nemo-clinical-data-designer

name	nemo-clinical-data-designer
license	Apache-2.0
description	Use when generating synthetic tabular datasets via Data Designer — sampler columns, LLM columns, custom generators. Not for ASR audio.
argument-hint	["describe the dataset you want to generate"]
metadata	{"author":"Voice Eval Flywheel team <[e-mail]>","tags":["nvidia","data-designer","synthetic-data","dataset-generation","llm","sampler"]}

NeMo Clinical Data Designer

Purpose

Produce a synthetic tabular dataset by authoring a NeMo Data Designer config (data_designer.config), previewing the output, then committing to a full run. The agent first picks a conversation mode (Interactive vs. Autopilot), drives the user through config construction, and ends at a written-to-disk dataset.

Instructions

The flow is five steps, in order:

1. Decide on conversation mode. Default is Interactive — ask the user clarifying questions as you build. Switch to Autopilot whenever the user signals they want an opinionated build with minimal back-and-forth ("just build it", "you decide", "surprise me").
2. Open the matching per-mode runbook — workflows/interactive.md or workflows/autopilot.md — and follow it. The runbooks are the source of truth for which questions to ask and what defaults to pick.
3. Author a Python config module exposing load_config_builder() that returns a DataDesignerConfigBuilder. The Output Template section near the end of this file shows the skeleton (PEP 723 deps header + samplers + LLM columns + optional Pydantic schema + custom generators).
4. Preview before generating: run data-designer preview against the config and show the user the sample rows.
5. After approval, run data-designer run (the run_script entry point) for the full generation. If the dataset references person attributes, first inspect the person-object schema via python3 scripts/get_person_object_schema.py.

Examples

Worked example dialogues live in the per-mode runbooks; the SKILL.md doesn't duplicate them:

• For a question-driven build with full user collaboration → workflows/interactive.md.
• For a hands-off "you decide" build → workflows/autopilot.md.

A representative Python config — sampler column + LLM column + Pydantic-typed output + custom column generator — is laid out in the Output Template section further down.

Scripts

Script	Purpose
scripts/get_person_object_schema.py	Inspect the Data Designer person-object field schema (names, demographics, addresses) before adding person-sampler columns to a config. Invoke via python3 scripts/get_person_object_schema.py.

Prerequisites

• A Python ≥ 3.10 virtualenv (Data Designer requires it).
• The data-designer package itself — install via pip install data-designer.
• If the config you build references LLM columns or LLM judges, the user must have valid LLM credentials configured per Data Designer's own auth conventions.
• pydantic is only needed when the config declares structured outputs; the PEP 723 header in the generated script will fetch it automatically.

Limitations

• Output domain is tabular synthetic data exclusively — no audio, images, or unstructured documents. Clinical-ASR audio benchmarks belong to /digital-health-clinical-asr-build, not here.
• The skill writes the Python config file but does not invoke the generation pipeline on the user's behalf — the user runs data-designer preview and data-designer run themselves.
• LLM columns require network reachability at preview and run time. In sandboxed environments those calls may be blocked; see Troubleshooting for the sandbox-disable handshake.

Brief to satisfy

$ARGUMENTS

The brief above is what you need to translate into a Data Designer config. Don't open additional workspace files to "look around" — the selected workflow runbook walks you through any context-gathering you actually need (its Learn step). Trust the runbook over instinct here.

Mode selection + runbook

A simple heuristic: if the user's framing is opinionated-and-hands-off ("be opinionated", "you decide", "make reasonable assumptions", "just build it", "surprise me", and similar), pick Autopilot. For everything else, default to Interactive so the user shapes column choices.

After selecting, read only that mode's runbook and execute it linearly:

• Interactive path → workflows/interactive.md.
• Autopilot path → workflows/autopilot.md.

Authoring rules

A handful of constraints that hold regardless of which mode you're in:

• Output column retention defaults to keep everything. Only drop a column when the user explicitly asks, or when the column is a pure intermediate (e.g., a person-object sampler whose fields you extract into discrete columns downstream). Err toward keeping; the user can always trim later.
• Seed datasets are never suggested unilaterally. Only wire one in when the user volunteers seed data or asks to build off existing records. If they do, consult references/seed-datasets.md before configuring the seed source.
• Person attributes (names, demographics, addresses) require the person-sampling reference — read references/person-sampling.md before adding any person columns.
• If a config script already in the workspace matches the brief, surface the conflict to the user and ask whether to edit it in place or author a new one. Never silently overwrite.

Usage Tips and Common Pitfalls

• Sampler / validation columns require both a type and a params object. Pair sampler_type="category" with params=dd.CategorySamplerParams(...); either alone won't validate.
• Jinja2 in prompt, system_prompt, and expr — top-level columns are {{ column_name }}, nested fields are {{ column_name.field }}.
• SamplerColumnConfig constructor takes params — not sampler_params. Easy to typo if you're new to the library.
• Reading LLM-judge scores. LLMJudgeColumnConfig returns a nested dict shaped {score_name: {reasoning: str, score: int}}. To pull the integer score in a Jinja template for a judge column quality with score correctness, write {{ quality.correctness.score }}. Dropping the trailing .score yields the full dict, which is almost never what you want.

Troubleshooting

• data-designer: command not found → the package isn't installed in this environment. Tell the user, confirm they're on Python ≥ 3.10, and offer to create a venv + run pip install data-designer for them — but wait for explicit permission before installing.
• Preview fails on network errors → most likely a sandbox is blocking outbound calls to the LLM endpoint. Ask whether to retry with the sandbox disabled. If that still fails, ask the user to run the command in their own shell.

Output deliverable

A single Python file in the user's working directory exporting load_config_builder() → DataDesignerConfigBuilder. Filename should describe the dataset (customer_reviews.py, medical_term_seeds.py), not be generic. Use a PEP 723 inline metadata dep header so the file is runnable with data-designer run directly.

For the canonical skeleton — Pydantic-typed output, custom column generators, optional seed-dataset wiring — read references/output-template.md. Strip blocks from the skeleton when the brief doesn't need them.