397 件 ・ 関連度順
概要と使いどころ
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
日本語の概要は準備中です。原文の説明を表示しています。
Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.
日本語の概要は準備中です。原文の説明を表示しています。
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
日本語の概要は準備中です。原文の説明を表示しています。
Generates security-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify security requirements, and provide actionable recommendations for IAM, network security, data protection, and operational security.
日本語の概要は準備中です。原文の説明を表示しています。
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
日本語の概要は準備中です。原文の説明を表示しています。
Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident management.
日本語の概要は準備中です。原文の説明を表示しています。
Select and implement the right Azure compute platform for a workload. Decision tree guides choice based on workload characteristics, scaling needs, and operational maturity. Covers serverless, PaaS, containers, Kubernetes, and IaaS patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.
日本語の概要は準備中です。原文の説明を表示しています。
Marketing project management and execution excellence for B2B SaaS campaigns. Use this skill when coordinating multi-channel campaigns, allocating team resources, planning agile marketing sprints, running QBRs with clients, managing project timelines, balancing marketing workload, or implementing marketing operations scrum. Also triggers on: campaign coordination, resource allocation, agile marketing, sprint planning, client success, QBR, project timeline, marketing ops scrum, workload balancing.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Coreweave — coreWeave GPU cloud management covering Kubernetes namespace inventory, GPU workload status, virtual server instances, persistent volume claims, node allocation, billing analysis, and network configuration. Use for comprehensive CoreWeave infrastructure assessment and GPU workload optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Kubescape — kubescape Kubernetes security posture analysis. Covers NSA/CISA framework assessment, MITRE ATT&CK mapping, CIS Kubernetes benchmarks, workload scanning, RBAC analysis, and network policy evaluation. Use when assessing Kubernetes cluster security, evaluating compliance frameworks, or analyzing workload configurations.
日本語の概要は準備中です。原文の説明を表示しています。
bu skill covers hardening managed Kubernetes clusters on EKS, AKS, and GKE by implementing Pod Security Standards, network policies, workload identity, RBAC scoping, image admission controls, and runtime security monitoring. It addresses cloud-specific security features including IRSA for EKS, Workload Identity for GKE, and Managed Identities for AKS.
日本語の概要は準備中です。原文の説明を表示しています。
Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.
日本語の概要は準備中です。原文の説明を表示しています。
OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC/SPIFFE), safety best practices, moderation API, production best practices, deployment checklist, cost / latency optimization, fast mode, Realtime API costs。rate limits・error handling・prompt caching は openai-api-core、Codex のワークスペース 管理は openai-codex が担当。
Claude API (platform.claude.com) の組織管理・Admin API (sk-ant-admin) リファレンス。 users, invites, workspaces, API keys, service accounts, RBAC (groups / roles), Workload Identity Federation (WIF, federation issuers / rules), Usage & Cost API, rate limits / spend limits, Analytics API, Compliance API, CMEK, zero data retention (ZDR), data residency, inference hooks。
Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.
日本語の概要は準備中です。原文の説明を表示しています。
Plan, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).
日本語の概要は準備中です。原文の説明を表示しています。
工場の設備・人員・材料・納期を踏まえて生産順序を組み、段取り替えや工程間の負荷を調整し、設備故障や材料不足時の計画見直しを支援するスキル。
Kubernetesの設定ファイル作成・レビューと障害調査を支援します。稼働確認、権限、リソース制限、自動スケールの設定例と調査コマンドを参照できます。
ClickHouseの分析用テーブル設計、集計クエリの作成、遅い処理の見直しを支援します。大量データの一括取り込みやリアルタイム集計の設計にも使えます。
文章などを数値化して似た内容を探す検索をQdrantで実装。属性による絞り込みやAI回答用の資料検索から、大規模運用の設定・調整まで支援します。
Performs exact symbolic mathematics with SymPy for algebra, calculus, equation solving, symbolic linear algebra, physics, and lambdify or LaTeX code generation. Use when a task needs symbolic results, explicit assumptions, or exact arithmetic; use NumPy or SciPy for purely numerical workloads.
日本語の概要は準備中です。原文の説明を表示しています。
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.
日本語の概要は準備中です。原文の説明を表示しています。
Detects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.
日本語の概要は準備中です。原文の説明を表示しています。