Data Designer

NVIDIAOfficialPublished by NVIDIA

Skill
Workflow
Data and AI
Apache-2.0
Medium risk
Has scripts

From the author

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. The argument is the description of the dataset to generate.

— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The data-designer skill provides a structured workflow for creating synthetic datasets or building data generation pipelines. It guides users through defining dataset requirements, configuring column samplers, and implementing custom generation logic. The skill supports both interactive and automated modes, ensuring that users can either collaborate on the design or allow the agent to make opinionated decisions. It includes specific references for handling person-related data and validating generated content, ensuring the output is statistically sound and aligned with user specifications.

What it does

Generates synthetic datasets based on user-provided descriptions.
Builds data generation pipelines using the Data Designer library.
Supports custom column generators for complex data logic.
Provides guidance on sampling person data, including demographics and personas.
Offers an automated mode for opinionated, hands-off dataset creation.
Includes a structured output template using Python and Pydantic.

When to use it

When you need to create a synthetic dataset for testing or training purposes.
When you require a repeatable data generation pipeline.
When you need to generate realistic person data with specific demographic or persona traits.
When you want to define complex data relationships that require custom generation logic.

Advantages

Ensures consistent data generation through a standardized configuration builder.
Provides flexible sampling options, including census-grounded person data.
Allows for easy integration of custom logic via Python decorators.
Supports structured output schemas using Pydantic models.
Reduces manual effort by offering an automated generation mode.

Usage examples

“Create a synthetic dataset of 100 customer reviews for a coffee shop, including ratings and sentiment scores.”: The agent will generate a Python script using Data Designer to produce a parquet file containing the requested customer review data.
“I need a dataset of user profiles with realistic names, addresses, and personality traits. Just build it for me.”: The agent will use Autopilot mode to configure a dataset with person samplers and synthetic personas, outputting the configuration file.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/data-designer/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip data-designer.zip -d .agents/skills

Documentation by NVIDIA

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 >= 3.11). 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()

    # 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

ATTRIBUTION.mdAdded by AutoGineer825 B
BENCHMARK.md9.1 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md4.6 KiB
evals/config.yml1.5 KiB
evals/evals.json1.4 KiB
references/person-sampling.md2.3 KiB
references/preview-review.md1.8 KiB
references/seed-datasets.md1.1 KiB
scripts/get_person_object_schema.pyScript1.7 KiB
skill-card.md4.7 KiB
skill.oms.sig6.1 KiB
workflows/autopilot.md2.6 KiB
workflows/interactive.md3.2 KiB

BENCHMARK.md

Skill Benchmark: data-designer

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

Skill: data-designer
Validation status: passed
Evaluation date: 2026-10-29
Evaluator version: 1.6.4
Agents: Claude Code (aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)
Tasks: 1 evaluation tasks (1 positive)
Dataset digest: sha256:deb7bb002c1a418bbb6f2f9a51c88f65380e4befd2e9ccc4e83a1678dd69f436 (skill-evaluator-dataset-snapshot/1)
Attempts per task: 1
Environment: k8s-sandbox
Tier 2 evidence: required for publication
Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

Tier 1 LLM Provenance

SkillSpector provider, model, analyzer revision, and determinism evidence retained for this scan.

Skill	Provider	Adapter	Analyzer	Model	Revision	Determinism
data-designer	nv_inference	openai	mcp_tool_poisoning	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.2`	nondeterministic / provider_defaults
data-designer	nv_inference	openai	semantic_developer_intent	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.2`	nondeterministic / provider_defaults
data-designer	nv_inference	openai	semantic_quality_policy	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.2`	nondeterministic / provider_defaults
data-designer	nv_inference	openai	semantic_security_discovery	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.2`	nondeterministic / provider_defaults
data-designer	nv_inference	openai	meta_analyzer	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.2`	nondeterministic / provider_defaults

Controls for data-designer: temperature=not set (source=provider_default, forwarded=no); seed=not set (source=unset, forwarded=no); reasoning_effort=not set (source=provider_default, forwarded=no)

What This Report Answers

The three-tier evaluation checks whether the skill:

is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.

Results at a Glance

Measure	Claude Code (Baseline → Skill Uplift)	Codex (Baseline → Skill Uplift)
Overall	96.1% — baseline ran, but no comparable score was available; uplift unavailable	94.7% — baseline ran, but no comparable score was available; uplift unavailable
Security	100.0% → 100.0% (±0.0 points)	50.0% → 100.0% (+50.0 points)
Correctness	80.0% → 100.0% (+20.0 points)	100.0% → 100.0% (±0.0 points)
Discoverability	95.0% — baseline ran, but no comparable score was available; uplift unavailable	80.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness	100.0% → 100.0% (±0.0 points)	100.0% → 100.0% (±0.0 points)
Efficiency	85.4% — baseline ran, but no comparable score was available; uplift unavailable	93.7% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Comparison details

Shared-source comparisons use the same signals and weights on both conditions. They do not replace the full dimension scores above or change the publication verdict.

Agent	Dimension	Basis	Baseline → With skill (uplift)	Explanation
claude-code	Discoverability	Dimension comparison	N/A	Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
claude-code	Efficiency	Token-only comparison	77.4% → 88.9% (+11.5 points)	Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.
codex	Discoverability	Dimension comparison	N/A	Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
codex	Efficiency	Token-only comparison	84.7% → 87.4% (+2.7 points)	Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

Agent	Dataset case	With skill	Without skill	Delta	Change	Coverage
claude-code	All cases	502,369	635,144	-132,775	-20.90%	skill 1/1; base 1/1
claude-code	data-designer-001	502,369	635,144	-132,775	-20.90%	skill 1/1; base 1/1
codex	All cases	194,327	246,199	-51,872	-21.07%	skill 1/1; base 1/1
codex	data-designer-001	194,327	246,199	-51,872	-21.07%	skill 1/1; base 1/1
ALL AGENTS	Dataset aggregate	696,696	881,343	-184,647	-20.95%	skill 2/2; base 2/2

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. Change is 100 * (with - without) / without; negative means fewer tokens. N/A comparisons include their reason in the coverage column; coverage is never estimated.

Tier Status

Tier	Purpose	Status	Evidence
Tier 1	Static validation	PASSED WITH OBSERVATIONS	11 validator(s); 17 finding(s)
Tier 2	Semantic deduplication	PASSED	2 validator(s); 0 finding(s)
Tier 3	Live agent evaluation	PASS	2 agent(s); 1 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

MEDIUM QUALITY/quality_correctness: No documented scripts in table format (skills/data-designer/SKILL.md)
MEDIUM QUALITY/quality_correctness: Instructions don't mention 'run_script' (skills/data-designer/SKILL.md)
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.author' (skills/data-designer/SKILL.md)
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (skills/data-designer/SKILL.md)
MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/data-designer/SKILL.md)
12 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

Dimension	Question	Scored signals
Security	Is it safe to use?	`security` (100%)
Correctness	Is the answer correct?	`accuracy` (100%)
Discoverability	Was the right skill loaded when needed?	`skill_execution` (100%)
Effectiveness	Did the skill help complete the task?	`goal_accuracy` (50%) + `behavior_check` (50%)
Efficiency	Did it avoid wasted tool calls and token usage?	`skill_efficiency` (50%) + `token_efficiency` (50%)

Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).
Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

security (Security): unsafe operations, secret leakage, and unauthorized access.
skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.
skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).
accuracy (Accuracy): final-answer correctness against the reference answer.
goal_accuracy (Goal Accuracy): whether the user's goal was achieved.
behavior_check (Behavior Check): whether the expected workflow behavior was followed.
token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs
Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

NVIDIA

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/data-designer · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download data-designer.zip