メニュー
Design Kubernetes platforms — GitOps (ArgoCD/Flux), service mesh, progressive delivery, multi-tenancy, security, and FinOps. Use for K8s architecture, GitOps implementation, platform engineering, or cloud-native platform design on EKS/AKS/GKE.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | kubernetes-architect |
| description | Design Kubernetes platforms — GitOps (ArgoCD/Flux), service mesh, progressive delivery, multi-tenancy, security, and FinOps. Use for K8s architecture, GitOps implementation, platform engineering, or cloud-native platform design on EKS/AKS/GKE. |
Design Kubernetes platforms that developers want to use and operators can sleep through.
Cluster topology
Delivery
Workloads
Service mesh
Security
restricted for most workloads.Observability
DR & backup
FinOps
Strategy-layer agent planner. Analyses a complex request and recommends which subagents and skills to involve, in what order, with what hand-off. Use only for genuinely multi-domain tasks where the default delegation isn't obvious.
Design and build LLM-powered applications — RAG systems, agentic workflows, vector search, prompt pipelines, tool-using agents. Use for shipping production AI features, chatbots, or any AI-driven feature that goes beyond a one-shot prompt.
Generate developer-first API documentation: OpenAPI 3.1 specs, multi-language code examples, Postman collections, auth guides, and error references. Use when documenting a new API, adding endpoints, or when the user asks for OpenAPI/Postman/docs output.
Design scalable, secure, cost-efficient cloud infrastructure on AWS, Azure, or GCP. Terraform-first, FinOps-aware, multi-cloud and serverless patterns. Use for infrastructure planning, cost-reduction analysis, or cloud migration strategy.
Design context-management systems for AI-powered products — RAG, vector stores, knowledge graphs, episodic/semantic memory, multi-agent context handoff. Use when architecting AI features that need long-running or cross-session memory. Not for conversational context inside Cursor itself (handled by built-in compaction).
Design and build data-intensive systems — ETL/ELT pipelines, warehouses, streaming architectures. Expertise in Spark, Airflow, Kafka, dimensional modelling, governance, and cost optimisation. Use for new data solutions, optimising pipelines, or troubleshooting data infrastructure.