Official agent skill

Data Designer

A skill your agent uses when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

OfficialApache-2.0Auto-check: warningsTesting & QA

Install Data Designer

The automated check flagged lines worth reading first. See the safety section below.

skills CLI

$ npx skills add NVIDIA/skills --skill data-designer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills data-designer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-designer .claude/skills/data-designer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "data-designer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/data-designer into .claude/skills/data-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-designer", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/data-designer

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill data-designer -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills data-designer --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-designer .agents/skills/data-designer && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "data-designer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/data-designer into .agents/skills/data-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-designer", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill data-designer -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills data-designer --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-designer .cursor/skills/data-designer && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "data-designer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/data-designer into .cursor/skills/data-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-designer", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/data-designer

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill data-designer -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills data-designer --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-designer .gemini/skills/data-designer && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "data-designer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/data-designer into .gemini/skills/data-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-designer", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills data-designer

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill data-designer -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-designer .github/skills/data-designer && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "data-designer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/data-designer into .github/skills/data-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-designer", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill data-designer -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills data-designer --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-designer .opencode/skills/data-designer && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "data-designer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/data-designer into .opencode/skills/data-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-designer", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: data-designer
GitHub stars: 3.5k
Token cost: ~1.2k tokens
SKILL.md length: 440 words
Files: 11 (incl. scripts, references)
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

The user wants to create a dataset
Runs Python scripts from its folder
Generate synthetic data
Build a data generation pipeline

What it does

Data Designer is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/person-sampling.md`).

It sits in Testing & QA, covering Test data and fixtures. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

The user wants to create a dataset
Generate synthetic data
Build a data generation pipeline

Example prompts

“/data-designer”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Designer loads about 1.2k tokens when it runs, and up to ~2.5k if it reads all its reference files. Until then it costs about 30 tokens; SKILL.md has 440 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~30
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

WarningContains instruction-override wording (e.g. “without asking the user”)SKILL.md:45
```
it themselves. Do not install anything without the user's permission.
```

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 440 words, ~1,178 tokens.

Download SKILL.mdSave it as .claude/skills/data-designer/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.

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.

Show full SKILL.md (140 more words)Show less

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.
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.

python

# /// 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()

    # Seed dataset (only if the user explicitly mentions a seed dataset path)
    # config_builder.with_seed_dataset(dd.LocalFileSeedSource(path="path/to/seed.parquet"))

    # config_builder.add_column(...)
    # config_builder.add_processor(...)

    return config_builder

Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them.

Files

SKILL.md and 10 other files (scripts, references) in skills/data-designer of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
references/person-sampling.md
references/preview-review.md
references/seed-datasets.md
scripts/get_person_object_schema.py
skill-card.md
skill.oms.sig
workflows/autopilot.md
workflows/interactive.md

Open the folder on GitHubat commit 0e0d506

Compare with similar skills

Data Designer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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RTK Filter TDD in Rustrtk-ai/rtk	83k	—	~1.9k	Automated safety check: Notes	Apache-2.0	today
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Questions about Data Designer

What does Data Designer do?

A skill your agent uses when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. Data Designer is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

When should I use Data Designer?

Data Designer fits situations like: the user wants to create a dataset; generate synthetic data; build a data generation pipeline.

How do I install Data Designer in Claude Code?

Run `npx skills add NVIDIA/skills --skill data-designer -a claude-code`. Or copy the skill folder (skills/data-designer in NVIDIA/skills) into .claude/skills/data-designer in your project. Claude Code loads it when a task matches its description.

How do I install Data Designer in Codex?

Run `npx skills add NVIDIA/skills --skill data-designer -a codex`. Or copy the skill folder (skills/data-designer in NVIDIA/skills) into .agents/skills/data-designer in your project. Codex loads it when a task matches its description.

Can I use Data Designer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill data-designer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-designer, .gemini/skills/data-designer, .github/skills/data-designer and .opencode/skills/data-designer in your project.

What does Data Designer need to run?

Going by SKILL.md and its folder, Data Designer needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Data Designer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Data Designer safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): contains instruction-override wording (e.g. “without asking the user”). Read the flagged lines before installing; the check is not a guarantee either way. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Data Designer use?

Data Designer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Designer use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Data Designer?

Skills that share tags, products or a category with Data Designer: Jest Testing Patterns (ChrisWiles/claude-code-showcase, 6.1k stars), Java SDK E2E Test with Replay Snapshot (github/copilot-sdk, 11k stars), source-mssql E2E Test Harness (airbytehq/airbyte, 22k stars) and RTK Filter TDD in Rust (rtk-ai/rtk, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Designer?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Data Designer

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Before You Start

Goal

Workflow

Rules

Usage Tips and Common Pitfalls

Troubleshooting

Output Template

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Data Designer