Generate synthetic datasets with statistical samplers and LLM column generation.
npx skills add https://github.com/bacoco/Data-designer-skill --skill data-designer
Please help me install this Agent Skill. Skill: data-designer Source: https://github.com/bacoco/Data-designer-skill/tree/main Command: npx skills add https://github.com/bacoco/Data-designer-skill --skill data-designer
This Skill automates the creation of synthetic datasets, combining statistical methods with LLM capabilities to generate realistic data for various purposes without requiring external API keys.
Use the data-designer skill to generate 50 product reviews with ratings from 1 to 5.
High-intent search queries and answers about installing and using this skill.
You can generate synthetic test data by defining a schema with statistical samplers for categories and ratings, using LLM column generation to produce realistic product reviews, and exporting the dataset as CSV or Parquet.
Creating mock datasets without external API keys is possible by combining statistical samplers for distributions and categories with native LLM capabilities to generate realistic text and structured data based on a defined schema.
Yes, schema-driven generation allows you to define dataset structure, column types, and dependencies in a schema file, which the system uses to generate synthetic datasets that match your exact requirements.
Synthetic data generation supports CSV, JSON, JSONL, and Parquet output formats, allowing you to export generated test data and training datasets directly into the format your downstream applications require.
You can generate realistic customer personas and ratings by using built-in statistical samplers for categories and distributions, alongside LLM column generation to create rich, contextual text data for training.
LLM data generation for large datasets depends on combining statistical samplers with LLM capabilities, which may require careful schema design to manage dependencies and ensure realistic data generation at scale.
surus-latConfigure and validate benchmark datasets by mapping CSV or JSONL files to required schemas.
Sir-chawakornClean, transform, and reshape CSV, Parquet, and DataFrame tabular data.
NVIDIA-NeMoEnforce Python and shell script style rules with ruff, isort, and pre-commit.
Ethical-AI-SyndicatePlan synthetic data generation strategies for AI training and testing.
