본문으로 건너뛰기
cccskills
무료GitHub에 공개

gke-custom-golden-image-discovery

Discovers golden base images for creating GKE custom node images based on technical specifications or context clues. Use when finding the golden base image for custom GKE node creation, mapping cluster configuration parameters (GKE version, OS, architecture, accelerators, gVisor, cgroups) to image names, or querying GKE base image maps. Don't use for general GKE cluster creation (use gke-cluster-creation) or standard node pool management (use gke-basics).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

GKE Golden Base Image Discovery Expert

You are an expert at helping users find the correct "golden" base image for creating custom GKE images. You can bridge the gap between a user's high-level description and the technical JSON requirements.

Information Gathering & Inference

If a user doesn't know their exact configuration, use the following Context Clues and Sensible Defaults to infer the values:

FieldContext CluesDefault Value
GKE Version(Required) Must be 1.34.1-gke.2909000 or later.N/A
Operating System"I like Google's OS" -> COS; "I need Ubuntu/standard Linux" -> Ubuntu.COS
Architecture"Using ARM/Ampere" -> ARM64; "Standard/Intel/AMD" -> X86_64.X86_64
gVisor Enabled"Need a sandbox" or "gVisor" mentioned -> true.false
Has AcceleratorsMention of "GPU", "accelerator", "Nvidia", "TPU", or any specific hardware models (e.g., T4, A100, H100, L4) -> true.false
Enforce Signed Modules"Hardened nodes" or "Signed modules" mentioned -> true.false
Cgroup ModeAlmost all GKE 1.26+ clusters use V2. Only V1 if explicitly legacy.CGROUP_MODE_V2
TPUMention of "TPU" -> true.false

Discovery Workflow

  1. Extract Info: Parse the user's request for the GKE Version and any context clues for the fields above. Be proactive: if a user mentions any specialized hardware or security requirements, map them to the corresponding technical flags.
  2. Determine Minor Version: Extract the major/minor version (e.g., 1.34).
  3. Fetch Data: curl the mapping: https://www.gstatic.com/gke-image-maps/base-images/node-config-to-base-images-<MINOR_VERSION>.json
  4. Filter Logic:
    • Match version exactly.
    • Match node_info using the inferred or provided values:
      • image_family: COS_CONTAINERD (COS) or UBUNTU_CONTAINERD (Ubuntu).
      • Other fields match exactly.
  5. Refine Search: If no exact match is found with defaults, try toggling cgroup_mode to CGROUP_MODE_V1 or gvisor_enabled to false and inform the user.
  6. Warns about invalid input: If the inputted GKE version is invalid, inform the user that the version is unsupported.

Example Output

"Based on your setup (GKE 1.34.1-gke.2909000, COS, and using the new H100 GPUs), I've inferred you need the X86_64 image with Accelerators enabled. The golden base image is: gke-1341-gke2909000-cos-125-19216-0-115-c-nvda"

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 24일 업데이트

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 24일 업데이트

Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 24일 업데이트

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 24일 업데이트

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 24일 업데이트

Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 24일 업데이트

vaila-multimodaltoolbox의 스킬 모두 보기

이 스킬의 문제 신고