Skills Kubernetes Operator

Kubernetes Operator

Build operators that reconcile correctly. Most operator bugs are not Kubernetes bugs — they are reconcile-loop bugs: missing finalizers, blocking calls, no requeue on transient errors, status drift, RBAC over-grants. This skill catches them deterministically before they reach a cluster.

When to use

• Building a new Kubernetes Operator (controller for a CRD)
• Reviewing an existing operator for capability-level gaps
• Auditing a CRD spec for status/conditions/finalizer correctness
• Choosing a framework (controller-runtime / kubebuilder / operator-sdk / metacontroller / KOPF)
• Designing the API surface of a Custom Resource
• Hardening RBAC, leader election, or webhook validation

When NOT to use

• Plain Helm chart packaging → use helm-chart-builder
• Standard kubectl operations / blue-green deploys → use senior-devops
• General k8s security posture → use cloud-security
• "I want to run a workload" — that's a Deployment / Job, not an operator

Core principle: an operator is a reconcile loop, not a script

observe(actual) → desired = read(spec) → diff(actual, desired) → act → update(status)
                                                                          ↓
                                                                   requeue / done

Operators that fail are the ones that:

1. Treat reconcile as imperative (do this, then this, then this) instead of declarative (make actual=desired, idempotently)
2. Don't requeue transient failures
3. Don't use finalizers, leaving orphan resources
4. Mutate spec instead of status
5. Don't use the status subresource (status updates trigger spec reconciles → loop)
6. Block in reconcile (long HTTP calls, locks)
7. Forget leader election → split-brain on multi-replica deploys

The 3 tools below catch each of these.

Quick start

SKILL=engineering/kubernetes-operator/skills/kubernetes-operator

# Validate a CRD design
python "$SKILL/scripts/crd_validator.py" --crd config/crd/myapp.yaml

# Lint a Go reconcile function
python "$SKILL/scripts/reconcile_lint.py" --controller controllers/myapp_controller.go

# Score against OperatorHub Capability Levels (1-5)
python "$SKILL/scripts/operator_capability_audit.py" --operator-dir .

The 3 Python tools

All stdlib-only. Run with --help.

crd_validator.py

Validates a CRD YAML against operator-pattern best practices.

python scripts/crd_validator.py --crd config/crd/myapp.yaml
python scripts/crd_validator.py --crd config/crd/ --format json

Checks:

• spec.versions[*].subresources.status is set (status subresource)
• spec.scope is Namespaced (not Cluster) unless explicitly justified
• Singular and listKind defined
• spec.versions[*].schema.openAPIV3Schema has type definitions (no x-kubernetes-preserve-unknown-fields: true at top level)
• A version is marked served: true AND storage: true
• Conditions array is in the schema (allows metav1.Conditions)
• Printer columns include Age and Status/Phase

reconcile_lint.py

Lints a Go controller reconcile function for anti-patterns.

python scripts/reconcile_lint.py --controller controllers/myapp_controller.go

Checks (regex-based heuristics):

• Returns are (ctrl.Result, error) shape
• Errors trigger a non-zero requeue (return ctrl.Result{Requeue: true}, err)
• client.Update() on the spec object is flagged (controllers should update only status)
• time.Sleep inside reconcile is flagged (use RequeueAfter)
• HTTP calls without context cancellation are flagged
• Missing defer after a finalizer add
• No IsConditionTrue / SetCondition calls when conditions present in CRD
• Reconcile function exceeds 80 lines (extract subroutines)

operator_capability_audit.py

Scores an operator against OperatorHub's 5 Capability Levels.

python scripts/operator_capability_audit.py --operator-dir .

Levels:

• L1 — Basic Install: CRD defined, controller deploys it
• L2 — Seamless Upgrades: PDBs, conversion webhooks, version skew strategy
• L3 — Full Lifecycle: backups, restores, failure recovery
• L4 — Deep Insights: metrics endpoint, Prometheus rules, alerts
• L5 — Auto Pilot: auto-scaling, auto-tuning, anomaly detection

Reports current level + concrete next steps to advance one level.

Tooling landscape

Pick a framework based on language and complexity. See references/tooling_landscape.md.

Decision rules:

• New operator + Go shop → kubebuilder
• New operator + Python shop → KOPF
• New operator + can't pick a language → metacontroller
• OpenShift target → operator-sdk

CRD design principles

See references/crd_design.md for full detail. Quick rules:

1. status is the source of truth for the controller's view of the world. Spec is what the user wants; status is what the controller observed.
2. Use the status subresource. Without it, status updates re-trigger reconcile (loop).
3. Use Conditions. Ready, Reconciling, Degraded. Each carries a reason and message.
4. Add finalizers. Without finalizers, deletion races the controller and orphans external resources.
5. Version your CRD from day 1. v1alpha1 → v1beta1 → v1. Plan a conversion webhook.
6. Validate via OpenAPI v3 schema. Don't rely on the controller for validation that should fail at admission.
7. Use additionalPrinterColumns for kubectl get. Show Age, Phase, Ready at minimum.
8. Namespace your CRDs unless they manage cluster-scoped resources.

Reconcile loop principles

See references/reconcile_loop.md for full detail. Quick rules:

1. Idempotent. Reconciling the same state twice → same result, zero side effects.
2. Read once, decide, act. Don't observe the world repeatedly during reconcile.
3. Update status, not spec. Spec belongs to the user.
4. Return errors that requeue. Use ctrl.Result{RequeueAfter: ...} for known transient cases.
5. Never block. No time.Sleep. No long HTTP calls without context.
6. Use the cache. Read via the controller's cached client; only escape the cache for a specific reason.
7. Leader-elect when running >1 replica. Otherwise enable single-replica mode.
8. Set OwnerReferences. Cascading deletion is the operator pattern's free gift.

Workflows

Workflow 1: Bootstrap a new operator (Go + kubebuilder)

1. Pick a Group/Version/Kind: e.g., apps.example.com/v1alpha1, kind=MyApp
2. kubebuilder init --domain example.com --repo github.com/org/myapp-operator
3. kubebuilder create api --group apps --version v1alpha1 --kind MyApp
4. Run crd_validator.py on config/crd/bases/apps.example.com_myapps.yaml
   → Fix every WARN before writing controller code
5. Implement the reconcile function (Karpathy principle 2: simplest correct version first)
6. Run reconcile_lint.py on controllers/myapp_controller.go
7. Run operator_capability_audit.py --operator-dir . — confirm L1
8. Test in a kind cluster: kubectl apply -f config/samples/
9. Add status conditions; aim for L2 in the same PR

Workflow 2: Audit an existing operator

1. Run operator_capability_audit.py --operator-dir <path>
2. Run crd_validator.py --crd config/crd/
3. Run reconcile_lint.py --controller controllers/
4. Triage findings:
   - FAIL → block release; fix before next deploy
   - WARN → file an issue; fix in next 30 days
5. Document current capability level in README; commit
6. Plan one capability level advancement per quarter

Workflow 3: Choose a framework

1. Identify primary language constraint (team skill)
2. Identify deployment target (vanilla k8s vs OpenShift)
3. Identify operator complexity (single CRD vs multi-CRD vs cluster-wide)
4. Cross-reference with references/tooling_landscape.md
5. Build a 1-week proof-of-concept before committing

References

• references/operator_pattern.md — what an operator IS, when to use vs alternatives
• references/crd_design.md — CRD design principles, versioning, conversion webhooks
• references/reconcile_loop.md — reconcile patterns, error handling, idempotency
• references/tooling_landscape.md — framework comparison + decision tree

Slash command

/operator-audit — Run all 3 tools on an operator repo and produce a markdown report.

Asset templates

• assets/crd_template.yaml — CRD with status subresource, conditions, finalizer hint, printer columns
• assets/reconcile_skeleton.go — Go controller reconcile function with idempotency, conditions, finalizers, requeue patterns

Anti-patterns

• time.Sleep(30 * time.Second) inside reconcile — block other reconciles. Use RequeueAfter.
• r.Client.Update(ctx, obj) to set status — use r.Status().Update(ctx, obj) instead.
• No leader election + 2+ replicas — split-brain.
• No finalizer — external resources orphan on deletion.
• CRD without status subresource — status updates trigger spec reconciles (infinite loop).
• Reconcile function > 200 lines — extract reconcileXxx subroutines per condition.
• x-kubernetes-preserve-unknown-fields: true on spec root — defeats validation.
• Imperative reconcile — "if creating, do A; if updating, do B; if deleting, do C". Wrong shape. Reconcile = make actual=desired, regardless of how we got here.

Verifiable success

A team using this skill should achieve:

• 100% of new CRDs pass crd_validator.py before merge
• All reconcile functions pass reconcile_lint.py strict mode
• Operators reach OperatorHub Capability Level 3 (Full Lifecycle) before public release
• Mean time to fix a reconcile bug: <1 day (no infinite loops in production)