create-ruleset

To create a synthetic data ruleset, you can use an interactive interview process to define domain details, core rules, and edge-case coverage. The final output is a structured markdown template saved to a designated directory for immediate use.

An interview-driven ruleset is a formal document defining synthetic data constraints, created by collecting domain requirements through an interactive Q&A process. This approach reduces ambiguity and accelerates dataset design by capturing context, defaults, and edge cases upfront.

Yes, the ruleset template applies to any domain requiring structured data generation rules. It accommodates dataset definitions, data augmentation guidelines, and quality constraints, ensuring comprehensive coverage regardless of the specific field or data structure.

The best way to define edge cases and defaults for synthetic data validation is through a guided interview that captures domain-specific constraints. The process outputs a formal ruleset document that explicitly includes context, default values, and edge-case coverage.

To start the interview process for defining data generation rules, provide the domain name and key constraints. The skill then conducts an interactive interview to gather necessary details before generating the final structured ruleset artifact.

Generated synthetic data rulesets are saved as markdown files to a specific path: dataset/rulesets/[domain_name].md. The domain name is formatted in lowercase with underscores, ensuring clear provenance and easy file retrieval.