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Found 46 Skills
SQL patterns for query optimization, schema design, and data modeling
Expert-level Looker BI, LookML, explores, dimensions, measures, dashboards, and data modeling
Create and manage NocoBase data models via MCP. Use when users want to inspect or change collections, fields, relations, or view-backed schemas in a NocoBase app.
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.
Execute the implementation planning workflow using the plan template to generate design artifacts.
Interactive skill for eliciting, formalizing, and persisting DynamoDB access patterns. Use when the user wants to start designing a DynamoDB table, define entities, or document how their application will read and write data. This is Step 1 of a 3-step pipeline: access patterns -> table design -> query interfaces. The output is a structured .md file that feeds into the dynamodb-table-design skill.