How do I install the Bigquery Basics skill?

Run npx skillmds@latest add izzyfresh/bigquery-basics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Bigquery Basics safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bigquery Basics?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Bigquery Basics free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Bigquery Basics?

IzzyFresh (@izzyfresh) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bigquery Basics by izzyfresh — Agent Skill

Name: Bigquery Basics
Author: IzzyFresh

Bigquery Basics

Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.

IzzyFresh Updated Sep 21, 2026

File contents

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

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.
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.

Related Skills

BigQuery AI & ML Skill: SKILL.md file for BigQuery AI and ML capabilities.
BigQuery AI & ML References: Reference files published for the BigQuery AI and ML skill.

---name: bigquery-basicsdescription: Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.---# BigQuery BasicsBigQuery is a serverless, AI-ready data platform that enables high-speedanalysis of large datasets using SQL and Python. Its disaggregated architectureseparates compute and storage, allowing them to scale independently whileproviding built-in machine learning, geospatial analysis, and businessintelligence capabilities.## Setup and Basic Usage1. **Enable the BigQuery API:** ```bash gcloud services enable bigquery.googleapis.com ```2. **Create a Dataset:** ```bash bq mk --dataset --location=US my_dataset ```3. **Create a Table:** Create a file named `schema.json` with your table schema: ```json [ { "name": "name", "type": "STRING", "mode": "REQUIRED" }, { "name": "post_abbr", "type": "STRING", "mode": "NULLABLE" } ] ``` Then create the table with the `bq` tool: ```bash bq mk --table my_dataset.mytable schema.json ```4. **Run a Query:** ```bash 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.- [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 theDeveloper Knowledge MCP server `search_documents` tool.*## Related Skills- [BigQuery AI & ML Skill](https://github.com/google/adk-python/tree/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml): SKILL.md file for BigQuery AI and ML capabilities.- [BigQuery AI & ML References](https://github.com/google/adk-python/tree/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references): Reference files published for the BigQuery AI and ML skill. - [bigquery_ai_classify.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_classify.md) - [bigquery_ai_detect_anomalies.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_detect_anomalies.md) - [bigquery_ai_forecast.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_forecast.md) - [bigquery_ai_generate.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_generate.md) - [bigquery_ai_generate_bool.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_generate_bool.md) - [bigquery_ai_generate_double.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_generate_double.md) - [bigquery_ai_generate_int.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_generate_int.md) - [bigquery_ai_if.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_if.md) - [bigquery_ai_score.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_score.md) - [bigquery_ai_search.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_search.md) - [bigquery_ai_similarity.md](https://github.com/google/adk-python/blob/main/src/google/adk/tools/bigquery/skills/bigquery-ai-ml/references/bigquery_ai_similarity.md)

Frequently asked questions