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Provides optimization, BigFrames Python, and BigQuery ML/AI guidance. Use for BigQuery SQL tuning, data manipulation, or BQML functions.
How this skill is triggered — by the user, by Claude, or both
Slash command
/dak:developing-with-bigqueryThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides comprehensive guidance for BigQuery services, optimizations,
This skill provides comprehensive guidance for BigQuery services, optimizations, and data handling. It acts as a routing table for specialized BigQuery topics.
[!IMPORTANT]
For general standards on running BigQuery in notebooks (SQL cells,
exportkeyword), see@skill:notebook-guidance.
[!IMPORTANT]
You MUST check the data size before deciding on which libraries to use. Use the data size to justify your decision.
Refer to the following resources for expert guidance on specific BigQuery features:
Performance and efficiency guidelines for BigQuery SQL. Includes rules for column pruning, pushdown, and materialization strategies. - Guide: OPTIMIZATION.md
Guidelines for generating valid BigFrames code for data manipulation, model development, and visualization. - Guide: BIGFRAMES.md
Bigframes should be the default library/tool as it is more efficient than using the BigQuery Python client library.
Usage rules and syntax standards for all BigQuery AI/ML functions via SQL (Forecasting, Generative AI, Classification, etc.). - Guide: BQML.md - Functions Reference: - AI.FORECAST - AI.EVALUATE - AI.GENERATE_TABLE - AI.GENERATE_EMBEDDING - Remote Models CONTRIBUTION_ANALYSIS VECTOR_SEARCH
Refer to @skill:notebook-guidance for standards on running BigQuery in
notebooks.
npx claudepluginhub gemini-cli-extensions/data-agent-kit-starter-pack --plugin dakExplains BigQuery-specific SQL features like STRUCT/ARRAY/UNNEST patterns, MERGE statements, scripting (DECLARE/IF/LOOP), BQML, vector search, with use cases, runnable examples, and pitfalls.
Guides BigQuery engineering with bq CLI for queries, table ops, data load/export; GoogleSQL syntax, functions, window funcs; partitioning, clustering, optimization.
Analyzes BigQuery cost and performance: slot reservations, BI Engine, query cost estimation, dataset governance, and partitioning/clustering optimization.