Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combin…
---
name: hypogenic
description: Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---
# Hypogenic
## Overview
Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses).
## Quick Start
Get started with Hypogenic in minutes:
```bash
# Install the package
uv pip install hypogenic
# Clone example datasets
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data
# Run basic hypothesis generation… load the full skill through Skill Me