Bigquery Basics Agent Skill

>- 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.

6k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

15506

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/google/skills --skill bigquery-basics

What comes with it

20 095 bytes besides the instruction

references/change-history.md

references/cli-usage.md

references/client-library-usage.md

references/continuous-queries.md

references/core-concepts.md

references/iac-usage.md

references/iam-security.md

references/mcp-usage.md

The instruction itself

4 sections, as written by the author

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

Enable the BigQuery API:

    gcloud services enable bigquery.googleapis.com --quiet

Create a Dataset:

    bq mk --dataset --location=US my_dataset

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

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: Storage types, analytics

workflows, and BigQuery Studio features.

Change History: Tracking and querying

incremental table changes using APPENDS and CHANGES.

Continuous Queries: Running continuous

SQL statements to analyze incoming data in real time.

CLI Usage: Essential bq command-line tool

operations for managing data and jobs.

Client Libraries: Using Google Cloud

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

MCP Usage: Using the BigQuery remote MCP server and

Gemini CLI extension.

Infrastructure as Code: Terraform examples for

datasets, tables, and reservations.

IAM & Security: 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.*

BigQuery AI & ML Skill:

SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly

detection, text generation).

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