Kubernetes Operator

SkillCloud & infra

This skill gives your AI the know-how to build and review Kubernetes operators, the custom controllers that keep a resource's actual state matching what you declared. Once added, your AI can help you design custom resources, write the reconcile loop, and check the finished operator before it ships.

Available today. Use it from your connected AI after setup.

Add the skill, then ask your AI to build an operator or design a custom resource for whatever you want managed. Once it produces something, ask it to lint the reconcile loop or run a capability audit.

Then ask your AI: use the Kubernetes Operator skill

What your AI can do with it

  • Design custom resources and validate their schemas
  • Write and improve reconcile loops
  • Work with common operator frameworks such as controller-runtime, kubebuilder, operator-sdk, metacontroller, and KOPF
  • Lint reconcile loops for problems before you merge
  • Run capability audits on an operator

What this skill tells your AI

The instructions your AI receives, as published by borghei/claude-skills in engineering/kubernetes-operator/SKILL.md and read by ahel’s review.

End-to-end Kubernetes operator design and construction. Covers the operator pattern (control loops for stateful workloads), CRD design (schema, validation, conversion, status), the reconciliation loop (idempotency, convergence, level- vs edge-triggered), framework selection (controller-runtime / Kubebuilder / operator-SDK / metacontroller), and operational concerns (finalizers, leader election, RBAC scoping, status subresource, observability). Targets Go-based operators (the dominant ecosystem) with notes on alternatives (KOPF, JOSDK, kube-rs).

Core Capabilities

  • Decide whether to write an operator — operator vs Helm vs GitOps vs admission webhook vs Crossplane, and when NOT to build one at all.
  • CRD design — spec/status separation, OpenAPI v3 structural schemas, CEL cross-field validation, versioning, conversion webhooks, status subresource, printer columns, scale subresource.
  • Reconciliation loops — idempotent, level-triggered, converging control loops; phase machines vs always-converge; owner references and garbage collection.
  • Controller-runtime patterns (Go) — skeleton reconcilers, server-side apply, finalizers, leader election, watches/predicates/indexers, error classification, envtest.
  • Framework selection — controller-runtime, Kubebuilder, operator-SDK, Metacontroller, KOPF, JOSDK, kube-rs.
  • Operational hardening — finalizers, leader election, tightened RBAC, status conditions/observedGeneration, Prometheus metrics + structured logging.
  • Anti-pattern audit — 24-entry catalog with severities, detection heuristics, and fixes for production-readiness review.

When to Use

SituationSkill applies
Building a new operator for an internal platform primitiveYes — start with the operator pattern decision
Auditing an existing operator for production-readinessYes — use anti-patterns + scripts/reconciliation_audit.py
Designing CRDs for a custom resourceYes — use CRD design + scripts/crd_validator.py
Deciding "operator vs Helm chart vs plain manifests"Yes — use the decision matrix
Scaffolding a new operator projectYes — scripts/operator_scaffold.py
Debugging a controller that "isn't reconciling"Yes — use reconciliation troubleshooting
Just running someone else's operator (Postgres, Kafka, etc.)Partially — useful for understanding what it does and how to monitor it

Clarify First

Before scaffolding or auditing, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Task — scaffold an operator, validate a CRD, or audit a controller (selects operator_scaffold.py vs crd_validator.py vs reconciliation_audit.py)
  • Resource identity — for scaffolding: the operator name, API group, and kind (sets --name/--group/--kind); for validation/audit: the CRD YAML or controller path (the input the scripts read)
  • Framework — controller-runtime/Kubebuilder/operator-SDK (Go), or KOPF/JOSDK/kube-rs (drives the scaffold patterns)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

ToolPurposeCommand
crd_validator.pyValidate CRD YAML against design best practices (preserve-unknown, missing descriptions/enums/printer-columns, status subresource, cluster-scope)python3 scripts/crd_validator.py --schema my-crd.yaml --format markdown
operator_scaffold.pyGenerate a production-ready operator project skeleton with stricter RBAC, observability, and finalizer scaffoldingpython3 scripts/operator_scaffold.py --name db-operator --group example.com --kind Database
reconciliation_audit.pyAudit Go controller source + CRDs for static-detectable anti-patterns (missing finalizers, no leader election, tight loops, no ownerRef, wide RBAC)python3 scripts/reconciliation_audit.py --controller-path ./internal/controllers --crd ./config/crd/bases/*.yaml

All scripts: stdlib only, argparse CLI, JSON or markdown output.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

  • references/operator-pattern-and-crds.md — pattern fundamentals, full CRD schema design (validation, CEL, status patterns), versioning, conversion webhooks, status/scale subresources, printer columns, RBAC, and a production-CRD checklist. Read when designing or reviewing a CRD.
  • references/controller-runtime-patterns.md — Go controller-runtime examples: skeleton reconciler, idempotent apply, owner refs, status updates, finalizers, leader election, watches/predicates/indexers, error handling, RBAC markers, envtest, plus the reconciliation-loop overview and operational concerns. Read when implementing or debugging a controller.
  • references/operator-anti-patterns.md — the full 24-entry anti-pattern catalog with severity, symptoms, consequences, fixes, a static-detection summary, and symptom-based triage. Read during design review, code review, or pre-production audit.
  • references/operator-decisions-and-workflows.md — when NOT to write an operator, operator-vs-alternatives decision matrix, framework selection, the four end-to-end workflows (scaffold / audit / design CRD / upgrade versions), and tooling outputs. Read when deciding the approach or running a workflow.

Scope & Limitations

Covers: operator pattern decisions; CRD design (schema/validation/versioning/conversion/subresources); idempotent reconciliation loops; Go controller-runtime / Kubebuilder / operator-SDK patterns; finalizers, leader election, RBAC scoping, observability; anti-pattern auditing. Primary target is Go operators, with notes on KOPF (Python), JOSDK (Java), kube-rs (Rust).

Does NOT cover: operating third-party community operators beyond understanding/monitoring them; cloud-provider-specific resource provisioning (see Crossplane); general Kubernetes cluster administration.

Integration Points

SkillIntegration
engineering/chaos-engineeringChaos-test operators (kill the controller, partition from API server)
engineering/observability-designerWire metrics + logging for operators
engineering/incident-commanderOperators amplify blast radius; incident response matters more
engineering/feature-flags-architectOperators with spec.feature.<x>.enabled fields effectively become flag systems; consider the trade-off

Signals

GitHub stars
752
Forks
137
Last commit
Aug 2026

ahel review

  • S4info
    community integration — published by borghei, not kubernetes

Automated review, not a security audit. Ruleset v1+k2.

Advanced
Catalog kind
skill
Gateway key
kubernetes-operator
Source
github.com/borghei/claude-skills
Kubernetes Operator (kubernetes-operator): Skill · ahel