536 件 ・ 関連度順
概要と使いどころ
Run Playwright tests at scale using Azure Playwright Workspaces (formerly Microsoft Playwright Testing). Use when scaling browser tests across cloud-hosted browsers, integrating with CI/CD pipeline...
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 Gamelift — aWS GameLift fleet management including game server deployments, fleet scaling, matchmaking configuration, game session monitoring, and player session tracking. Covers fleet health, capacity utilization, matchmaking metrics, and cost analysis.
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 Baseten — baseten ML deployment platform management covering model inventory, deployment status, autoscaling configuration, inference call history, GPU allocation, environment management, and performance metrics. Use for comprehensive Baseten workspace assessment and ML serving 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 Aws Dynamodb Deep — aWS DynamoDB deep-dive management covering table configurations, capacity analysis, GSI/LSI health, auto-scaling policies, streams, backups, contributor insights, and TTL settings. Use when performing deep analysis of DynamoDB tables, optimizing capacity and throughput, debugging throttling, or auditing table configurations beyond basic inventory.
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 Azure Functions Deep — deep Azure Functions analysis covering function app inventory, trigger types, execution metrics, scaling behavior, slot deployments, application insights integration, and consumption plan cost tracking. Provides optimization guidance for cold starts and memory usage.
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 Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance analysis.
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 Azure Container Apps — azure Container Apps revision management, scaling rules, Dapr integration, ingress configuration, and environment health via Azure CLI.
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 Azure App Service — azure App Service web app health monitoring, deployment slot management, scaling configuration, and application settings analysis via Azure CLI.
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 Gcp Cloud Functions — google Cloud Functions management, execution analysis, scaling configuration, event trigger inspection, and runtime diagnostics via gcloud CLI.
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 Azure Functions — azure Functions app analysis, execution metrics, consumption plan monitoring, scaling configuration, and deployment management via Azure CLI.
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 Rds Deep — aWS RDS deep analysis covering Performance Insights, event subscriptions, proxy management, and global database status. Covers PI counter metrics, wait event analysis, top SQL queries, read replica lag, storage autoscaling, and Aurora-specific diagnostics.
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 Gcp Cloud Run — google Cloud Run service management, revision traffic splitting, scaling configuration, concurrency tuning, and container diagnostics via gcloud CLI.
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 Ecs — aWS ECS cluster health, service status, task analysis, capacity provider management, and deployment tracking. Covers service stability, task failure investigation, container insights, resource utilization, and scaling analysis.
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 Eks — aWS EKS cluster management, nodegroup status, addon management, IRSA configuration, and cluster health analysis. Covers Kubernetes version tracking, nodegroup scaling, addon compatibility, IAM role mapping, and control plane logging.
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 en n8n avancé (workflows, nodes, expressions, webhooks, self-hosted, custom nodes, scaling, error handling)
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。
Neon is a serverless Postgres platform that separates compute and storage to offer autoscaling, branching, instant restore, and scale-to-zero. It's fully compatible with Postgres and works with any language, framework, or ORM that supports Postgres.
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。
Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.
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。
Server management principles and decision-making. Process management, monitoring strategy, and scaling decisions. Teaches thinking, not 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。
Expert in building Telegram bots that solve real problems - from simple automation to complex AI-powered bots. Covers bot architecture, the Telegram Bot API, user experience, monetization strategies, and scaling bots to thousands of users. Use when: telegram bot, bot api, telegram automation, chat bot telegram, tg bot.
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 in building and selling Notion templates as a business - not just making templates, but building a sustainable digital product business. Covers template design, pricing, marketplaces, marketing, and scaling to real revenue. Use when: notion template, sell templates, digital product, notion business, gumroad.
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 AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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 run reproducible benchmarks. Use when measuring performance scaling, comparing branches, or analyzing speedup 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。
Run the full implement-review-fix loop using implementer and reviewer personas. Supports effort-based multi-reviewer scaling (1-5 reviewers) with automatic specialization selection. Includes memory-based feedback loop that learns from past review patterns. Loops until all reviewers find 0 issues of any severity.
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。