hypogenic

Generate and test hypotheses about empirical data patterns with literature insights.

3|Updated Apr 17, 2026

Search Tags:#empirical testing #large language models #hypothesis generation #literature integration #data-driven research

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill hypogenic-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hypogenic
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/hypogenic
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill hypogenic-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hypogenic, redis, s2orc-doc2json, grobid, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of generating and testing hypotheses about patterns in empirical data, combining literature insights with data-driven approaches to accelerate scientific discovery.

Core Features & Use Cases

Automated Hypothesis Generation: Generate testable hypotheses from data in minutes.
Literature Integration: Extract insights from research papers and combine them with empirical data.
Use Case: For a researcher analyzing social media data, Hypogenic can generate hypotheses about user behavior based on both the data and relevant literature.

Quick Start

Run the Hypogenic skill with the following 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 empirical data and literature?▼

To generate testable hypotheses from data, you configure a YAML task file and run the command line interface. This integrates literature insights extracted via GROBID PDF processing with empirical data patterns to automate scientific discovery.

What is literature integration for data-driven research?▼

Literature integration combines extracted insights from research papers with empirical datasets. This process uses GROBID for PDF processing to parse document structures, enabling large language models to ground generated hypotheses in existing academic findings.

Do I need Redis and GROBID to run hypothesis generation tasks?▼

Yes, Redis and GROBID are required dependencies for hypothesis generation tasks. Redis provides caching to manage computational overhead, while GROBID is required for parsing and extracting structured insights from research PDFs during literature integration.

Can I use large language models to generate testable hypotheses for scientific research?▼

Yes, large language models can generate testable hypotheses for scientific research. By applying data-driven approaches and literature integration, the models analyze empirical data patterns to output multiple structured, testable hypotheses.

What are the limitations of automated hypothesis generation from empirical data?▼

Limitations include dependencies on external infrastructure like Redis caching and GROBID for PDF processing. The approach requires properly formatted empirical data and structured YAML configuration files to successfully generate relevant scientific hypotheses.