288 件 ・ 関連度順
概要と使いどころ
Creates interactive data visualizations with Apache ECharts, the open-source JavaScript charting library. Use when a user asks to build charts, dashboards, or data-driven graphics using ECharts in React, Vue, or vanilla JavaScript applications, render a chart to SVG on the server, or upgrade from ECharts 5 to 6.
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。
Apache Cassandra is a distributed NoSQL database designed for high availability and linear scalability. Learn CQL (Cassandra Query Language), data modeling with partition keys, replication strategies, and integration with Node.js using the DataStax driver.
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。
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Learn to write DAGs, use operators, set up connections, configure scheduling, and deploy with Docker Compose.
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。
Route HugeGraph MCP regression testing tasks to the current public tool surface, including inspection, read-only queries, Gremlin generation, extraction, controlled import, schema validate/dry-run, safety guards, and admin-gated tool checks.
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。
Route HugeGraph MCP graph data extraction and controlled import/delete tasks to stable public tools. Use when the user asks to extract graph data from text, import structured vertices or edges, verify an import, or asks whether table, SQL, update, or delete graph writes are available.
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。
Route HugeGraph MCP operational checks to status, schema, permission, AI availability, and read-only verification tools. Use when the user asks to check the current graph, schema, MCP connection, readonly state, HugeGraph-AI state, counts, indexes, or readiness.
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。
Route HugeGraph MCP structured graph query tasks to stable read-only tools. Use when the user asks graph questions, asks to generate Gremlin, provides Gremlin to explain, or needs read-only graph exploration.
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。
Route HugeGraph MCP schema design, validation, and dry-run preview tasks to stable public tools. Use when the user asks to design vertex labels, edge labels, properties, primary keys, indexes, or preview schema operations.
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 guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines, ETL/ELT. Mencakup dbt, Airflow, Dagster, BigQuery, ClickHouse, dan DuckDB.
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。
Diagnoses Kafka consumer group lag using the Kafka AdminClient API and JMX metrics exposed via the Confluent Metrics API. Identifies slow consumers, topic partition hotspots, and broker rebalance storms that contribute to lag growth. Provides a step-by-step runbook to tune fetch.min.bytes, max.poll.records, and partition count.
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。
Extracts and transforms Avro, Protobuf, and JSON Schema definitions from Confluent Schema Registry. Generates typed data models and validates schema compatibility using the Schema Registry REST API.
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。
Processes real-time event streams using KafkaJS consumer groups and transforms messages with configurable schemas. Handles partition rebalancing, offset commits, and dead-letter queue routing for failed transformations.
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 serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.
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。
Open Source License guidance, selection, compliance review, and drafting. Use this skill when users ask about choosing open source licenses, checking license compatibility, reviewing projects for OSS compliance, generating LICENSE/NOTICE files, or understanding specific license terms. Triggers include questions about MIT, Apache, GPL, BSD, LGPL, AGPL, copyleft, permissive licenses, license compatibility, SPDX identifiers, or any OSS licensing topic.
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。
Analyze server log files for crawl budget insights. Reads Apache Combined/Common or Nginx access logs locally (no external calls). Classifies bot vs user traffic, identifies top crawled URLs, crawl frequency by path, and crawl budget concerns. Use when user says "log analysis", "crawl budget", "server logs", "bot traffic", "crawl frequency", "access log", "analyze logs".
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 rich visualizations with Apache ECharts: option config, dataset transforms, large-data performance, and custom rendering. Use for complex dashboards and big datasets.
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。
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).
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。
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).
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。
Apache Spark 4.0.2 best practices for PySpark and Scala distributed data processing
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。
Apache Airflow best practices for DAG authoring, testing, and production 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。
Apache Kafka 4.2 best practices for event streaming, topic design, and producer-consumer 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。
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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。
license: Apache-2.0 NOT for unrelated tasks outside this domain.
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。
license: Apache-2.0 NOT for unrelated tasks outside this domain.
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。