libs-synthetic-data

Parse DSLs to generate deterministic synthetic datasets in HTML, Markdown, and YAML.

4|Updated Jan 14, 2026

Search Tags:#testing #data generation #synthetic data #DSL parsing #LLM content #data rendering

Ready to run

npx skills add https://github.com/forwardimpact/monorepo --skill libs-synthetic-data

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: libs-synthetic-data
Source: https://github.com/forwardimpact/monorepo/tree/main/.claude/skills/libs-synthetic-data
Command: npx skills add https://github.com/forwardimpact/monorepo --skill libs-synthetic-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a robust framework for generating synthetic data, essential for testing software, creating example content, and building realistic data pipelines without relying on sensitive production information.

Core Features & Use Cases

DSL Parsing: Parse custom Domain Specific Languages (DSLs) to define data structures and relationships.
Deterministic Entity Generation: Create predictable and repeatable datasets for organizations, teams, and individuals.
LLM-Powered Content: Generate natural language prose and framework content using Large Language Models.
Multi-Format Rendering: Output generated data into various formats like HTML, Markdown, and YAML, with built-in validation.
Use Case: Generate a realistic organizational structure with 50 employees, their roles, and project assignments for testing a new HR platform.

Quick Start

Use the libs-synthetic-data skill to parse the 'universe.dsl' file and generate entities.

Frequently Asked Questions about libs-synthetic-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate synthetic data for testing a new platform?▼

To generate synthetic data for testing, you can parse custom DSL files to define data structures and create deterministic, repeatable datasets for organizations, teams, and individuals. This allows you to build realistic test pipelines without exposing sensitive production information.

Can I generate natural language prose using synthetic data generation?▼

Yes, synthetic data generation can produce natural language prose by leveraging Large Language Models. This LLM-powered content generation allows you to create realistic framework content and example text for your testing and development needs.

Does synthetic data generation support outputting to HTML, Markdown, and YAML?▼

Yes, synthetic data generation supports multi-format rendering to output created entities into HTML, Markdown, and YAML. The rendering process includes built-in validation to ensure the generated datasets meet your structural requirements.

What is DSL parsing and how does it work for creating test datasets?▼

DSL parsing involves interpreting custom Domain Specific Languages to define specific data structures and relationships for your test datasets. By parsing a DSL file, you can deterministically create predictable entities like an organizational structure with employees, roles, and project assignments.

Is there a way to ensure deterministic entity creation when generating synthetic data?▼

Yes, you can ensure deterministic entity creation by defining data structures through DSL parsing. This approach generates predictable and repeatable datasets, which is essential for consistent software testing and validating application behavior across multiple development cycles.