Workflow·Data Science & ML

Data Designer

Name: Data Designer
Author: NVIDIA

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

You say

Buy it · $29 Read it before you buy $29 Written by NVIDIA · unverified publisher

Context cost: 6.7k tokensestimated from the bundle, loaded when it triggers
Bundle: 10 files · 26.9 kB1 script among them — read before you run
Licence: Apache-2.0paid listing
Last change: —no release on file
Servers it uses: Noneruns standalone

What it does

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

Installed, it changes the agent in these ways.

What this skill changes about the agent is not written down here yet. The listing was collected from its source, and the description is in its own SKILL.md.

Workflow

Runs a procedure end to end.

synthetic-datadatasetspipelines

Filed under

Data Science & ML

The skill itself

This is the whole product. A skill is instructions the model reads, so there is nothing behind the listing you cannot see first — the front matter loads with every session, and the body below it loads when the skill triggers.

SKILL.md4.7 kB · 95 lines

--- 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 ---

10# Before You Start

12Do not explore the workspace first. The workflow's Learn step gives you everything you need.

14# Goal

16Build a synthetic dataset using the Data Designer library that matches this description:

18$ARGUMENTS

20# Workflow

22Use **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).

24Read **only** the workflow file that matches the selected mode, then follow it:

26- **Interactive** → read workflows/interactive.md

27- **Autopilot** → read workflows/autopilot.md

29# Rules

31- 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.

32- 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.

33- When the dataset requires person data (names, demographics, addresses), read references/person-sampling.md.

34- If a dataset script that matches the dataset description already exists, ask the user whether to edit it or create a new one.

36# Usage Tips and Common Pitfalls

38- **Sampler and validation columns need both a type and params.** E.g., sampler_type="category" with params=dd.CategorySamplerParams(...).

39- **Jinja2 templates** in prompt, system_prompt, and expr fields: reference columns with {{ column_name }}, nested fields with {{ column_name.field }}.

40- **SamplerColumnConfig:** Takes params, not sampler_params.

41- **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.

43# Troubleshooting

45- **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.

46- **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.

48# Output Template

50Write 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.

52```python

53# /// script

54# dependencies = [

55# "data-designer", # always required

56# "pydantic", # only if this script imports from pydantic

57# # add additional dependencies here

58# ]

59# ///

60import data_designer.config as dd

61from pydantic import BaseModel, Field

64# Use Pydantic models when the output needs to conform to a specific schema

65class MyStructuredOutput(BaseModel):

66 field_one: str = Field(description="...")

67 field_two: int = Field(description="...")

70# Use custom generators when built-in column types aren't enough

71@dd.custom_column_generator(

72 required_columns=["col_a"],

73 side_effect_columns=["extra_col"],

74)

75def generator_function(row: dict) -> dict:

76 # add custom logic here that depends on "col_a" and update row in place

77 row["name_in_custom_column_config"] = "custom value"

78 row["extra_col"] = "extra value"

79 return row

82def load_config_builder() -> dd.DataDesignerConfigBuilder:

83 config_builder = dd.DataDesignerConfigBuilder()

85 # Seed dataset (only if the user explicitly mentions a seed dataset path)

86 # config_builder.with_seed_dataset(dd.LocalFileSeedSource(path="path/to/seed.parquet"))

88 # config_builder.add_column(...)

89 # config_builder.add_processor(...)

91 return config_builder

92```

94Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them.

In the file

SKILL.md645 words

Files10

LicenceApache-2.0

Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈60

always loaded

The name and description, so the model knows the skill exists and when to reach for it.

6,665

on trigger

The instruction body and 9 supporting files, read only when the skill fires.

3.4%

of a 200k window

Ten skills this size would take about 34% of the window before you open a file.

050k100k150k200k context window

6.7k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

Adoption

Installsnone yet

Ratingno reviews yet

The procedure it runs

The procedure has not been published here. It is in the skill’s own SKILL.md, which its author has not sent to the marketplace yet.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

It writes no files and reaches no network. All it changes is how the model reasons and writes.

What it asks for

Writes filesno

Network accessno

Read from the allowed-tools line of this skill’s own SKILL.md. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

If this is not it

12 other skills in Data Science & ML

What is in the bundle

10 files, 26.9 kB on disk. Mostly text — the instructions the model reads — with 1 script in it that your client would run only if the instructions tell it to.

BENCHMARK.md3.8 kB
SKILL.md4.7 kB
skill-card.md3.7 kB
evals/evals.json1.5 kB
references/person-sampling.md2.4 kB
references/preview-review.md1.9 kB
references/seed-datasets.md1.2 kB
scripts/get_person_object_schema.py1.8 kB
workflows/autopilot.md2.6 kB
workflows/interactive.md3.3 kB

What is not in it

A skill installs nothing and depends on nothing: it is a folder your client reads. This one carries 1 script beside the text, so the bundle is 10 files you can review in full before installing. The Apache-2.0 licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

$29 once

Data Designer · Apache-2.0 · NVIDIA

one-time

Price$29 once

LicenceApache-2.0 — the author’s, unchanged by this purchase

Paid throughStripe, once, on the card you add at the checkout

Keeps workingfor good — the files are yours once they are on disk

Updatesevery update its author ships, delivered through this account

You can read the whole bundle before paying — the SKILL.md above is the product, not a preview of it. What the money buys is the delivery: the folder packaged and handed to your machine by key, every update its author ships, and our support if it does not do what this listing says. The terms of use are Apache-2.0, set by the author and unchanged by buying it here.

Payment runs through Stripe, on a page like this one rather than a redirect. Once there is an account it joins the same mcprush invoice as everything else you run, so there is never a second card to enter.

Which clients pick it up on their own

A skill is a folder of text. A client with a skills folder reads it without being told; everywhere else the same text works, it is just handed to the model rather than found.

Claude Code.claude/skills/

Claude Desktop

ChatGPT

Cursor.cursor/skills/

VS Code.github/skills/

Codex CLI.agents/skills/

Gemini CLI.gemini/skills/

Grok.grok/skills/

Zed.agents/skills/

Windsurf.windsurf/skills/

Agent SDK.claude/skills/

HTTP / API

This release

Versionnot versioned

Publishedno release date on file

Price$29

Referencenvidia/data-designer

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

No earlier releases have been published to the marketplace.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

Reviews

—no reviews yet · no installs yet

Nobody has reviewed this skill yet. The rating is the mean of the reviews written here, so there is none until somebody writes the first.

Who can post

Only accounts that have had the skill installed for fourteen days, so a review is written after living with it rather than after reading it. Publishers may reply once.

Who wrote it

NVIDIA

Publishes on mcprush.

0 servers listed9 skills listednot claimed

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