What problem does it solve?
This Skill eliminates the ambiguity and misalignment between business stakeholders and data engineering teams by translating messy, unstructured business requests, stakeholder interviews, source system documentation, and data catalogs into formal, actionable Data Requirements Documents (DRDs) that serve as a single source of truth for data project requirements.
Core Features & Use Cases
- Structured Requirements Elicitation: Uses a gap-based Q&A loop with targeted multiple-choice questions to fill missing or vague requirements across all DRD sections, ensuring no assumptions or incomplete information make it into the final document.
- Live Source Data Verification: Queries actual source databases using read-only commands to verify table existence, row counts, column schemas, and data quality metrics, eliminating reliance on outdated or inaccurate document estimates.
- Standardized DRD Generation: Produces business-friendly, consistent DRDs following a predefined Jinja2 template, with built-in validation, session memory for future reference, and automatic learning capture from user corrections.
- Use Case: For example, a healthcare data team building a patient 360 search tool can use this Skill to take stakeholder interview notes, Synthea source system docs, and existing data catalogs to generate a complete DRD with all required sections including business context, data quality rules, consumer SLAs, and HIPAA compliance requirements.
Quick Start
Provide your business request, stakeholder interview notes, source system documentation, and data catalog files, then ask the AI to generate a complete, validated Data Requirements Document for your data project.
