Individual skill

semantic-model-builder

Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.

02-documentation-knowledge/semantic-model-builder/SKILL.md

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Repository stars (not skill ratings): 519
Repository last updated: Nov 7, 2026
License: MIT

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claude-code Repository topic
codex Repository topic
data-analytics Repository topic
skills Repository topic
data Derived category
docs Derived category

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SKILL.mdcore · 2,611 bytes
SKILL.mdcore · 2,611 bytes
assets/dimension_definition.yamlasset · 892 bytes
assets/entity_definition.yamlasset · 1,073 bytes
assets/metric_definition.yamlasset · 1,500 bytes
references/dbt_semantic_layer_guide.mddoc · 4,442 bytes
references/dimension_hierarchy_patterns.mddoc · 3,949 bytes
references/metric_definition_framework.mddoc · 3,415 bytes
scripts/metric_template_generator.pyruns · 4,332 bytes
scripts/model_yaml_validator.pyruns · 5,141 bytes

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# When to use
- A stakeholder asks "how is [metric] calculated?" and no canonical definition exists
- You're setting up dbt Semantic Layer and need YAML metric/dimension/entity definitions
- Multiple teams are using different SQL queries for the same metric — you need to codify the one true definition
- You're building a data catalog entry for a core model and need structured metadata

# Process
1. **Identify the object type** — decide whether you're documenting a metric, a dimension, or an entity. Use the frameworks in `references/metric_definition_framework.md` for metrics and `references/dimension_hierarchy_patterns.md` for dimensions.
2. **Gather the definition inputs** — collect: calculation logic (SQL or formula), business context, data source(s), grain, edge cases, and known gotchas. Ask the data owner if anything is unclear.
3. **Generate the YAML template** — run `scripts/metric_template_generator.py` to scaffold the initial YAML structure for the object type. Fill in the generated template.
4. **Validate the YAML** — run `scripts/model_yaml_validator.py` to check required fields, type constraints, and reference integrity (referenced dimensions exist in the same file).
5. **Add dbt context** — if this will be deployed to dbt Semantic Layer, consult `references/dbt_semantic_layer_guide.md` for the exact field names and constraints for your dbt version.
6. **Save final definitions** — save metrics to `assets/metric_definition.yaml`, dimensions to `assets/dimension_definition.yaml`, entities to `assets/entity_definition.yaml`.