kubernetes-operator
by alirezarezvaniUse kubernetes-operator to design and review Kubernetes Operators, CRDs, and reconcile loops. The skill includes CRD design references, a controller-runtime reconcile skeleton, a production CRD template, and Python audits for CRD validation, reconcile linting, and OperatorHub-style capability checks.
This skill scores 84/100, making it a solid listing candidate for directory users who want focused help building or reviewing Kubernetes Operators. It provides enough trigger guidance, reusable templates, domain-specific checks, and explanatory references to reduce guesswork beyond a generic Kubernetes prompt, though adoption would be easier with clearer installation and quick-start instructions.
- Strong triggerability: the frontmatter explicitly names operator-building intents and keywords such as CRD design, reconcile loop, controller-runtime, kubebuilder, operator-sdk, KOPF, and custom resource.
- Good operational leverage: it ships concrete assets including a production CRD template, a Go reconcile skeleton, and scripts for CRD validation, reconcile-loop linting, and capability auditing.
- Useful progressive reference material: the four references cover when to build an operator, CRD design, reconcile-loop structure, and framework/tooling selection.
- No install command or README is present in the skill path, so users may need to rely on the broader repository's installation conventions.
- The included validators/linter are described as stdlib-only and useful for best-practice checks, but the excerpts show they are heuristic rather than full Kubernetes/API validation.
Overview of kubernetes-operator skill
What the kubernetes-operator skill helps you build
The kubernetes-operator skill is for designing, generating, and reviewing Kubernetes Operators: custom controllers that reconcile a Custom Resource’s desired state into real cluster resources. It is not a general Kubernetes helper. Its strongest use is catching operator-specific design problems before they become cluster bugs: weak CRD schemas, missing status conditions, finalizer mistakes, non-idempotent reconcile logic, poor requeue handling, and RBAC or capability gaps.
Best-fit users and Cloud Architecture work
This skill is a good fit for platform engineers, SREs, DevOps teams, and Cloud Architecture teams building internal platforms on Kubernetes. Use it when you are turning operational runbooks into a Kubernetes API, comparing kubebuilder, operator-sdk, controller-runtime, KOPF, or metacontroller, or auditing whether an operator is production-ready enough for internal use or OperatorHub-style expectations.
What makes it different from a normal prompt
A generic prompt can explain the operator pattern, but this skill gives the assistant concrete operator guardrails: CRD design references, a reconcile-loop skeleton, a production CRD template, and stdlib Python scripts for validation. That makes it better for deterministic review tasks such as “does this CRD expose status correctly?” or “does this reconcile loop safely handle deletion and transient errors?”
When this skill is the wrong choice
Do not install kubernetes-operator if you only need a Helm chart, Deployment, CronJob, or one-time migration Job. It is also not the best first stop for broad Kubernetes troubleshooting, cluster networking, storage classes, or generic YAML cleanup unless the work is directly tied to a Custom Resource and controller.
How to Use kubernetes-operator skill
kubernetes-operator install and first files to inspect
Install the skill in a compatible agent environment with:
npx skills add alirezarezvani/claude-skills --skill kubernetes-operator
After install, read SKILL.md first to understand triggers and workflow. Then inspect:
references/operator_pattern.mdfor build-vs-don’t-build judgmentreferences/crd_design.mdbefore writing the API surfacereferences/reconcile_loop.mdbefore implementing controller logicreferences/tooling_landscape.mdbefore choosing a frameworkassets/crd_template.yamlandassets/reconcile_skeleton.gofor starting structuresscripts/crd_validator.py,scripts/reconcile_lint.py, andscripts/operator_capability_audit.pyfor review checks
Inputs that produce better kubernetes-operator usage
Give the skill more than “build an operator.” Strong inputs include the managed application, lifecycle operations, failure modes, desired CR fields, status fields, namespace model, cloud dependencies, and chosen framework.
Weak prompt:
Build a Kubernetes operator for PostgreSQL.
Stronger prompt:
Use the kubernetes-operator skill to design a
DatabaseClusterCRD for a namespaced PostgreSQL operator. It must support version upgrades, replica count, backup schedule, restore source, storage size, and maintenance window. We are usingkubebuilderandcontroller-runtime. Include status conditions, printer columns, finalizer behavior, reconcile phases, RBAC notes, and risks that would make this better as Helm instead of an operator.
The stronger version tells the skill what operational knowledge must be encoded, which framework constraints matter, and what tradeoffs to check.
Practical workflow for new operator design
Start with a fit check: confirm the application needs lifecycle automation beyond Helm. Next, design the CRD before writing controller code; bad API shape is expensive to change after users adopt it. Use assets/crd_template.yaml as a baseline and adapt fields deliberately, especially spec, status.conditions, subresources.status, versioning, and printer columns.
Then draft the reconcile loop from assets/reconcile_skeleton.go. Ask the assistant to map each CR field to idempotent actions, status updates, requeue behavior, and finalizer cleanup. Run or review against the included scripts where applicable, then iterate on the findings rather than treating generated code as final.
Using the audit scripts effectively
The scripts are lightweight checks, not a replacement for cluster tests. crd_validator.py looks for structural CRD design issues such as missing status subresource, missing conditions, weak version policy, and incomplete naming. reconcile_lint.py is useful when reviewing controller-runtime-style code for common reconcile mistakes. operator_capability_audit.py helps reason about maturity gaps such as install, upgrade, observability, backup, recovery, and lifecycle behavior.
Use script output as a punch list: ask the skill to explain each finding, classify severity, and propose a minimal fix that preserves your API compatibility.
kubernetes-operator skill FAQ
Is kubernetes-operator suitable for beginners?
Yes, if you already understand basic Kubernetes objects and want a guided operator-specific path. It will not teach all Kubernetes fundamentals from scratch, but it does make the operator pattern more concrete by focusing on CRD shape, reconcile flow, status, finalizers, and framework choice.
How is this different from asking for kubebuilder help?
kubebuilder scaffolds a Go operator project. The kubernetes-operator skill helps decide what the operator should do, whether it should exist, how the CRD should look, and whether the reconcile logic follows safe controller patterns. In practice, use both: kubebuilder for project structure, this skill for design and review discipline.
Can I use it for Python, Java, or non-Go operators?
Yes for architecture, CRD design, lifecycle modeling, and framework comparison. The included Go reconcile skeleton is most directly aligned with controller-runtime, but the reconcile principles still apply to KOPF, Java Operator SDK, and webhook-based approaches such as metacontroller.
When should I avoid this skill?
Avoid it when your goal is ordinary manifest generation, cluster debugging, service mesh setup, or application deployment that does not require a Custom Resource and controller. For many stateless apps, a Helm chart plus good defaults is simpler, cheaper, and easier to operate than an operator.
How to Improve kubernetes-operator skill
Improve kubernetes-operator results with stronger context
The skill performs best when you provide architectural constraints, not just code. Include Kubernetes version assumptions, target distribution such as vanilla Kubernetes or OpenShift, namespace vs cluster scope, multi-tenancy needs, cloud provider dependencies, required lifecycle operations, and what users should see in kubectl get. This helps the assistant design APIs and status fields that match real operations.
Common failure modes to ask the skill to check
Ask explicitly for operator-specific review points:
- Is the reconcile loop idempotent?
- Are transient errors returned so the request requeues?
- Are finalizers added and removed safely?
- Is
statusupdated separately fromspec? - Are conditions using
observedGeneration? - Does the CRD have structural schema, status subresource, and printer columns?
- Is the RBAC narrower than “cluster-admin”?
These checks make kubernetes-operator usage more concrete than a broad architecture prompt.
Iterate from design to implementation to audit
A good workflow is three passes. First, ask for an API and lifecycle design. Second, ask for reconcile pseudocode or framework-specific implementation guidance. Third, run the CRD, controller, or design through an audit prompt using the repository scripts and references as criteria. Keep each pass focused; mixing CRD design, RBAC, generated code, and production readiness in one prompt often produces shallow output.
Customize the skill for your platform standards
To improve the kubernetes-operator skill for Cloud Architecture teams, add your organization’s conventions: approved API groups, naming rules, condition types, backup requirements, observability standards, security boundaries, and supported frameworks. If you already have golden operators, ask the assistant to compare new designs against them so the skill produces platform-consistent recommendations instead of generic operator advice.