# data-designer

Creates synthetic datasets and Python data-generation pipelines with Data Designer. Use it when you need to describe a dataset and turn that description into a script.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2500 installs · ★ 3735
- Author: NVIDIA
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/data-designer/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/data-designer

## What it does

This skill builds a synthetic dataset from a written description with the Data Designer library. It uses Interactive mode by default, then switches to Autopilot when the request asks it to make the decisions. It does not inspect the workspace before starting.

It reads one selected workflow file: `workflows/interactive.md` or `workflows/autopilot.md`. The result is a Python file in the current directory, with a `load_config_builder()` function that returns `DataDesignerConfigBuilder` and PEP 723 dependency metadata.

Columns stay in the output unless the user asks to remove one or it only supports other derived columns. The `data-designer` CLI requires Python >= 3.10, so check the environment before generating data. For person data, the skill follows its person-sampling reference; if a matching script exists, it asks whether to edit it or create another file.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill data-designer -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/data-designer/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/data-designer/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill data-designer -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/data-designer/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill data-designer -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/data-designer/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills data-designer
```

2. **Manually: put the files in place**
- File: `.github/skills/data-designer/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill data-designer -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/data-designer
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/data-designer/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill data-designer -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/data-designer/SKILL.md

## When to use it
- create a synthetic dataset from a description
- write a Python data-generation script
- build a `DataDesignerConfigBuilder` configuration
- add Jinja2 templates for column values
- read a numeric score from an LLM judge column

## Example prompts
- Create a synthetic customer review dataset with a quality score.
- Build a data generation pipeline for this schema and write the Python file.
- Make the decisions and generate a dataset with addresses and demographic fields.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: c604c2dfa2e7f282.

BEGIN THIRD-PARTY FILE c604c2dfa2e7f282
```text
---
name: data-designer
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: DataDesigner
---

# 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.

# 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

- **`data-designer` CLI not found:** Tell the user that `data-designer` is not installed in this environment (requires Python >= 3.10). 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.
```
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