Generate valid Tablefaker YAML configurations for synthetic datasets.
npx skills add https://github.com/necatiarslan/table-faker --skill tablefaker
Please help me install this Agent Skill. Skill: tablefaker Source: https://github.com/necatiarslan/table-faker/tree/main/skills/tablefaker Command: npx skills add https://github.com/necatiarslan/table-faker --skill tablefaker
This Skill helps agents generate valid YAML configurations for Tablefaker to create synthetic datasets with multiple tables, relationships, and faker-driven data.
Create a version-1 YAML configuration that defines two tables with appropriate columns and relationships.
High-intent search queries and answers about installing and using this skill.
To generate synthetic data with foreign keys, define a version-1 YAML configuration where parent tables are listed before child tables to enforce runtime ordering. You must specify table names, columns, and the foreign key relationships linking them.
A valid Tablefaker YAML configuration requires a version-1 declaration, specific table_name fields, and defined columns. It enforces parent-first table ordering for foreign keys and requires explicit data expressions or inference rules.
Yes, you can set deterministic seeds and locale settings within the YAML configuration to generate reproducible synthetic datasets. This ensures faker-driven data expressions produce identical results across multiple runs.
You configure export formats by using the optional export_file_name setting in your YAML schema. When exporting to parquet, define the parquet_type setting alongside data expressions to ensure proper file structure.
Use the data: auto inference rule when you want the system to automatically determine faker-driven data types based on column names. Explicit data expressions are better when you need strict control over specific synthetic value generation.
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