nemo-data-designer-plugin

name	nemo-data-designer-plugin
description	Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
argument-hint	["describe the dataset you want to generate"]
license	Apache-2.0
metadata	{"owner":"nemo-platform"}

Before You Start

Do not explore the workspace first. The workflow's Learn step gives you everything you need.

Goal

Build a synthetic dataset using the Data Designer library that matches this description:

$ARGUMENTS

Workflow

Use Autopilot mode if the user implies they don't want to answer questions — e.g., they say something like "be opinionated", "you decide", "make reasonable assumptions", "just build it", "surprise me", etc. Otherwise, use Interactive mode (default).

Read only the workflow file that matches the selected mode, then follow it:

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

Rules

• Keep all columns in the output by default. The only exceptions for dropping a column are: (1) the user explicitly asks, or (2) it is a helper column that exists solely to derive other columns (e.g., a sampled person object used to extract name, city, etc.). When in doubt, keep the column.
• Do not suggest or ask about seed datasets. Only use one when the user explicitly provides seed data or asks to build from existing records. When using a seed, read references/seed-datasets.md.
• When the dataset requires person data (names, demographics, addresses), read references/person-sampling.md.
• If a dataset script that matches the dataset description already exists, ask the user whether to edit it or create a new one.
• For commands and context specific to this NeMo Platform plugin (e.g., sourcing model configs from IGW providers or in-script ModelConfigs, installing or publishing Nemotron Personas locales, platform-side resource pointers), read references/nemo-platform-plugin-additions.md.

Usage Tips and Common Pitfalls

• Sampler and validation columns need both a type and params. E.g., sampler_type="category" with params=dd.CategorySamplerParams(...).
• Jinja2 templates in prompt, system_prompt, and expr fields: reference columns with {{ column_name }}, nested fields with {{ column_name.field }}.
• SamplerColumnConfig: Takes params, not sampler_params.
• LLM judge score access: LLMJudgeColumnConfig produces a nested dict where each score name maps to {reasoning: str, score: int}. To get the numeric score, use the .score attribute. For example, for a judge column named quality with a score named correctness, use {{ quality.correctness.score }}. Using {{ quality.correctness }} returns the full dict, not the numeric score.

Troubleshooting

• nemo data-designer CLI not found: Tell the user that nemo data-designer is not installed in this environment (requires Python >= 3.11). Ask if they would like you to create a virtual environment and install it, or if they prefer to do it themselves. Do not install anything without the user's permission.
• Network errors during preview: A sandbox environment may be blocking outbound requests. Ask the user for permission to retry the command with the sandbox disabled. Only as a last resort, if retrying outside the sandbox also fails, tell the user to run the command themselves.

Output Template

Write a Python file to the current directory with a load_config_builder() function returning a DataDesignerConfigBuilder. Name the file descriptively (e.g., customer_reviews.py). Use PEP 723 inline metadata for dependencies.

# /// script
# dependencies = [
#   "data-designer", # always required
#   "pydantic", # only if this script imports from pydantic
#   # add additional dependencies here
# ]
# ///
import data_designer.config as dd
from pydantic import BaseModel, Field


# Use Pydantic models when the output needs to conform to a specific schema
class MyStructuredOutput(BaseModel):
    field_one: str = Field(description="...")
    field_two: int = Field(description="...")


# Use custom generators when built-in column types aren't enough
@dd.custom_column_generator(
    required_columns=["col_a"],
    side_effect_columns=["extra_col"],
)
def generator_function(row: dict) -> dict:
    # add custom logic here that depends on "col_a" and update row in place
    row["name_in_custom_column_config"] = "custom value"
    row["extra_col"] = "extra value"
    return row


def load_config_builder() -> dd.DataDesignerConfigBuilder:
    config_builder = dd.DataDesignerConfigBuilder(
        # Declaring model configs programmatically here is the portable path:
        # it works for both local `run` and cluster `submit`, while the local
        # YAML registry alternative only works for `run`. The provider below
        
        
        
        model_configs=[
            dd.ModelConfig(
                alias=,
                model=,
                provider=,
                inference_parameters=dd.ChatCompletionInferenceParams(),
            ),
        ],
    )

    
    

    
    

     config_builder

Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them. Prefer including model_configs when the dataset uses LLM columns — declaring it in the script keeps the config portable between local run and cluster submit, while the local YAML registry alternative only works for run.