What is the difference between HypoGeniC and HypoRefine?

HypoGeniC focuses on purely data-driven discovery from observational datasets, while HypoRefine integrates existing research literature with empirical data for a theory-grounded approach.

Can I use Hypogenic with local LLMs?

Yes, Hypogenic is designed to be model-agnostic and supports both API-based providers (like Anthropic and OpenAI) as well as locally hosted models.

How does Hypogenic process scientific literature?

It utilizes GROBID to process PDF research papers into a structured format, allowing the LLM to extract theoretical insights and convert them into testable hypotheses.

Does this skill require external services like Redis?

A Redis server is optional but highly recommended; it enables caching of LLM responses which significantly reduces API costs and speeds up iterative hypothesis generation.

What kind of performance improvements can I expect?

Evaluations show up to an 8.97% improvement over few-shot baselines and a 15.75% improvement over literature-only approaches in specific empirical research tasks.

Hypogenic Scientific Hypothesis Generator

Name: Hypogenic Scientific Hypothesis Generator
Author: Microck

byMicrock

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Data Science & ML

Automates the generation, refinement, and testing of scientific hypotheses using data-driven insights and literature integration.

Hypogenic is a specialized framework designed to accelerate scientific discovery by automating the hypothesis lifecycle through large language models. It provides researchers with three distinct approaches: HypoGeniC for purely data-driven discovery, HypoRefine for synthesizing research literature with empirical data, and Union methods for comprehensive theoretical coverage. By systematically exploring and testing multiple competing theories, Hypogenic enables rigorous discovery in complex domains like deception detection, AI content analysis, and mental health research, often outperforming traditional few-shot baselines.

Key Features

01Performance optimization with Redis caching and parallel processing

02Automated data-driven hypothesis generation and iterative refinement

03Flexible template-based configuration for domain-specific prompt engineering

04Synergistic literature integration via GROBID PDF processing

05Systematic hypothesis testing and validation against observational data

0681 GitHub stars

Use Cases

01Systematically testing competing hypotheses to improve predictive model accuracy

02Accelerating research discovery in behavioral sciences like deception detection

03Validating and extending existing theoretical frameworks with new empirical data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills hypogenic

For use in Claude.ai and ChatGPT

Download Skill