What is the primary benefit of using Hypogenic?

Hypogenic automates the discovery and validation of testable hypotheses from raw data and literature, significantly improving research accuracy and providing non-redundant insights that exceed standard few-shot LLM prompts.

Can I integrate my own research papers into the generation process?

Yes, the HypoRefine and Union methods allow you to process PDF research papers using GROBID, enabling the skill to ground new hypotheses in existing theoretical foundations.

Which large language models are compatible with Hypogenic?

The framework is flexible and supports both API-based models (such as OpenAI and Anthropic) as well as local LLM deployments for privacy-sensitive research tasks.

Why is a Redis server recommended for this skill?

A Redis server provides a caching layer for LLM responses. This is crucial for iterative hypothesis generation, as it prevents redundant API calls and significantly reduces operational costs.

Hypogenic: Automated Hypothesis Generation

Name: Hypogenic: Automated Hypothesis Generation
Author: Activer007

byActiver007

Data Science & ML

Automates scientific hypothesis generation and testing by combining observational data with literature insights using large language models.

About

Hypogenic is a powerful research framework designed to accelerate scientific discovery through automated hypothesis ideation and validation. By leveraging large language models, it provides three distinct approaches: data-driven generation (HypoGeniC), literature-integrated refinement (HypoRefine), and mechanistic union methods. This skill is ideal for researchers working on complex empirical tasks like deception detection, AI-generated content identification, and mental health analysis, offering up to 15% improvement over literature-only baselines and providing systematic, non-redundant insights from large datasets.

Key Features

Template-based prompt engineering with support for custom label extraction
Iterative data-driven hypothesis generation and performance-based refinement
Redis-based caching to minimize LLM API costs and execution time
Multi-hypothesis inference and adaptive selection for complex datasets
Automated literature integration via PDF processing and GROBID support
0 GitHub stars

Use Cases

Generating and testing scientific hypotheses from novel observational datasets
Systematically validating established research theories against new empirical patterns
Accelerating discovery in domains like AI content detection and deception detection

About

Key Features

Template-based prompt engineering with support for custom label extraction
Iterative data-driven hypothesis generation and performance-based refinement
Redis-based caching to minimize LLM API costs and execution time
Multi-hypothesis inference and adaptive selection for complex datasets
Automated literature integration via PDF processing and GROBID support
0 GitHub stars

Use Cases

Generating and testing scientific hypotheses from novel observational datasets
Systematically validating established research theories against new empirical patterns
Accelerating discovery in domains like AI content detection and deception detection