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Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
A workflow for conducting rigorous, reproducible data investigations and ad-hoc analyses. This skill should be used when investigating a business question, explaining a metric anomaly, validating a hypothesis, performing root cause analysis, or preparing a one-off investigation dashboard or memo.
Drive the `airbyte-agent` CLI to manage Airbyte connectors, workspaces, and organizations. Run list/get/search/create/update actions against connector data (HubSpot, Salesforce, Slack, GitHub, etc.), install new connectors via the browser credential flow, list and switch workspaces, list organizations, inspect connector metadata, read skill docs, or print the merged CLI + OpenAPI schema for any operation. Use when the user mentions Airbyte, the `airbyte-agent` CLI, connectors, syncs, workspaces, organizations, or asks to read/write data from a connected SaaS product.
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
Implement dependency injection in Angular v20+ using inject(), injection tokens, and provider configuration. Use for service architecture, providing dependencies at different levels, creating injectable tokens, and managing singleton vs scoped services. Triggers on service creation, configuring providers, using injection tokens, or understanding DI hierarchy.
Create custom directives in Angular v20+ for DOM manipulation and behavior extension. Use for attribute directives that modify element behavior/appearance, structural directives for portals/overlays, and host directives for composition. Triggers on creating reusable DOM behaviors, extending element functionality, or composing behaviors across components. Note - use native @if/@for/@switch for control flow, not custom structural directives.
Build signal-based forms in Angular v21+ using the new Signal Forms API. Use for form creation with automatic two-way binding, schema-based validation, field state management, and dynamic forms. Triggers on form implementation, adding validation, creating multi-step forms, or building forms with conditional fields. Signal Forms are experimental but recommended for new Angular projects. Don't use for template-driven forms without signals or third-party form libraries like Formly or ngx-formly.
| name | bigquery-basics |
| description | Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis. |
| metadata | {"category":"data","source":{"repository":"https://github.com/google/skills","path":"skills/cloud/bigquery-basics","license_path":"LICENSE","commit":"28d90a333c4d900bcc76e498363e0c835dc69a5c"}} |
BigQuery is a serverless, AI-ready data platform that enables high-speed analysis of large datasets using SQL and Python. Its disaggregated architecture separates compute and storage, allowing them to scale independently while providing built-in machine learning, geospatial analysis, and business intelligence capabilities.
Enable the BigQuery API:
gcloud services enable bigquery.googleapis.com --quiet
Create a Dataset:
bq mk --dataset --location=US my_dataset
Create a Table:
Create a file named schema.json with your table schema:
[
{
"name": "name",
"type": "STRING",
"mode": "REQUIRED"
},
{
"name": "post_abbr",
"type": "STRING",
"mode": "NULLABLE"
}
]
Then create the table with the bq tool:
bq mk --table my_dataset.mytable schema.json
Run a Query:
bq query --use_legacy_sql=false \
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \
WHERE state = "TX" LIMIT 10'
Core Concepts: Storage types, analytics workflows, and BigQuery Studio features.
CLI Usage: Essential bq command-line tool
operations for managing data and jobs.
Client Libraries: Using Google Cloud client libraries for Python, Java, Node.js, and Go.
MCP Usage: Using the BigQuery remote MCP server and related client integrations.
Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
IAM & Security: Roles, permissions, and data governance best practices.
If you need product information not found in these references, use the
Developer Knowledge MCP server search_documents tool.