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Found 74 Skills
Free 9-week data engineering course covering Docker, Terraform, Kestra, BigQuery, dbt, Spark, and Kafka with hands-on projects
Cloud CLI patterns for GCP and AWS. Use when running bq queries, gcloud commands, aws commands, or making decisions about cloud services. Covers BigQuery cost optimization and operational best practices.
Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines. Processes data sourced from BigQuery or Cloud Storage (GCS), applying best practices for data ingestion, movement, schema mapping, and comprehensive data cleaning.
MUST READ before setting up observability for ADK agents or when analyzing production traffic, debugging agent behavior, or improving agent performance. ADK observability guide — Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations, and troubleshooting. Use when configuring monitoring, tracing, or logging for agents, or when understanding how a deployed agent handles real traffic.
Google Cloud Platform services including GKE, Cloud Run, Cloud Storage, BigQuery, and Pub/Sub. Activate for GCP infrastructure, Google Cloud deployment, and GCP integration.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Comprehensive Google Analytics 4 guide covering property setup, events, custom events, recommended events, custom dimensions, user tracking, audiences, reporting, BigQuery integration, gtag.js implementation, GTM integration, Measurement Protocol, DebugView, privacy compliance, and data management. Use when working with GA4 implementation, tracking, analysis, or any GA4-related tasks.
Google Cloud Platform SDK integration. Cloud Functions, Firestore, Cloud Storage, Pub/Sub, BigQuery, and Cloud Run. Node.js and Python client libraries. USE WHEN: user mentions "GCP", "Google Cloud", "Cloud Functions", "Firestore", "Cloud Storage", "Pub/Sub", "BigQuery", "Cloud Run", "Firebase" DO NOT USE FOR: AWS services - use `aws`; Azure services - use `azure`; Firebase Auth - use auth skills
Provide a lookup index of dbt models (BigQuery tables) to guide query writing against a data warehouse. Use when you need to query, analyze, or look up data in a dbt-powered data warehouse, or when resolving a vague data question into the right BigQuery tables to query.
Configure GCP Cloud Audit Logs for compliance. Set up log routing and BigQuery analysis. Use when auditing GCP activity.
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
Choose and configure the data warehouse engine connection for CARTO (BigQuery, Snowflake, Redshift, Postgres, Databricks, Oracle).