experiment-data-builder

Convert experiment-plan specifications into Hydra-backed dataset generators and loaders.

Updated Mar 27, 2026

Search Tags:#validation #plan #reproducibility #dataset #data-generation #hydra-config

Ready to run

npx skills add https://github.com/EmaRimoldi/Claude-scholar-extended --skill experiment-data-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-data-builder
Source: https://github.com/EmaRimoldi/Claude-scholar-extended/tree/main/skills/experiment-data-builder
Command: npx skills add https://github.com/EmaRimoldi/Claude-scholar-extended --skill experiment-data-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Translates experiment-plan specifications into ready-to-use data generators and loaders that register with the Factory/Registry from project-scaffold.

Core Features & Use Cases

Translate the Datasets & Splits section from experiment-plan.md into Hydra-ready dataset definitions.
Generate @register_dataset classes, Hydra YAML configs, and validation scaffolds, enabling reproducible, end-to-end data pipelines.
Use case: bootstrap synthetic and real-world dataset loaders for ML experiments and ablations.

Quick Start

Provide an experiment plan and run the data builder to generate dataset classes and Hydra configs.

Frequently Asked Questions about experiment-data-builder

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

FAQPage Schema

How do I generate data loaders from an experiment plan specification?▼

Generating data loaders from an experiment plan involves converting plan specifications into executable data generators and loaders. This process outputs @register_dataset classes and Hydra YAML configs for your pipeline.

How do I create Hydra configs for reproducible ML datasets?▼

Creating Hydra configs for reproducible ML datasets is done by translating experiment-plan specifications into Hydra-ready dataset definitions. This ensures generated dataset classes and validation scaffolds support end-to-end reproducible pipelines.

Can I bootstrap synthetic dataset loaders for ablation studies?▼

Yes, you can bootstrap synthetic dataset loaders for ablation studies by using a data builder to translate experiment plans. It generates the necessary dataset classes and validation scaffolds to support reproducible ML experiments.

Does this data generation approach work with existing Factory or Registry patterns?▼

Yes, the data generation approach works with Factory or Registry patterns by generating @register_dataset classes. These classes register automatically with the Factory or Registry from your project-scaffold to ensure seamless integration.

What is needed to start building data generators from plan specs?▼

To start building data generators from plan specs, you need an experiment-plan specification detailing Datasets and Splits. Running the data builder on this plan generates the required dataset classes, Hydra configs, and validation scaffolds.