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google-bigquery

Translate natural language questions into BigQuery SQL and execute them.

Updated Feb 17, 2026
Search Tags:#nlp#partitioning#bigquery#data-warehouse#sql-generation#query-execution#schema-caching
One-click install
npx skills add https://github.com/gnestor/claude-workflow-plugin --skill google-bigquery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: google-bigquery
Source: https://github.com/gnestor/claude-workflow-plugin/tree/main/.claude/skills/google/bigquery
Command: npx skills add https://github.com/gnestor/claude-workflow-plugin --skill google-bigquery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Translates natural language questions into BigQuery SQL, executes them against the configured project, and returns structured results with helpful guidance on cost and schema usage.

Core Features & Use Cases

  • NLQ to BigQuery SQL translation and execution
  • Cached schema lookup and schema discovery to minimize API calls
  • Support for partitioned tables, nested fields, array data, and advanced BigQuery functions
  • Export results to Jupyter notebooks or structured reports for downstream analysis

Quick Start

Describe your BigQuery question to the agent and it will translate it into a BigQuery query, execute it, and return results.

Frequently Asked Questions about google-bigquery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I translate natural language questions into BigQuery SQL queries?▼

To translate natural language questions into BigQuery SQL, you describe your question to the agent, which converts it into a query, executes it against your project, and returns structured results.

Can I query partitioned tables and nested fields in BigQuery using natural language?▼

Yes, querying partitioned tables and nested fields in BigQuery is fully supported, allowing the agent to translate natural language into SQL that handles advanced BigQuery features and array data.

What is the best way to minimize BigQuery API calls when discovering table schemas?▼

The best way to minimize BigQuery API calls during schema discovery is leveraging cached schemas stored in references, which allows the system to look up table structures without repeated requests.

Does natural language to SQL generation expose BigQuery query costs?▼

Yes, natural language to SQL generation exposes BigQuery query costs by reporting the bytes processed during execution, helping you monitor data scanning expenses alongside structured results.

How do I export BigQuery results to Jupyter notebooks after query execution?▼

To export BigQuery results to Jupyter notebooks after query execution, the skill supports direct export of structured results, allowing downstream analysis within notebook environments.

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