Does this skill support local LLMs?

Yes, Hypogenic is compatible with both API-based models (like OpenAI and Anthropic) and locally hosted LLM configurations.

What kind of dataset format is required?

It requires datasets in HuggingFace-style JSON formats containing text features and corresponding labels for training, validation, and testing.

How does HypoRefine differ from the standard HypoGeniC method?

HypoGeniC is a data-driven approach that generates hypotheses from observations, while HypoRefine integrates external research literature with data to create theory-grounded hypotheses.

How does Hypogenic manage API costs during large experiments?

The framework utilizes Redis caching for LLM responses and supports parallel processing to minimize redundant calls and optimize resource usage.

What is the primary purpose of the Hypogenic skill?

Hypogenic is designed to automate the process of generating, testing, and refining scientific hypotheses by combining empirical data with LLM-driven insights.

Hypogenic Scientific Hypothesis Framework

Name: Hypogenic Scientific Hypothesis Framework
Author: Activer007

byActiver007

0•

Data Science & ML

Automates the generation and systematic testing of scientific hypotheses using large language models and empirical datasets.

Hypogenic is a specialized framework designed to accelerate scientific discovery by automating the process of hypothesis ideation, refinement, and validation. By leveraging Large Language Models, it offers three distinct workflows: purely data-driven pattern recognition (HypoGeniC), theory-grounded literature integration (HypoRefine), and mechanistic union methods that combine existing research with new empirical insights. This skill is particularly valuable for researchers working in complex domains like deception detection, AI content identification, and predictive modeling where systematic exploration of multiple competing hypotheses is required to achieve high-accuracy results.

Key Features

01Seamless research literature integration via PDF processing and GROBID

02Customizable prompt templates for domain-specific label extraction

03Cost-effective experimentation with Redis caching and parallel processing

04Iterative refinement loops that optimize hypotheses based on performance

050 GitHub stars

06Automated hypothesis generation from raw observational data

Use Cases

01Accelerating discovery in mental health analysis and empirical social science

02Identifying linguistic markers for AI-generated content detection

03Testing theoretical behavioral models against large-scale observational datasets

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