What is the primary difference between HypoGeniC and HypoRefine?

HypoGeniC is purely data-driven, generating hypotheses from observational patterns, while HypoRefine synergistically integrates insights from academic literature with empirical data.

What data format is required for Hypogenic?

It requires datasets in HuggingFace JSON format with 'text_features' and 'label' keys to facilitate standardized training and validation.

What kind of performance gains does Hypogenic offer?

Research shows up to a 15.75% improvement over literature-only approaches and significantly higher hypothesis diversity compared to standard few-shot prompting.

Can I use this skill with local LLMs?

Yes, Hypogenic supports both cloud-based APIs like Anthropic and OpenAI as well as locally hosted LLMs for processing and inference.

Hypogenic: Scientific Hypothesis Engineering

Name: Hypogenic: Scientific Hypothesis Engineering
Author: BbgnsurfTech

byBbgnsurfTech

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数据科学与机器学习

Automates the generation and testing of scientific hypotheses by combining observational data patterns with academic literature insights.

Hypogenic is a specialized framework designed to accelerate scientific discovery using large language models. It enables researchers to automatically generate, refine, and test hypotheses across three distinct methodologies: data-driven exploration (HypoGeniC), synergistic literature integration (HypoRefine), and mechanistic combinations (Union methods). By leveraging techniques like iterative refinement and parallel processing, Hypogenic helps identify non-redundant insights in complex domains such as AI content detection and deception analysis, significantly outperforming standard few-shot prompting methods and literature-only approaches.

主要功能

01Parallel processing and Redis caching to minimize API costs

02Support for both API-based and local LLM backends

03Iterative data-driven hypothesis generation and refinement

04Literature integration via PDF processing and GROBID extraction

053 GitHub stars

06Adaptive refinement focused on challenging edge cases

使用场景

01Accelerating discovery in mental health and predictive modeling research

02Conducting forensic research for deception detection in text

03Developing novel detection methods for AI-generated content

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bbgnsurftech/claude-skills-collection hypogenic

For use in Claude.ai and ChatGPT

Download Skill