Looker LookML Development Accelerator
Generate LookML code and troubleshoot Looker dashboards faster
What You Can Do
You generate production-ready LookML code, debug model validation errors, and optimize explore performance. This skill accelerates your development cycle by handling syntax generation, relationship definition, and complex calculations—letting you focus on business logic rather than language mechanics.
Features
automatically write dimensions, measures, and derived tables with correct syntax
identify and fix LookML syntax errors, circular references, and data type mismatches
analyze join patterns, suggest indexing strategies, and refactor queries for faster execution
generate boilerplate views, common measure definitions, and dashboard LookML snippets
build PDT definitions, custom filters, and parametric measures with proper field type handling
check foreign key mappings, test many-to-many joins, and verify drill-path logic
create inline LookML comments and user-facing descriptions for fields and explores
Example Output
View Generation:
view: users {
sql_table_name: public.users ;;
dimension: id { primary_key: yes type: number sql: ${TABLE}.id ;; }
dimension: email { type: string sql: ${TABLE}.email ;; }
measure: count { type: count drill_fields: [id, email] }
}
Error Diagnosis:
Error: Circular join detected
→ users → orders → users (via customer_id)
Fix: Remove the circular join from orders.view or restructure the explore
Performance Recommendation:
Your explore has 5 joins and 3 derived tables.
Optimization: Pre-aggregate order facts into a PDT, add indexes on foreign keys
Estimated load time improvement: 40%
What's Included
- SKILL.md file (LookML development workflows and decision trees):
- LookML syntax reference and pattern templates:
- Common measure and dimension examples:
- Explore optimization checklist:
- Validation error troubleshooting guide:
- PDT and derived table templates:
Who It's For
- LookML Developers — speed up model creation and debugging
- BI Engineers — optimize explores and dashboards for performance
- Data Analysts — write production-ready LookML without deep syntax knowledge
- Analytics Platform Teams — standardize LookML patterns and reduce code review cycles
Best For
- Writing and generating LookML views, explores, and derived tables
- Debugging model validation errors and relationship issues
- Optimizing explore performance and query execution
- Creating reusable LookML templates and patterns
- Refactoring legacy LookML for maintainability and efficiency
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