Building Dbt Semantic Layer

This one handles the full lifecycle of dbt's Semantic Layer: semantic models, entities, dimensions, and all five metric types (simple, derived, cumulative, ratio, conversion). It's smart about routing you to the right YAML spec based on your dbt version, and if you're on 1.12+ it'll offer to migrate legacy configs with dbt-autofix. The guide works backwards from business questions or forwards from existing models, which makes it flexible whether you're starting fresh or retrofitting analytics onto established tables. One caveat: cumulative metrics need a time spine configured separately, and the spec has some gotchas around grain-to-date versus window parameters. Solid reference if you're moving beyond raw SQL aggregations into governed metric definitions.

skills.sh installs: 1K
GitHub stars: 759
last updated: Aug 21, 2026

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Install

$ npx -y skills add dbt-labs/dbt-agent-skills --skill building-dbt-semantic-layer --agent claude-code

Installs into .claude/skills of the current project.

Files

Building the dbt Semantic Layer

This skill guides the creation and modification of dbt Semantic Layer components: semantic models, entities, dimensions, and metrics.

Semantic models - Metadata configurations that define how dbt models map to business concepts
Entities - Keys that identify the grain of your data and enable joins between semantic models
Dimensions - Attributes used to filter or group metrics (categorical or time-based)
Metrics - Business calculations defined on top of semantic models (e.g., revenue, order count)

Additional Resources

Time Spine Setup - Required for time-based metrics and aggregations
Best Practices - Design patterns and recommendations for semantic models and metrics
Latest Spec Authoring Guide - Full YAML reference for dbt Core 1.12+ and Fusion
Legacy Spec Authoring Guide - Full YAML reference for dbt Core 1.6-1.11