「scaling」の検索結果

Diagnose runtime infrastructure issues — cold starts, timeouts, scaling problems, network failures. Use when asked about "infra is slow", "cold starts", "network issues", "why is this timing out", "scaling problem", "latency spikes", or "service is down".

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Assesses scaling companies with product-market fit validation, unit economics maturity, and growth trajectory sustainability analysis. Use when evaluating growth-stage investments, analyzing scaling businesses, or assessing expansion readiness.

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Implement cloud cost optimization strategies for Kubernetes workloads using tools like Kubecost for visibility, right-sizing recommendations, horizontal and vertical pod autoscaling, spot/preemptible instances, and resource quotas. Covers cost allocation, showback reporting, and continuous optimization practices. Use when cloud costs are growing without proportional business value, when resource requests are misaligned with actual usage, when manual scaling leads to over-provisioning, or when implementing showback and chargeback for internal cost accountability.

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Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

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Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.

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Deploy ShinyProxy for hosting multiple containerized Shiny applications. Covers ShinyProxy Docker deployment, application.yml configuration, Shiny app Docker images, authentication, container backends, usage tracking, and scaling. Use when hosting multiple Shiny apps behind a single entry point, needing per-app authentication and access control, deploying Shiny apps as isolated Docker containers, or scaling beyond single-app deployment with usage analytics and audit logging.

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Turn in vitro potency and animal pharmacokinetics into a defensible human dose projection — the arithmetic that decides whether a compound can reach its target concentration safely. Use this skill for non-compartmental analysis of a concentration-time profile (AUC, Cmax, terminal half-life, clearance, volume of distribution), one- and two-compartment simulation of a dosing regimen, interspecies allometric scaling, human-equivalent dose conversion by body surface area, and the exposure margin between a projected therapeutic concentration and a toxicology no-effect level. Also trigger on non-compartmental analysis, AUC, clearance, volume of distribution, allometric scaling, human equivalent dose, first-in-human, NOAEL, therapeutic index, or exposure margin.

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Translating coarse global climate projections to local scales — dynamical vs statistical downscaling, bias correction, and added value.

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Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).

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Inspects Google Cloud Filestore capacity and utilization, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.

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Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).

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Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference, GKE upgrade, GKE observability, GKE multi-tenancy, GKE batch, GKE HPC, GKE compute class.

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Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.

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Banco de dados vetorial gerenciado para aplicações de IA em produção. Totalmente gerenciado, com auto-scaling, busca híbrida (vetores densos + esparsos), filtragem de metadados e namespaces. Latência baixa (<100ms p95). Use para RAG em produção, sistemas de recomendação ou busca semântica em escala. Ideal para infraestrutura serverless e gerenciada.

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Solution-Focused Brief Therapy according to de Shazer and Berg: Miracle question, exception exploration, scaling, resource activation.

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Systemic questioning techniques: Circular questions, scaling questions, miracle question, and exception questions for reflection and perspective expansion.

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Systemic and solution-focused methods: miracle question, scaling, exception exploration, circular questions, hypothetical questions, worsening questions, coping questions. Merged from solution-focused-therapy and systemic-questioning.

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Use when working with Aws App Runner — aWS App Runner service management and health analysis. Covers service inventory, deployment status, auto-scaling configurations, custom domain associations, VPC connector settings, and observability configurations. Use when inspecting App Runner services, debugging deployment failures, reviewing scaling behavior, or auditing service configurations.

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Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

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Expert en création de cabinet de conseil (positioning, proposals, deliverables, pricing, scaling)

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Expert en génération d'images ComfyUI (workflows, nodes, models, inpainting, upscaling)

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Backend Node.js v2 — Express, NestJS, tRPC, WebSockets, scaling

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Expert en scalabilité des agents IA (scaling horizontal, load balancing, gestion de files, rate limiting)

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Expert en déploiement d'agents IA (containerisation, scaling, monitoring, versioning, A/B testing agents)

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。