From data-agent-kit-starter-pack
Guides BigQuery usage with query optimization rules, BigFrames Python code generation, and BQML/AI functions like forecasting, embeddings, and vector search.
npx claudepluginhub gemini-cli-extensions/data-agent-kit-starter-pack --plugin data-agent-kit-starter-packThis skill uses the workspace's default tool permissions.
This skill provides comprehensive guidance for BigQuery services, optimizations,
Implements structured self-debugging workflow for AI agent failures: capture errors, diagnose patterns like loops or context overflow, apply contained recoveries, and generate introspection reports.
Monitors deployed URLs for regressions in HTTP status, console errors, performance metrics, content, network, and APIs after deploys, merges, or upgrades.
Provides React and Next.js patterns for component composition, compound components, state management, data fetching, performance optimization, forms, routing, and accessible UIs.
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.