement lever is input quality. Instead of “deploy my service,” provide:

• exact image
• real port numbers
• health endpoints
• expected traffic direction
• storage requirements
• runtime configuration split between secret and non-secret data

The more operational your request, the less placeholder-heavy the output.

Ask for the exact resource bundle you need

If you only need a Deployment and internal Service, say so. If you also need a ConfigMap, Secret, and PVC, list them explicitly. This reduces unnecessary YAML and makes review easier.

State your environment assumptions early

Useful examples:

• “EKS with external LoadBalancer access”
• “internal-only cluster traffic”
• “single-namespace staging deployment”
• “restricted cluster requiring non-root containers”

This changes manifest quality more than cosmetic prompt detail.

Prevent common failure modes

Common weak-output patterns include:

• missing or generic probes
• wrong Service type
• selectors that do not line up
• secrets mixed into config
• unrealistic resource settings
• production defaults applied to local dev without adjustment

You can prevent most of these by including one sentence per concern in the initial prompt.

Improve k8s-manifest-generator output with a two-pass workflow

A reliable method:

1. first ask k8s-manifest-generator for the core manifests
2. then ask the agent to critique them for label consistency, rollout safety, security context, and service exposure choices

This second pass catches more real issues than endlessly expanding the first prompt.

Use the asset templates as quality anchors

If the first output feels too generic, explicitly tell the agent to align with:

• assets/deployment-template.yaml for rollout and security structure
• assets/service-template.yaml for service exposure patterns
• assets/configmap-template.yaml for config organization

That nudges generation toward the repository’s strongest material instead of a generic model prior.

Ask for rationale when you expect review friction

If teammates will review the YAML, ask the agent to include short comments or a brief rationale for:

• replica count
• probe choices
• resource settings
• service type
• security context

This makes the k8s-manifest-generator guide more useful in real engineering workflows, not just in isolated generation.

Iterate after the first draft with targeted edits

Do not regenerate everything if only one part is wrong. Ask focused follow-ups such as:

• “Change the Service from LoadBalancer to ClusterIP.”
• “Add a PVC mounted at /data.”
• “Move non-secret env vars into a ConfigMap.”
• “Tighten the security context for a non-root container.”

Targeted iteration preserves the good parts and converges faster.

Know when to graduate beyond k8s-manifest-generator

If your recurring needs are environment overlays, chart packaging, policy enforcement, or organization-wide golden paths, use k8s-manifest-generator as a drafting step and then move into your standard platform tooling. The skill is strongest at creating a solid manifest baseline, not at replacing your deployment system.