What is the difference between filters.dateRange and timeDimensions?

Filters.dateRange only restricts the data included in the result (ideal for KPI cards), whereas timeDimensions both filters and groups the data by time (ideal for charts).

What kind of data aggregations are supported?

It supports any measures defined in your Drizzle Cube schema, including counts, sums, averages, and other custom semantic calculations.

Does this skill support multi-cube queries?

Yes, it supports cross-cube measures and dimensions, performing automatic JOINs between related cubes in the Drizzle Cube schema.

Can I use relative dates like 'last 30 days'?

Yes, the skill supports a wide range of relative date patterns, including fixed ranges like 'this month' and flexible ranges like 'last N weeks'.

Drizzle Cube Queries

Name: Drizzle Cube Queries
Author: cliftonc

bycliftonc

分析与监控

Builds and executes semantic analytics queries using measures, dimensions, and advanced temporal filtering.

关于

The Drizzle Cube Queries skill empowers Claude to interface directly with Drizzle Cube's semantic layer to generate complex analytical reports. It provides a standardized framework for constructing queries that include measures for aggregation, dimensions for grouping, and sophisticated time-based filtering. This skill is essential for developers building data-driven applications, allowing for seamless retrieval of time-series data, KPI metrics, and cross-cube joins without manual SQL construction. It handles everything from simple counts to relative date ranges and granular pagination logic.

主要功能

Advanced time-series grouping with granularities from seconds to years
Support for automatic cross-cube JOINs for multi-source data analysis
Semantic query construction with automated measure and dimension handling
Flexible relative date range filtering using natural language patterns
Comprehensive operator support for numeric, string, and null-state filtering
0 GitHub stars

使用场景

Creating filtered datasets for complex business intelligence reporting
Building time-series charts and trend reports for analytics dashboards
Generating single-metric KPI cards for real-time monitoring

关于

主要功能

Advanced time-series grouping with granularities from seconds to years
Support for automatic cross-cube JOINs for multi-source data analysis
Semantic query construction with automated measure and dimension handling
Flexible relative date range filtering using natural language patterns
Comprehensive operator support for numeric, string, and null-state filtering
0 GitHub stars

使用场景

Creating filtered datasets for complex business intelligence reporting
Building time-series charts and trend reports for analytics dashboards
Generating single-metric KPI cards for real-time monitoring