Does this skill require external tools for literature processing?

Yes, for literature-integrated workflows, Hypogenic uses GROBID and s2orc-doc2json to process and structure PDF research papers.

Is a Redis server mandatory?

Redis is optional but highly recommended; it provides a caching layer for LLM responses that significantly reduces API costs during the iterative generation phases.

What is the difference between HypoGeniC and HypoRefine?

HypoGeniC generates hypotheses purely from observational data patterns, whereas HypoRefine synergistically combines insights extracted from research literature with empirical data.

How does the iterative refinement process work?

The system generates initial hypotheses, tests them against a validation set, and then replaces poorly performing hypotheses with new ones generated from the most challenging data examples.

Can I use Hypogenic with local LLMs?

Yes, the framework is designed to be flexible and supports both API-based models (like Anthropic and OpenAI) and locally hosted models.

Hypogenic

Name: Hypogenic
Author: Activer007

byActiver007

0•

Data Science & ML

Automates the generation and testing of scientific hypotheses by integrating empirical data with existing literature insights.

Hypogenic is a sophisticated research framework designed to accelerate scientific discovery by automating the hypothesis lifecycle through Large Language Models. It enables researchers to generate testable hypotheses using three distinct methodologies: data-driven discovery (HypoGeniC), synergistic literature-data integration (HypoRefine), and mechanistic union methods. By combining observational patterns with theoretical foundations extracted from research papers via GROBID, Hypogenic facilitates rigorous empirical analysis in complex domains such as deception detection, AI content identification, and mental health research, offering significant performance improvements over traditional few-shot baselines.

Key Features

010 GitHub stars

02Automated data-driven hypothesis generation with iterative performance-based refinement

03Agentic integration of research literature via structured PDF processing and GROBID

04Systematic hypothesis inference and validation against large-scale observational datasets

05Redis-based caching to minimize API costs and parallel processing for scale

06Modular configuration supporting both cloud-based (Anthropic/OpenAI) and local LLMs

Use Cases

01Accelerating exploratory research and pattern discovery in novel datasets without established literature

02Enhancing predictive model accuracy for complex classification tasks like AI-generated content detection

03Bridging the gap between theoretical research papers and empirical data validation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills hypogenic

For use in Claude.ai and ChatGPT

Download Skill