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Provides BigQuery SQL query optimization techniques, execution best practices, and performance tuning rules for high-efficiency querying. Use when optimizing BigQuery SQL queries, reducing query costs, or designing performant SQL transformations.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack --skill bigquery-sql命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Guides the authoring and validation of Apache Airflow DAGs for Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
Provides recommendations and best practices for creating, configuring, tuning and optimizing Managed Service for Apache Airflow (MSAA, Cloud Composer) environments. Use when the user asks for guidance, recommendations, or best practices on configuring Cloud Composer, scaling Airflow environments, preventing workload restarts, or analyzing system health.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
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正在显示 SKILL.md
| name | bigquery-sql |
| metadata | {"version":"v1"} |
| description | Provides BigQuery SQL query optimization techniques, execution best practices, and performance tuning rules for high-efficiency querying. Use when optimizing BigQuery SQL queries, reducing query costs, or designing performant SQL transformations. |
Performance and efficiency guidelines for BigQuery SQL queries. Includes rules for column pruning, predicate pushdown, join optimization, and materialization strategies.
[!TIP] Always include a "Summary of Optimizations" section listing only the optimizations applied.
WHERE filters as early as possible.GROUP BY before joins when possible.VIEW vs TABLE for intermediate
nodes based on efficiency.VIEW: Small datasets or simple transformations.TABLE: Large datasets, expensive computations, or nodes reused
multiple times.WHERE <col> IN (SELECT ...): Replace With WHERE EXISTS (SELECT 1 FROM ...)WHERE (SELECT COUNT(*) ...) > 0: Replace With WHERE EXISTS (SELECT 1 FROM ...)UNION → UNION ALL: Faster (skips deduplication), but permits
duplicate rows.COUNT(DISTINCT) → APPROX_COUNT_DISTINCT: Faster and lower memory,
but approximate.