hypogenic

Generate and test hypotheses on tabular datasets using large language models.

Updated May 17, 2026

Search Tags:#data analysis #automated testing #large language models #scientific research #hypthesis generation

Ready to run

npx skills add https://github.com/galeep/plugin-place --skill hypogenic-galeep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hypogenic
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-machine-learning/skills/hypogenic
Command: npx skills add https://github.com/galeep/plugin-place --skill hypogenic-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pypdf, pdfplumber, redis, grobid, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of manually generating and testing hypotheses in scientific research, providing an efficient, automated solution that expedites the discovery process.

Core Features & Use Cases

Automated Hypothesis Generation: Quickly create testable hypotheses from empirical data.
Literature Integration: Combine insights from literature with empirical data for enhanced analysis.
Performance Optimization: Utilize Redis caching and parallel processing for efficient LLM interactions.
Use Case: For a scientist exploring potential relationships in data without an existing theoretical framework, Hypogenic automates hypothesis generation based on the data, speeding up the discovery process.

Quick Start

Generate hypothesis using Hypogenic with the command:

hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20

Frequently Asked Questions about hypogenic

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

FAQPage Schema

How do I automate hypothesis generation from tabular datasets for scientific research?▼

Automated hypothesis generation uses large language models to analyze tabular datasets and create testable hypotheses. This process accelerates scientific discovery by quickly identifying potential empirical relationships without requiring an existing theoretical framework.

Do I need Redis and GROBID to run large language model hypothesis testing?▼

Yes, Redis is required for caching to optimize performance during LLM interactions, and GROBID is needed for literature processing. These dependencies ensure efficient parallel processing and proper document parsing when generating hypotheses.

What is the best way to generate multiple testable hypotheses from empirical data quickly?▼

The best way to generate multiple hypotheses is using automated LLM-driven testing on tabular datasets. By configuring the generation command, you can specify the exact number of hypotheses to produce, such as generating twenty hypotheses at once.

Can I use automated hypothesis testing if I only have raw tabular datasets without a theoretical framework?▼

Yes, automated hypothesis generation is specifically designed for scientists exploring potential relationships in data without an existing theoretical framework. It processes raw empirical data to formulate and validate testable hypotheses automatically.