bigquery-bigframes
Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
Install
With your agent
Install OMGSkills skill "googlecloudplatform/data-agent-kit-plugin:skills/bigquery-bigframes" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/GoogleCloudPlatform/data-agent-kit-plugin /tmp/data-agent-kit-plugin && ln -s /tmp/data-agent-kit-plugin/skills/bigquery-bigframes ~/.claude/skills/bigquery-bigframesFrom README
BigFrames (BigQuery DataFrame) basics BigFrames is a Python library that lets you take advantage of BigQuery data processing by using familiar Python APIs. Generic Coding Guidelines Avoid .topandas(): You MUST NOT use .topandas() to download the entire dataset into memory. There are some exceptions: An error message explicitly requests you to use topandas() You are going to visualize the data, and the visualization library does not accept BigFrames Dataframe/Series instances. In this case, reduce the amount of data you are going to download before calling .topandas() Avoid readgbq() for SQL: Do not write SQL queries and execute them with readgbq(). Use BigFrames Dataframe/Series methods instead. Use BigFrames ML package for Machine Learning Tasks: Do not use Scikit-learn or other ML libraries with BigFrames dataframes. Import your tools/classes from bigframes.ml. Stay in the Cloud: Perform data cleaning, transformation, and analysis via BigFrames methods to leverage BigQuery's scale.
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