Agent skill · data analytics · aiskillstore
looker-studio-bigquery
Design and configure Looker Studio dashboards with BigQuery data sources. Use when creating analytics dashboards, connecting BigQuery to visualization tools, or optimizing data pipeline performance. Handles BigQuery connections, custom SQL queries, scheduled queries, dashboard design, and performance optimization.
Why this skill is useful
Provides specific SQL commands and performance optimization techniques for BigQuery that the AI wouldn't reliably generate on its own.
What it needs
Requires google-cloud account access. About 7k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Looker Studio BigQuery Integration When to use this skill Analytics dashboard creation: Visualizing BigQuery data to derive business insights Real-time reporting: Building auto-refreshing dashboards Performance optimization: Optimizing query costs and loading time for large datasets Data pipeline: Automating ETL processes with scheduled queries Team collaboration: Building shareable interactive dashboards Instructions Step 1: Prepare GCP BigQuery Environment Project creation and activation Create a new project in Google Cloud Console and enable the BigQuery API. Create dataset and table IAM permission configuration Grant IAM permissions so Looker Studio can access BigQuery: Role Description ------ ------ BigQuery Data Viewer Table read permission BigQuery User Query execution permission BigQuery Job User Job execution permission Step 2: Connecting BigQuery in Looker Studio Using native BigQuery connector (recommended) 1. On Looker Studio homepage, click + Create → Data Source 2. Search for "BigQuery" and select Google BigQuery connector 3. Authenticate with Google account 4. Select project, dataset, and table 5. Click Connect to create data source Custom SQL query approach Write SQL directly when complex data transformation is needed: Advantages: Handle complex data transformations in SQL Pre-aggregate data in BigQuery to reduce query costs Improved performance by not loading all data every time Multiple table join approach Step 3: Performance Optimization with Scheduled Queries Use scheduled queries instead of live queries to periodically pre-compute data: Configure as scheduled query in BigQuery UI: Runs automatically daily Saves results to a new table Looker Studio connects to the pre-computed table Advantages: Reduce Looker Studio loading time (50-80%) Reduce BigQuery costs (less data scanned) Improved dashboard refresh speed Step 4: Dashboard Layout Design F-pattern layout Use the F-pattern that follows the natural reading flow of users: Dashboard components El …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/looker-studio-bigquery