Can I define metrics at the model level?

Yes, the skill supports both column-level definitions for simple aggregations and model-level definitions for more complex calculations.

How does the skill handle SQL variables in metrics?

It follows standard SQL templating patterns, supporting the use of ${field} and user attributes to create dynamic metrics.

What types of aggregations are supported?

It supports all standard aggregations such as sum, average, count, and custom SQL filters as defined in the Lightdash and dbt documentation.

Does this skill support older versions of dbt?

No, this skill is specifically optimized for dbt 1.10+ and uses the config.meta.metrics structure required by modern Lightdash implementations.

Will this skill help avoid duplicate metric definitions?

The skill includes a rule to verify existing calculations using the read-metrics capability before adding new ones to prevent redundancy.

Lightdash Metric Editor

Name: Lightdash Metric Editor
Author: yu-iskw

byyu-iskw

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분석 및 모니터링

Manages and modifies aggregated metrics within the Lightdash semantic layer using dbt 1.10+ syntax.

The Lightdash Metric Editor skill streamlines the process of defining and updating metrics within the Lightdash semantic layer by leveraging modern dbt 1.10+ standards. It enables developers to create precise atomic aggregations, such as sums and averages, directly within dbt YAML files using the config.meta.metrics structure. By providing standardized guidance on column-level and model-level definitions, the skill ensures that business logic remains consistent between the data transformation layer and the BI environment, facilitating a more maintainable and scalable analytics infrastructure.

주요 기능

01Enables atomic aggregation types including sums, counts, and averages

02Implements dbt 1.10+ meta-metrics syntax for Lightdash

03Supports both column-level and model-level metric definitions

04Ensures adherence to Fusion standards and best practices

053 GitHub stars

06Utilizes SQL templating for dynamic field references and user attributes

사용 사례

01Updating existing Lightdash metrics to reflect changes in business logic

02Standardizing aggregation patterns across a distributed dbt semantic layer

03Defining new Key Performance Indicators (KPIs) in dbt project files

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yu-iskw/dbt-heroes edit-metrics

For use in Claude.ai and ChatGPT