Does this skill support local LLMs?

Yes, Hypogenic is compatible with both API-based models like Anthropic and OpenAI, as well as local LLM configurations for privacy and cost management.

How does the literature integration work?

The skill uses GROBID and s2orc-doc2json to process PDF research papers, extracting theoretical foundations that are then converted into testable hypotheses by the LLM.

What is the difference between HypoGeniC and HypoRefine?

HypoGeniC generates hypotheses purely from observational data patterns, whereas HypoRefine synergistically combines insights from existing scientific literature with empirical data.

What dataset format is required?

Hypogenic requires datasets in a standard JSON format similar to HuggingFace datasets, containing text feature columns and a ground truth label column.

Is a Redis server required to use Hypogenic?

Redis is optional but highly recommended; it enables caching of LLM responses which significantly reduces API costs during iterative generation cycles.

Hypogenic Hypothesis Generation

Name: Hypogenic Hypothesis Generation
Author: sanand0

bysanand0

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Data Science & ML

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

Hypogenic is a specialized research framework designed to accelerate scientific discovery by automating the creation and validation of testable hypotheses. It enables researchers to bridge the gap between empirical patterns and existing theoretical knowledge through three primary methods: data-driven generation (HypoGeniC), literature-integrated refinement (HypoRefine), and hybrid union approaches. By iteratively refining hypotheses based on performance against real-world datasets, Hypogenic helps identify non-obvious insights in complex domains such as deception detection, AI-content identification, and mental health analysis, providing a systematic way to conduct empirical research at scale.

Key Features

01Iterative data-driven hypothesis generation and refinement based on validation accuracy

02Automated inference engine to test hypothesis banks against observational datasets

03Adaptive refinement focused on correcting hypotheses for challenging data examples

043 GitHub stars

05Synergistic integration of scientific literature via GROBID-powered PDF processing

06Redis-based caching and parallel processing to optimize API costs and execution speed

Use Cases

01Discovering distinctive linguistic patterns for detecting AI-generated content or deceptive text

02Systematically synthesizing insights from dozens of research papers into testable predictive models

03Conducting exploratory data analysis to generate novel scientific theories from novel empirical datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sanand0/scientific-research hypogenic

For use in Claude.ai and ChatGPT

Download Skill