Designs, reviews, and hardens Kimball-style dimensional models: star schemas, conformed dimensions, bus matrix, slowly changing dimensions. Use when modeling analytical data for a warehouse, lakehouse, or BI semantic layer: declaring a fact table's grain, deciding whether something is a fact or a dimension attribute, picking an SCD type for an attribute that changes, or drafting the bus matrix that makes cross-process reporting reconcile. MUST be used whenever star-schema DDL, a dbt mart, or a model spec is reviewed, checked over, or handed to an ETL or engineering team - mixed grains, nullable foreign keys, descriptive attributes in fact tables, snowflaked dimensions and a type 2 dimension keyed on its business key are cheap to catch there and expensive to catch once history is loaded. Also use when preparing or facilitating a modeling workshop with business stakeholders, and when inheriting an unfamiliar warehouse whose reports disagree with each other. Ships an offline checker (scripts/dimcheck.py).
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryProduct & designStars0
About this skill
Designs, reviews, and hardens Kimball-style dimensional models: star schemas, conformed dimensions, bus matrix, slowly changing dimensions. Use when modeling analytical data for a warehouse, lakehouse, or BI semantic layer: declaring a fact table's grain, deciding whether something is a fact or a dimension attribute, picking an SCD type for an attribute that changes, or drafting the bus matrix that makes cross-process reporting reconcile. MUST be used whenever star-schema DDL, a dbt mart, or a model spec is reviewed, checked over, or handed to an ETL or engineering team - mixed grains, nullable foreign keys, descriptive attributes in fact tables, snowflaked dimensions and a type 2 dimension keyed on its business key are cheap to catch there and expensive to catch once history is loaded. Also use when preparing or facilitating a modeling workshop with business stakeholders, and when inheriting an unfamiliar warehouse whose reports disagree with each other. Ships an offline checker (scripts/dimcheck.py).
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Designs, reviews, and hardens Kimball-style dimensional models: star schemas, conformed dimensions, bus matrix, slowly changing dimensions. Use when modeling analytical data for a warehouse, lakehouse, or BI semantic layer: declaring a fact table's grain, deciding whether something is a fact or a dimension attribute, picking an SCD type for an attribute that changes, or drafting the bus matrix that makes cross-process reporting reconcile. MUST be used whenever star-schema DDL, a dbt mart, or a model spec is reviewed, checked over, or handed to an ETL or engineering team - mixed grains, nullable foreign keys, descriptive attributes in fact tables, snowflaked dimensions and a type 2 dimension keyed on its business key are cheap to catch there and expensive to catch once history is loaded. Also use when preparing or facilitating a modeling workshop with business stakeholders, and when inheriting an unfamiliar warehouse whose reports disagree with each other. Ships an offline checker (scripts/dim_check.py).
About this listing
Category
Product & design
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI