/data-designer

Interactive Workflow

This is an interactive, iterative design process. Do not disengage from the loop unless the user says they are satisfied.

1. Resolve CLI command — Run command -v data-designer 2>/dev/null || (test -x .venv/bin/data-designer && realpath .venv/bin/data-designer) || echo CLI_NOT_FOUND.

• If the output is a path, use it as the data-designer executable for all commands in this workflow.
• If the output is CLI_NOT_FOUND, STOP and follow the Troubleshooting section in SKILL.md. Do not continue to the next step.

2. Learn — Run data-designer agent context.

• If no model aliases are configured, stop and tell the user to run data-designer config to set them up before proceeding.
• Inspect schemas for every column, sampler type, validator, and processor you plan to use.
• Never guess types or parameters — read the relevant config files first.
• Always read base.py for inherited fields shared by all config objects.

3. Clarify — Ask the user clarifying questions to narrow down precisely what they want.

• Optimize for a great user experience: prefer a structured question tool over plain text if one is available, batch related questions together, keep the set short, provide concrete options/examples/defaults where possible, and use structured inputs (single-select, multi-select, free text, etc.) when they make answering easier.
• If multiple model aliases are available, ask which one(s) to use (or default to an alias with the appropriate generation_type for each column).
• Common things to make precise:
  • What the "axes of diversity" are — what should be well represented and diverse in the resulting dataset.
  • The kind and nature of any input data.
  • What variables should be randomized.
  • The schema of the final dataset.
  • The structure of any required structured output columns.
  • What facets of the output dataset are important to capture.

4. Plan — Determine columns, samplers, processors, validators, and other dataset features needed. Present the plan to the user and ask if they want any changes before generating a preview.
5. Build — Write the Python script with load_config_builder() (see Output Template in SKILL.md).
6. Validate — Run data-designer validate <path>. Address any warnings or errors and re-validate until it passes.
7. Preview — Run data-designer preview <path> --save-results to generate sample records as HTML files.

• Note the sample records directory printed by the data-designer preview command
• Give the user a clickable link: file://<sample-records-dir>/sample_records_browser.html

8. Iterate
   • Ask the user for feedback.
   • Offer to review the records yourself and suggest improvements. If the user accepts, read references/preview-review.md for guidance.
   • Apply changes, re-validate, and re-preview. Repeat until the user is satisfied.
9. Finalize — Once the user is happy, tell them they can run the following command to create the dataset:

• data-designer create <path> --num-records <N> --dataset-name <name>.
• Caution the user that generation speed depends heavily on the dataset configuration and their inference setup.
• Do not run this command yourself — the user should control when it runs.