data-designer
FeaturedUse when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
AI & Automation 3,042 stars
352 forks Updated today Apache-2.0
Install
Quality Score: 99/100
Stars 20%
Recency 20%
Frontmatter 20%
Documentation 15%
Issue Health 10%
License 10%
Description 5%
Skill Content
# 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 typ...
Details
- Author
- NVIDIA
- Repository
- NVIDIA/skills
- Created
- 5 months ago
- Last Updated
- today
- Language
- Python
- License
- Apache-2.0
Similar Skills
Semantically similar based on skill content — not just same category
AI & Automation Listed
synthetic-data-desk
design synthetic data generation workflows with seed examples, constraints, diversity targets, contamination controls, review loops, and validation gates.
2 Updated 6 days ago
MadewellRD AI & Automation Listed
data-analyzer
Analyze datasets and extract insights. Use when user needs to understand data patterns, statistics, or trends.
31 Updated today
HIDORAKAI002 AI & Automation Listed
agentic-workflow-designer
Conversational skill that interviews users to design new agentic workflows
37 Updated today
githubnext