How does Hypogenic handle academic literature?

It uses a literature-to-hypothesis pipeline involving PDF processing via GROBID and s2orc-doc2json to extract theory-grounded insights from research papers.

Is a Redis server required to run this skill?

Redis is optional but highly recommended; it enables caching of LLM responses which significantly reduces API costs and speeds up iterative experiments.

Can I use Hypogenic with my own local models?

Yes, Hypogenic supports both API-based models (like OpenAI and Anthropic) and local LLMs to provide flexibility for different research environments.

What is the difference between HypoGeniC and HypoRefine?

HypoGeniC generates hypotheses solely from observational data, whereas HypoRefine synergistically combines literature-based theoretical foundations with empirical data patterns.

Hypogenic Scientific Hypothesis Engine

Name: Hypogenic Scientific Hypothesis Engine
Author: ricable

byricable

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データサイエンスとML

Generates and tests scientific hypotheses automatically by combining observational data with literature insights using LLMs.

Hypogenic is a specialized scientific discovery framework that automates the generation, refinement, and testing of hypotheses. It leverages large language models to systematically explore patterns in observational datasets and integrate theoretical insights from academic literature. By providing three distinct approaches—data-driven (HypoGeniC), literature-integrated (HypoRefine), and mechanistic combinations (Union)—it allows researchers to accelerate the scientific method. This skill is particularly effective for empirical research tasks such as deception detection, AI content identification, and mental health analysis, offering performance optimizations like Redis caching and parallel processing to ensure cost-effective and scalable discovery.

主な機能

01Automated data-driven hypothesis generation and iterative refinement

02Seamless literature integration via PDF processing and GROBID

03Redis caching to reduce API costs during large-scale experimentation

041 GitHub stars

05Modular architecture for custom label extraction and prompt templates

06Support for both API-based and local LLM backends

ユースケース

01Validating theoretical deception detection frameworks against new datasets

02Identifying linguistic markers for AI-generated content detection

03Accelerating exploratory research in mental health and social sciences

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ricable/ultimate-ai-agent hypogenic

For use in Claude.ai and ChatGPT

Download Skill