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概要と使いどころ
Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.
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 patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.
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 integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
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 generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.
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 Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.
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 configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI 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 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。
Use when starting analysis work that needs isolation, or before executing a pre-registered plan - ensures an isolated, reproducible workspace with pinned environment, fixed seeds, and immutable raw data
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。
Host-side setup, configuration, customization, builds, migration, and troubleshooting for the Aerovato Container CLI. Use when working with Aerovato Container, settings.json, Dockerfile.User, build stages, V2-to-V3 migration, mounts, harnesses, tools, permissions, Docker, or Podman. Do not use it to expose host Container configuration inside managed containers.
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。
Convert RVT files to IFC format. Support IFC2x3, IFC4, IFC4.3 with customizable export settings.
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 the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
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。
Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.
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。
Create error boundaries, error handling, and fallback UIs for React applications. Use when implementing error handling, creating fallback components, or setting up error reporting.
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。
Converts webm/mp4 video files to optimized GIFs via ffmpeg with configurable quality settings. Use when post-processing recordings into shareable GIFs.
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。
Python package creation and PyPI distribution via pyproject.toml and entry points. Use when publishing a package or setting up build 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。
Provisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality 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。
Provides standardized pytest config, reusable fixtures, and CI integration patterns. Use when setting up or auditing a Python plugin's test infrastructure.
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。
Installs egregore watchdog daemon via launchd or systemd for autonomous relaunching. Use when setting up egregore on a new machine. Do not use on CI/CD runners.
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Configures GitHub Actions CI/CD workflows for testing, linting, and deployment. Use when setting up automation for a Python, Rust, or TypeScript project.
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。
Builds or updates the code knowledge graph via tree-sitter AST and SQLite. Use when setting up the graph before search or blast-radius 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。
Configures pre-commit hooks for linting, type checking, formatting, and testing. Use when setting up a new project or adding quality gates to an existing one.
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。
Routes Argos Translate workflows for offline machine translation, package installation, CLI usage, runtime settings, and troubleshooting.
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。