クラウド、CI/CD、監視、デプロイ（6,709 件）
概要と使いどころ
Provides comprehensive Nx monorepo management guidance for TypeScript/JavaScript projects. Use when creating Nx workspaces, generating apps/libraries/components, running affected commands, setting up CI/CD, configuring Module Federation, or implementing NestJS backends within Nx
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 messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.
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 Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.
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 Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.
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 SDK for Java 2.x client configuration, credential resolution, HTTP client tuning, timeout, retry, and testing patterns. Use when creating or hardening AWS service clients, wiring Spring Boot beans, debugging auth or region issues, or choosing sync vs async SDK 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。
Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.
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。
Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider specifics, (3) BPMN process diagrams, (4) UML diagrams (class, sequence, activity), (5) data flow diagrams (DFD), (6) decision flowcharts, or (7) system interaction diagrams. This skill focuses on generic/abstract representations, not AWS/Azure-specific architectures (use aws-drawio-architecture-diagrams for cloud diagrams).
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 Lambda integration patterns for Java with cold start optimization. Use when deploying Java functions to AWS Lambda, choosing between Micronaut and Raw Java approaches, optimizing cold starts below 1 second, configuring API Gateway or ALB integration, or implementing serverless Java applications. Triggers include "create lambda java", "deploy java lambda", "micronaut lambda aws", "java lambda cold start", "aws lambda java performance", "java serverless framework".
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 patterns to configure AWS RDS (Aurora, MySQL, PostgreSQL) with Spring Boot applications. Configures HikariCP connection pools, implements read/write splitting, sets up IAM database authentication, enables SSL connections, and integrates with AWS Secrets Manager. Use when setting up RDS connections in Spring Boot, configuring connection pooling, or managing database credentials securely.
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 RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and 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。
Provides AWS Key Management Service (KMS) patterns using AWS SDK for Java 2.x. Use when creating/managing encryption keys, encrypting/decrypting data, generating data keys, digital signing, key rotation, or integrating encryption into Spring Boot applications.
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。
Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.
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 Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific 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。
Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services. Use when setting up monitoring, health checks, or metrics for Spring Boot applications.
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 Secrets Manager patterns for AWS SDK for Java 2.x, including secret retrieval, caching, rotation-aware access, and Spring Boot integration. Use when storing or reading secrets in Java services, replacing hardcoded credentials, or wiring secret-backed configuration into applications.
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。
Canonical rules for writing and reviewing reactive code in Shift. Use whenever you add or change code that uses `signal`, `computed`, `effect`, `track`, `peek`, `untracked`, `batch`, `keyedCache`, or `useSignalState`; when you touch the model (`Font`, `Glyph`, `FontStore`, layers), node definitions, render passes, or text layout; and whenever a UI or canvas "doesn't update", updates late, or redraws too often.
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 creating new skills, editing existing skills, or verifying skills work before deployment
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。
When the user wants to monitor reviews, mentions, and community sentiment about their own product. Also use when the user mentions "track our reviews", "what are people saying about us", "brand monitoring", "reputation management", or "review alerts".
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。
When the user wants to set up ongoing tracking of competitor activity — pricing changes, feature launches, hiring signals, content, or public mentions. Also use when the user mentions "track competitors", "what are competitors doing", "competitor alerts", or "market watch".
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。
Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
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。
Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.
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。
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
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。
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
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。
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines.
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。