SKILL.md
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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
SKILL.md file for BigQuery AI and ML capabilities.
Reference files published for the BigQuery AI and ML skill.