What is the primary purpose of the Hypogenic skill?

Hypogenic is designed to automate the process of generating, refining, and testing scientific hypotheses using large language models and observational data.

What kind of datasets work best with this skill?

It works best with structured JSON datasets containing text features and ground truth labels, such as those used in classification or predictive modeling tasks.

Does Hypogenic require existing research papers?

No, it is flexible. While the HypoRefine method uses literature, the HypoGeniC method is purely data-driven and can generate hypotheses solely from your datasets.

How does it improve research accuracy?

By iteratively refining hypotheses against validation data, it has shown up to an 8.97% improvement over standard few-shot baselines in research discovery tasks.

Can I use local models instead of OpenAI or Anthropic APIs?

Yes, Hypogenic supports both API-based models and local LLM deployments for hypothesis generation and inference.

Hypogenic Research Assistant

Name: Hypogenic Research Assistant
Author: Microck

byMicrock

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Ciencia de Datos y ML

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

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Hypogenic is a specialized research framework designed to accelerate scientific discovery through automated hypothesis ideation and rigorous testing. It offers three distinct methodologies: HypoGeniC for purely data-driven discovery, HypoRefine for integrating existing literature with empirical patterns, and Union methods for comprehensive coverage. This skill is particularly valuable for researchers working in empirical domains such as deception detection, AI-generated content identification, and mental health analysis, providing up to 15% performance improvements over traditional literature-only approaches.

Características Principales

Iterative data-driven hypothesis generation and refinement
Literature integration via GROBID-powered PDF processing
81 GitHub stars
Highly customizable prompt templates for domain-specific tasks
Support for multi-hypothesis inference and parallel processing
Redis-backed caching to minimize API costs during discovery

Casos de Uso

Bridging the gap between theoretical research papers and empirical data patterns
Systematically testing competing hypotheses against observational datasets
Identifying linguistic markers for AI-generated content detection

Acerca de

Características Principales

Iterative data-driven hypothesis generation and refinement
Literature integration via GROBID-powered PDF processing
81 GitHub stars
Highly customizable prompt templates for domain-specific tasks
Support for multi-hypothesis inference and parallel processing
Redis-backed caching to minimize API costs during discovery

Casos de Uso

Bridging the gap between theoretical research papers and empirical data patterns
Systematically testing competing hypotheses against observational datasets
Identifying linguistic markers for AI-generated content detection