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BigQuery Basics

BigQuery Basics Skill

by Google google/skills skills/cloud/bigquery-basics

Google's official BigQuery skill — datasets, tables, jobs and SQL from the bq CLI, plus the reference set for IAM, Terraform, change history and continuous queries.

GitHub Docs
Data & Analytics
Released
Apache-2.0

Install

Install with the skills CLI

One command for Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf and dozens of other agents. Needs Node.js; the skill is fetched straight from google/skills.

$ npx skills add google/skills --skill bigquery-basics

Run it in a terminal. The CLI finds the agents installed on your machine, asks which ones to add the skill to, and whether to install it into the folder you ran it from or for all your projects.

Other ways to install

Copy the whole skill folder — SKILL.md and any scripts or references beside it — into ~/.claude/skills/bigquery-basics/, where Claude Code picks it up in every project. Only git is needed.

tmp=$(mktemp -d) && \
  git clone --depth 1 --branch main https://github.com/google/skills.git "$tmp" && \
  mkdir -p ~/.claude/skills && \
  cp -R "$tmp"/skills/cloud/bigquery-basics ~/.claude/skills/bigquery-basics

No scripts or references next to it? Saving the SKILL.md alone as ~/.claude/skills/bigquery-basics/SKILL.md works too.

Using another agent? Its personal skills folder
Codex
~/.agents/skills/
Cursor
~/.cursor/skills/
GitHub Copilot
~/.copilot/skills/
Gemini CLI
~/.gemini/skills/
Windsurf
~/.codeium/windsurf/skills/
OpenCode
~/.config/opencode/skills/
Open folder on GitHub

About

  • Correct `bq` CLI syntax for datasets, tables and query jobs
  • Reference set covering IAM, governance and Terraform resources
  • Change history via APPENDS/CHANGES and continuous streaming queries
  • Client-library guidance for Python, Java, Node.js and Go

BigQuery has an unusually wide surface: the bq CLI, the SQL dialect, client libraries in four languages, Terraform resources, reservations, IAM roles and a governance model that all have to line up before a query runs. That is precisely the sort of territory where an agent produces plausible commands that fail.

This official skill from Google's own skills repository grounds the agent in the working set: enabling the API, creating datasets and tables from a JSON schema, running standard-SQL queries, and then a reference directory that covers storage types and BigQuery Studio, incremental change tracking with APPENDS and CHANGES, continuous queries over streaming data, client-library usage for Python, Java, Node.js and Go, Terraform for datasets, tables and reservations, and IAM roles and data-governance practice.

Use it when an agent needs to interact with BigQuery rather than write about it — building ingestion, standing up analytics, or reviewing someone else's dataset layout. It pairs with the BigQuery AI & ML skill in the same repository for forecasting, anomaly detection and text generation inside SQL.

Try it

Once installed, ask your agent something like:

"Create a BigQuery dataset in the US multi-region, load this CSV into a partitioned table, and show me the top 10 rows by revenue."

SKILL.md

The instructions your agent loads, as published by Google.

SKILL.md
name
bigquery-basics
metadata
category: BigDataAndAnalytics
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.

BigQuery Basics

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.

Setup and Basic Usage
  1. Enable the BigQuery API:
    gcloud services enable bigquery.googleapis.com --quiet
  1. Create a Dataset:
    bq mk --dataset --location=US my_dataset
  1. 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
  1. 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'
Reference Directory
  • [Core Concepts](references/core-concepts.md): Storage types, analytics

workflows, and BigQuery Studio features.

  • [Change History](references/change-history.md): Tracking and querying

incremental table changes using APPENDS and CHANGES.

  • [Continuous Queries](references/continuous-queries.md): Running continuous

SQL statements to analyze incoming data in real time.

  • [CLI Usage](references/cli-usage.md): Essential bq command-line tool

operations for managing data and jobs.

  • [Client Libraries](references/client-library-usage.md): Using Google Cloud

client libraries for Python, Java, Node.js, and Go.

  • [MCP Usage](references/mcp-usage.md): Using the BigQuery remote MCP server and

Gemini CLI extension.

  • [Infrastructure as Code](references/iac-usage.md): Terraform examples for

datasets, tables, and reservations.

  • [IAM & Security](references/iam-security.md): 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.*

Related Skills
  • [BigQuery AI & ML Skill](../bigquery-ai-ml):

SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly
detection, text generation).

Install

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