「workload」の検索結果

Expert knowledge for Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing VM/VMSS sizes, configuring disks/GPU/InfiniBand, using ADE/CMK, Trusted Launch, or Oracle/HPC workloads, and other Azure Virtual Machines related development tasks. Not for Azure Data Science Virtual Machines (use azure-data-science-vm), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Virtual Machine Scale Sets (use azure-vm-scalesets), Azure Virtual Desktop (use azure-virtual-desktop).

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。

Expert knowledge for Azure Kubernetes Service (AKS) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing AKS with Entra/RBAC, configuring Fleet, GPUs or Wasm, AI/ML workloads, or Istio/KEDA autoscaling, and other Azure Kubernetes Service (AKS) related development tasks. Not for Azure Container Apps (use azure-container-apps), Azure Container Instances (use azure-container-instances), Azure Container Registry (use azure-container-registry), Azure Red Hat OpenShift (use azure-redhat-openshift).

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。

Expert knowledge for Azure Database Migration Service development including troubleshooting, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when planning Azure DMS migrations for MySQL, PostgreSQL, MongoDB, SSIS-to-Azure SQL, or hybrid workloads, and other Azure Database Migration Service related development tasks. Not for Azure Migrate (use azure-migrate), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

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。

Expert knowledge for Azure Elastic SAN development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when creating Elastic SAN volumes, AVS datastores, iSCSI clients, snapshots, or clustered SQL/FC workloads, and other Azure Elastic SAN related development tasks. Not for Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files), Azure NetApp Files (use azure-netapp-files), Azure Virtual Machines (use azure-virtual-machines).

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。

Expert guidance for planning and executing cloud adoption using Azure Cloud Adoption Framework. Covers strategy, planning, readiness & landing zones, adoption patterns, governance, security, operations & management, organization & teams, and adoption scenarios. Use when planning AVD, SAP, Oracle, VMware, AI/ML workloads, or designing Azure landing zones and governance, and other Azure Cloud Adoption Framework related development tasks.

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。

Expert guidance for designing Azure solutions using Azure Architecture. Covers reference architectures, solution ideas, design patterns, technology choices, architecture styles, best practices, anti-patterns, example workloads, and migration guides. Use when designing AKS/microservices, hybrid/Arc, SAP or mainframe migrations, IoT/Edge, or mission-critical BCDR, and other Azure Architecture related development tasks.

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。

Expert knowledge for Azure Kubernetes Service Edge Essentials development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing AKS Edge/Hybrid with Arc, GitOps, IoT/OPC/ONVIF, TPM/Key Vault secrets, or AI model workloads, and other Azure Kubernetes Service Edge Essentials related development tasks. Not for Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure IoT Edge (use azure-iot-edge), Azure Stack Edge (use azure-stack-edge), Azure Container Apps (use azure-container-apps).

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。

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical 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。

Design cloud network architectures with VPC patterns, subnet strategies, zero trust principles, and hybrid connectivity. Use when planning VPC topology, implementing multi-cloud networking, or establishing secure network segmentation for cloud 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。

Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.

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。

Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational 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。

Install a .NET SDK locally for safe preview testing, specific-version pinning, or reproducible team setups — without modifying the system-wide installation. USE FOR: trying .NET previews safely, testing specific SDK versions, installing MAUI or other workloads on a preview, updating or replacing an existing local SDK, creating reproducible team/CI install scripts, configuring global.json paths. DO NOT USE FOR: system-wide SDK installs, .NET hosts older than 10, runtime-only installs, or projects not using SDK-style commands.

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。

Entrypoint for managing Volcengine Flink (based on Apache Flink) workloads. Use it for developing and deploying streaming and batch jobs, diagnosing real-time issues (e.g., backpressure, checkpoint failures, OOM), troubleshooting resources, and performing SRE actions (stop/start/restart/scale/config changes). It intelligently routes requests to four specialized sub-skills and leverages flink-mcp for tool execution.

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。

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

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。

Cross-engine decision rubric for self-hosting or recommending an LLM serving stack. Picks among vLLM, SGLang, TensorRT-LLM, TGI, llama.cpp, Ollama, and MLX as a function of (hardware × workload × constraint), not "which is fastest". Activates whenever a coder-agent must choose, defend, or migrate a serving runtime.

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。

Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for perception, and dev-vs-deploy container patterns. Use this skill when containerizing ROS2 workspaces, setting up docker-compose for robot software stacks, debugging DDS communication between containers, configuring NVIDIA Container Toolkit for GPU workloads, forwarding X11/Wayland for rviz2 and GUI tools, or managing USB device passthrough for cameras and serial devices. Trigger whenever the user mentions Docker with ROS2, docker-compose for robots, Dockerfile for colcon workspaces, container networking for DDS, devcontainer for ROS2, multi-stage builds for ROS2, or deploying ROS2 in containers. Also trigger for CI/CD with Docker-based ROS2 builds, CycloneDDS or FastDDS configuration in containers, or shared memory in Docker. Covers Humble, Jazzy, and Rolling distributions across Ubuntu 22.04 and 24.04 base images.

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。

Provides AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application deployments.

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。

Optimize construction resource allocation across activities. Level resources, resolve over-allocations, and balance workload while minimizing schedule impact.

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。

Allocate and track labor resources across project activities. Balance workload, track attendance, and optimize crew assignments.

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 querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.

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。

Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.

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。

Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.

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。

Applies data-grid architecture for high-traffic stateful workloads. Use when a single database cannot scale and in-memory partitioning is needed.

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。

Account assignment by revenue potential, geography, relationship. Workload balancing, TAM/SAM calculation, coverage models.

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。