> Design, build, and operate Kubernetes operators. Use when extending Kubernetes with a custom controller, choosing a framework, designing CRDs, implementing reconciliation loops, or auditing an operator for anti-patterns.
npx skills add https://github.com/borghei/Claude-Skills --skill kubernetes-operator
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).
| Situation | Skill applies |
|-----------|---------------|
| Building a new operator for an internal platform primitive | Yes — start with the operator pattern decision |
| Auditing an existing operator for production-readiness | Yes — use anti-patterns + scripts/reconciliation_audit.py |
| Designing CRDs for a custom resource | Yes — use CRD design + scripts/crd_validator.py |
| Deciding "operator vs Helm chart vs plain manifests" | Yes — use the decision matrix |
| Scaffolding a new operator project | Yes — 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 |
Before scaffolding or auditing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
operator_scaffold.py vs crd_validator.py vs reconciliation_audit.py)--name/--group/--kind); for validation/audit: the CRD YAML or controller path (the input the scripts read)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.
| Tool | Purpose | Command |
|------|---------|---------|
| crd_validator.py | Validate 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.py | Generate a production-ready operator project skeleton with stricter RBAC, observability, and finalizer scaffolding | python3 scripts/operator_scaffold.py --name db-operator --group example.com --kind Database |
| reconciliation_audit.py | Audit 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.
Load the reference that matches the task — keep this file lean and pull detail on demand:
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.
| Skill | Integration |
|-------|------------|
| engineering/chaos-engineering | Chaos-test operators (kill the controller, partition from API server) |
| engineering/observability-designer | Wire metrics + logging for operators |
| engineering/incident-commander | Operators amplify blast radius; incident response matters more |
| engineering/feature-flags-architect | Operators with spec.feature.<x>.enabled fields effectively become flag systems; consider the trade-off |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take borghei/kubernetes-operator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.