What problem does it solve?
This Skill eliminates the complexity and potential for errors in designing, validating, and integrating data pipelines. It ensures adherence to robust architectural patterns like SPEC-1, standardizing schema design, data modeling, identity resolution, and data quality frameworks. This saves time, reduces rework, and builds trust in your data platform.
Core Features & Use Cases
- Standardized Pipeline Design: Integrate new data sources (e.g., nflverse, KTC, Commissioner Sheets) following established batch ingestion patterns and storage layouts.
- Kimball Dimensional Modeling: Design and validate dbt models (staging, facts, dimensions, marts) for 2x2 stat models (actuals vs. projections, real-world vs. fantasy) and slowly changing dimensions.
- Robust Data Quality & Identity Resolution: Implement comprehensive data quality checks (grain, FK, enums) and canonical player ID mapping across 19 providers.
- Use Case: You need to integrate a new data source for player projections. This Skill guides you through defining the ingestion pattern, mapping player IDs to the canonical
mfl_id, creating staging and fact tables that comply with the 2x2 model, and setting up all necessary dbt tests to ensure data quality.
Quick Start
Help me design a new data source integration for KTC market values, ensuring it follows SPEC-1 patterns for batch ingestion, player identity resolution, and data quality testing.
