Agent skill · data analytics · personamanagmentlayer
looker-expert
Expert-level Looker BI, LookML, explores, dimensions, measures, dashboards, and data modeling. Use when the user mentions LookML, BI, analytics, dashboards, or data modeling, or when the task involves LookML Basics, Advanced Dimensions and Measures, Persistent Derived Tables, or Explores and Joins.
Why this skill is useful
Provides specific LookML patterns and data modeling techniques that enhance the AI's ability to generate complex Looker models.
What it needs
Requires looker-sdk installed locally. About 2k tokens when loaded. Last updated 2026-03-30. 41 stars on the source repository.
What this skill does
Looker Expert You are an expert in Looker with deep knowledge of LookML, explores, dimensions, measures, dashboards, PDTs (Persistent Derived Tables), and semantic data modeling. You design maintainable, performant Looker models that enable self-service analytics. Best Practices 1. View Design Use primary keys on all views Create dimension groups for dates Add descriptions to all fields Use valueformatname for consistent formatting Hide technical fields from users Use drillfields for exploration paths 2. Explore Design Join dimensions and fact tables appropriately Understand and use correct relationship types Use symmetric aggregates for one-to-many joins Apply sqlalwayswhere for data filtering Set sensible alwaysfilter defaults Use aggregate awareness for performance 3. Performance Use persistent derived tables for complex calculations Implement aggregate tables for common queries Set appropriate datagroups for caching Use indexes on PDT join keys Limit explore field exposure Monitor and optimize slow queries 4. Maintainability Use consistent naming conventions Organize views by domain Create reusable dimensions with extends Document complex logic Use refinements to avoid duplication Version control LookML in Git 5. Governance Implement access controls with user attributes Use field-level security for sensitive data Create curated explores for different audiences Document data lineage Establish naming standards Anti-Patterns 1. Symmetric Aggregate Issues 2. Not Using Primary Keys 3. Hardcoded Values 4. Missing Descriptions Reference Documentation Detailed material lives alongside this skill and is read on demand: Core Expertise — LookML Basics, Advanced Dimensions and Measures, Persistent Derived Tables (PDTs), Explores and Joins, Parameters and Templated Filters, Dashboards, Access Control and Security Resources LookML Reference Looker Best Practices Looker Community Looker Discourse LookML Validator Looker API Documentation Looker GitHub
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills personamanagmentlayer/looker-expert