「workload」の検索結果

Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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。

Secure containerized workloads — image hardening, registry scanning, runtime protection, and supply-chain integrity.

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。

Blazing inference on Cerebras wafer-scale hardware — ultra-fast open-model APIs for speed-critical 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。

Scale Ray workloads and serve models on Anyscale — managed Ray clusters for training and inference.

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。

Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.

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。

LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate 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。

Orquestração multi-nuvem para workloads de ML com otimização automática de custos. Use quando precisar executar treinamento ou jobs em lote em múltiplas nuvens, aproveitar instâncias spot com auto-recuperação ou otimizar custos de GPU entre provedores.

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。

Plataforma GPU serverless em nuvem para executar workloads de ML. Use quando você precisar de acesso GPU sob demanda sem gerenciamento de infraestrutura, fazendo deploy de modelos de ML como APIs, ou executando jobs em batch com auto-scaling.

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。

Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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。

Assess and migrate cross-cloud workloads to Azure. Generates assessment reports and converts code from AWS, GCP, or other providers to Azure services. WHEN: migrate Lambda to Azure Functions, migrate AWS to Azure, Lambda migration assessment, convert AWS serverless to Azure, migration readiness r...

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。

Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations based on workload requirements, performance needs, and budget constraints. No Azure account required — uses public documentation and the Azure Retail Prices API. WHEN: recommend VM size, which VM should I use, choose Azure VM, VM ...

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 multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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。

This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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。

This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation 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。

Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.

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 review Action Scheduler jobs in WordPress plugins using Action Scheduler 3.9.x public APIs - async, single, recurring, and cron-expression actions; action_scheduler_init load timing; hook/args/group naming; unique and priority parameters; idempotent callbacks; chunked workloads; activation/deactivation cleanup; WooCommerce-bundled or standalone dependency usage; admin and WP-CLI debugging; queue runner limits; and safe operational troubleshooting. Use when a plugin schedules background jobs with as_enqueue_async_action, as_schedule_single_action, as_schedule_recurring_action, as_schedule_cron_action, as_get_scheduled_actions, or integrates with WooCommerce background queues.

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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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 multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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 Codeball — codeBall AI-powered pull request risk assessment including automatic PR quality scoring, risk classification, and auto-approval for low-risk changes. Use when configuring CodeBall for automated PR triage, risk-based review routing, and review 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 Cast Ai — cAST AI Kubernetes cluster optimization and cost management. Covers cluster cost optimization, spot instance management, cost reports, node rebalancing, security posture, and workload right-sizing. Use when optimizing Kubernetes cluster costs, managing node lifecycle, or analyzing cloud spend with CAST AI.

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 Aws Fargate — aWS Fargate task and service analysis covering ECS cluster inventory, Fargate task status, CPU and memory utilization, task definition review, networking configuration, service auto-scaling policies, and container health checks. Use for Fargate-specific 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 Basecamp — basecamp project management covering projects, to-do lists, messages, schedules, and team activity. Use when auditing Basecamp usage, managing to-dos and message boards, analyzing project activity, or reviewing team workload across Basecamp projects.

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 Baselime — baselime serverless observability platform for AWS Lambda, CloudFlare Workers, and Vercel functions. Covers log and trace querying, alert management, service discovery, and performance analysis of serverless workloads. Use when investigating serverless function performance, querying Baselime events, managing alerts, or analyzing cold start and duration metrics.

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。