data-designer

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Use 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

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100
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100
Frontmatter 20%
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Issue Health 10%
50
License 10%
100
Description 5%
100

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

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