132건 · 관련도순
Apache Superset is a widely adopted open-source BI platform for SQL exploration, chart building, and dashboard delivery. This skill is useful when an agent needs to query warehouse data, assemble dashboards, or explain metrics using a mature analytics interface instead of ad hoc notebook code.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Airbyte is the leading open-source data integration platform providing 600+ pre-built connectors for ELT pipelines from APIs, databases, and files to data warehouses, lakes, and lakehouses. It supports both self-hosted and cloud deployments with a no-code connector builder.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit e-commerce and retail product return systems for return rate reduction strategies, reverse logistics efficiency, refurbishment routing, fraud detection in returns, and return reason analytics. Use when reviewing order management systems, RMA workflows, warehouse return processing, disposition engines, or retail loss prevention tools.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
PgBouncer, connection optimization, and pooling strategies for database performance. Activate on: connection pool, PgBouncer, database connections, pool size, connection limit, Prisma pool, Drizzle pool. NOT for: query optimization (use data-warehouse-optimizer), database schema design (use dimensional-modeler).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
CQRS pattern, event stores, projections, eventual consistency. Activate on: CQRS, event sourcing, event store, read model, projection, aggregate, domain event, command handler. NOT for: message broker setup (use event-driven-architecture-expert), database optimization (use data-warehouse-optimizer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Snowflake best practices for cloud data warehouse design, query optimization, and cost management
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Routes data engineering tasks to the correct DE expert agent. Use when user requests data pipeline design, DAG authoring, SQL modeling, stream processing, or warehouse optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Construa pipelines de dados escaláveis, data warehouses modernos e arquiteturas de streaming em tempo real. Implementa Apache Spark, dbt, Airflow e plataformas de dados nativas em nuvem.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录，支持云端公共 PostgreSQL 服务（aact-db.ctti-clinicaltrials.org）和每日 dump 本地还原两种连接方式，自动检测并优先使用本地高性能模式。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Writes and reviews Snowflake-specific SQL: QUALIFY, FLATTEN, VARIANT/OBJECT/ARRAY handling, streams, tasks, stages, and warehouse-aware patterns. Use when generic SQL is not enough on Snowflake — semi-structured data, incremental pipelines, or Snowflake DDL.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.