What is Hypogenic used for?

Hypogenic is used to automate the generation, refinement, and testing of scientific hypotheses using large language models and empirical datasets to accelerate research discovery.

How does it improve research results?

Studies show Hypogenic provides up to a 15% improvement over literature-only approaches while maintaining 80-84% hypothesis diversity, helping researchers find non-redundant insights.

Can I use my own research papers with this skill?

Yes, the HypoRefine method allows you to process PDF literature via GROBID to integrate existing research insights into the automated hypothesis generation pipeline.

What dataset formats are required?

Hypogenic utilizes the HuggingFace datasets format (JSON) with specific keys for text features and ground truth labels to ensure consistency during the inference phase.

Does Hypogenic support local LLMs?

Yes, Hypogenic is modular and compatible with both API-based models like Claude and OpenAI, as well as locally hosted large language models.

Hypogenic Scientific Discovery

Name: Hypogenic Scientific Discovery
Author: Microck

byMicrock

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Data Science & ML

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

About

Hypogenic is a powerful research framework that accelerates scientific discovery by automating the ideation and validation of hypotheses. It bridges the gap between empirical data and existing literature through three distinct methodologies: HypoGeniC for data-driven discovery, HypoRefine for integrated theory-data synthesis, and Union methods for comprehensive coverage. Whether you are analyzing deception detection, mental health indicators, or AI-generated content, this skill helps researchers systematically explore patterns, refine testable claims, and significantly improve predictive accuracy over standard baselines.

Key Features

Seamless integration of research literature via PDF processing and GROBID
Support for both cloud-based (Anthropic/OpenAI) and local LLMs
Adaptive refinement focusing on challenging or anomalous data examples
Performance optimization through Redis caching and parallel processing
81 GitHub stars
Automated data-driven hypothesis generation with iterative refinement

Use Cases

Synthesizing existing research papers with empirical observations for theory-grounded discovery
Identifying linguistic patterns in novel datasets like AI-generated content or deception detection
Systematically testing and ranking multiple competing hypotheses against large observational datasets

About

Key Features

Seamless integration of research literature via PDF processing and GROBID
Support for both cloud-based (Anthropic/OpenAI) and local LLMs
Adaptive refinement focusing on challenging or anomalous data examples
Performance optimization through Redis caching and parallel processing
81 GitHub stars
Automated data-driven hypothesis generation with iterative refinement

Use Cases

Synthesizing existing research papers with empirical observations for theory-grounded discovery
Identifying linguistic patterns in novel datasets like AI-generated content or deception detection
Systematically testing and ranking multiple competing hypotheses against large observational datasets