What is the Hypogenic skill used for?

It is used to systematically generate and test scientific hypotheses by analyzing tabular datasets and integrating research literature using LLMs.

What dataset formats does Hypogenic support?

It supports JSON-based datasets following the HuggingFace format, typically requiring structured train, validation, and test splits with feature keys and labels.

How does Hypogenic reduce API costs?

It implements an optional Redis-based caching system that stores LLM responses, preventing redundant API calls during the iterative refinement and testing phases.

Does it support local LLMs?

Yes, Hypogenic is compatible with both API-based models (like OpenAI and Anthropic) and locally hosted LLMs for private data processing.

Can Hypogenic work without existing research papers?

Yes, the HypoGeniC method allows for purely data-driven hypothesis generation based on observational patterns in your dataset without requiring external literature.

Hypogenic Scientific Hypothesis Discovery

Name: Hypogenic Scientific Hypothesis Discovery
Author: Sologa

bySologa

Data Science & ML

Automates scientific hypothesis generation and testing from tabular datasets using LLMs and literature integration.

About

Hypogenic is a specialized research framework designed to accelerate scientific discovery by systematically generating, refining, and testing hypotheses against empirical data. By bridging the gap between theoretical research and observational patterns, it allows researchers to integrate literature insights with data-driven analysis. This skill is particularly valuable for complex empirical tasks such as deception detection, AI-generated content identification, and mental health indicator analysis, where traditional manual hypothesis formulation is time-consuming and prone to bias.

Key Features

Redis-backed caching for cost-efficient large-scale experimentation
Support for multi-hypothesis inference and adaptive refinement
Automated LLM-driven hypothesis generation from observational data
Synergistic literature integration via PDF processing and GROBID
Iterative refinement loops to improve hypothesis accuracy and diversity
0 GitHub stars

Use Cases

Validating and extending established theoretical frameworks with new data
Identifying indicators for complex behaviors like deception or mental health status
Discovering patterns in novel empirical datasets without existing literature

About

Key Features

Redis-backed caching for cost-efficient large-scale experimentation
Support for multi-hypothesis inference and adaptive refinement
Automated LLM-driven hypothesis generation from observational data
Synergistic literature integration via PDF processing and GROBID
Iterative refinement loops to improve hypothesis accuracy and diversity
0 GitHub stars

Use Cases

Validating and extending established theoretical frameworks with new data
Identifying indicators for complex behaviors like deception or mental health status
Discovering patterns in novel empirical datasets without existing literature