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Found 64 Skills
Generate technical plan, data model, and interface contracts from spec.md
Create and edit Omni Analytics semantic model definitions — views, topics, dimensions, measures, relationships, and query views — using YAML through the Omni CLI. Use this skill whenever someone wants to add a field, create a new dimension or measure, define a topic, set up joins between tables, modify the data model, build a new view, add a calculated field, create a relationship, edit YAML, work on a branch, promote model changes, or any variant of "model this data", "add this metric", "create a view for", or "set up a join between". Also use for migrating modeling patterns since Omni's YAML is conceptually similar to other semantic layer definitions.
Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.
SQL patterns for query optimization, schema design, and data modeling
Expert-level Looker BI, LookML, explores, dimensions, measures, dashboards, and data modeling
Design a FHIR R4 data model for a healthcare application by mapping clinical concepts to resources, terminology, and implementation-ready relationships.
Create, validate, and modify Infrahub schemas. Use when designing data models, creating schema nodes with attributes and relationships, validating schema definitions, or planning schema migrations for Infrahub.
Expert-level Power BI, DAX, M language, data modeling, Power Query, report design, and paginated reports
Help design database schemas, create tables, and plan data models. Activates when users ask to create tables, design schemas, or model data relationships.
Deep dive into LookML includes, refinements (layering), and project structure best practices. Essential for mastering Looker's object-oriented capabilities.
Use this skill when you need to create or modify a LookML Explore. This includes defining the Explore, joins, access grants, and basic configuration.
Design data architecture at enterprise and solution levels. Cover data mesh, lakehouse, governance, domain-driven design, conceptual/logical/physical data modeling, platform selection, and compliance frameworks. Produce ADRs, data model diagrams, platform comparison matrices, and governance policy templates. Triggers on "design data platform", "choose data warehouse", "data mesh", "lakehouse architecture", "data governance", "data modeling", "platform selection", "data architecture decision", "compliance framework", or "data strategy". For applied AI solution architecture (RAG data plane, embeddings, vector stores in commercial or enterprise products), use applied-ai-architect-commercial-enterprise. For dbt analytics layers and mart delivery, use analytics-data-engineer—not data-architect.