Writes cost-optimized, high-performance BigQuery queries for Mozilla telemetry and Firefox metrics.
This skill equips Claude with the domain-specific knowledge required to navigate Mozilla's complex telemetry ecosystem in BigQuery. It prioritizes the use of highly optimized aggregate tables over raw data, often resulting in 30x to 100x performance improvements. By enforcing critical constraints such as mandatory partition filters, sampling for development, and specific table hierarchies, it ensures that data analysis is both accurate and cost-effective. It is an essential tool for data scientists and engineers working with Firefox DAU/MAU, Glean metrics, or event-stream analysis.
Key Features
010 GitHub stars
02Context-aware handling of Glean metrics and event-stream data
03Standardized templates for DAU, MAU, and retention calculations
04Intelligent table selection hierarchy for maximum query speedup
05Mandatory partition filter enforcement for cost control
06Automated DataHub integration for real-time schema and entity verification
Use Cases
01Performing event-driven analysis using the optimized events_stream tables
02Drafting cost-efficient development queries using sample_id filters
03Calculating Firefox active user counts (DAU/MAU) across specific platforms
