What is the Hypogenic Claude Code skill?

It is a specialized capability for Claude that automates the generation and testing of scientific hypotheses using large language models, datasets, and literature.

Does Hypogenic support local LLMs?

Yes, it supports both API-based models (like Anthropic and OpenAI) and local LLMs for hypothesis generation and inference.

What domains benefit most from this skill?

It is ideal for empirical research tasks like deception detection, AI content identification, mental health indicators, and predictive modeling.

How does Hypogenic combine literature with data?

Through the HypoRefine method, it extracts theoretical insights from research papers (PDFs) and combines them with empirical patterns found in observational data.

Is Redis required for using Hypogenic?

Redis is optional but highly recommended for caching LLM responses, which significantly reduces API costs during iterative refinement cycles.

Hypogenic: Automated Scientific Hypothesis Generation

Name: Hypogenic: Automated Scientific Hypothesis Generation
Author: Microck

byMicrock

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Ciencia de Datos y ML

Generates, refines, and tests scientific hypotheses from datasets and literature using large language models.

Hypogenic is a powerful research framework that automates the scientific discovery process by generating testable hypotheses from observational data and existing literature. It employs advanced strategies like HypoRefine to synergistically combine empirical patterns with theoretical foundations, enabling researchers to accelerate pattern discovery in domains such as deception detection, AI-generated content identification, and mental health analysis. This skill streamlines the iterative cycle of hypothesis formulation, testing, and refinement, significantly improving predictive accuracy and discovery diversity compared to standard baselines.

Características Principales

0181 GitHub stars

02Synergistic literature integration through PDF processing and GROBID

03Parallel processing and Redis caching for cost-efficient experimentation

04Iterative hypothesis refinement based on empirical performance validation

05Automated data-driven hypothesis generation from observational datasets

06Multi-hypothesis inference for robust predictive modeling and research discovery

Casos de Uso

01Automating literature reviews to form grounded empirical hypotheses for research

02Systematically testing competing scientific theories against large datasets

03Discovering novel indicators for AI-generated content or deception detection

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills hypogenic

For use in Claude.ai and ChatGPT

Download Skill