synth-data-generate

Synthetic Data Generator

Goal

Generate realistic, comprehensive synthetic data for subject in a Jupyter notebook only after the applicable dataops synthetic-data operation preflight passes. Use example_data when the user supplies an existing data source, schema, or file to use as a strict reference for structure and patterns.

Inputs

• subject: Required. User query describing the subject for synthetic data generation.
• example_data: Optional. Free-form input describing an existing data source, schema, or file to use as a reference for structure and patterns.

Required steps

Step 0: Validate synthetic-data operation preflight

• Activate the dataops skill by stable name and read its synthetic-data operation contract.
• Require a SYNTHETIC_DATA_OPERATION_V1 preflight record before creating a project, installing packages, creating a notebook, accessing a source, or generating data.
• Run the dataops validate command. Continue only when the gate passes with a current approved applicable classification decision carrying data-owner authority.
• Treat protected-attribute generation and subgroup evaluation as inactive unless current qualified decision references explicitly mark them applicable. Protected-attribute generation requires privacy, Responsible AI or fairness, and domain-owner roles. Subgroup evaluation requires Responsible AI or fairness and domain-owner roles plus every activated subgroup reference.
• For replace-local, require one existing regular local file under a caller-approved root, a matching expected SHA-256 source digest, and current approved applicable data-owner replacement authority. Reject directories, links, remote stores, network shares, databases, APIs, and multi-target operations.
• If validation fails, stop before any write or source access. Report only stable categories, record identifiers, counts, and digests, plus the smallest qualified-owner action needed to resume. Do not reproduce source values in chat or records.