Generate synthetic tabular datasets from YAML schemas and domain templates.
npx skills add https://github.com/rappdw/synthdata --skill synthdata-generate
Please help me install this Agent Skill. Skill: synthdata-generate Source: https://github.com/rappdw/synthdata/tree/main/skills/synthdata-generate Command: npx skills add https://github.com/rappdw/synthdata --skill synthdata-generate
Generate synthetic datasets from YAML schemas to accelerate testing, prototyping, and privacy-safe data exploration across multiple domains.
Provide a YAML schema or select a built-in template to generate a synthetic dataset.
High-intent search queries and answers about installing and using this skill.
To generate synthetic tabular data from a YAML schema, you define your table structures and fields in YAML, then the engine uses Faker-backed fields and distributions like normal or poisson to produce the dataset. You can also select built-in domain templates for quick generation.
Yes, generating fake data with foreign-key integrity is supported. The YAML schema engine maintains relational links across multiple generated tables, ensuring behavioral profiles and temporal event generation remain consistent for testing or data science pipelines.
Supported output formats for synthetic datasets include xlsx, csv, json, sql, and parquet. This allows direct integration of generated data into analytics pipelines, databases, or applications without requiring external conversion tools.
When generating synthetic data, you can apply normal, lognormal, zipf, and poisson distributions. These options allow you to model realistic numerical patterns and behavioral profiles directly within your YAML schema definitions.
Yes, this synthetic data generator includes built-in domain templates for HR, ecommerce, SaaS, healthcare, finance, security, logs, IoT, CRM, and surveys. These templates accelerate prototyping and privacy-preserving demos across various use cases.
Yes, pandas and numpy are required dependencies to generate synthetic datasets, along with pyyaml for schema parsing and faker for generating realistic mock values. These libraries handle data manipulation, statistical distributions, and fake data generation.
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