What is the difference between HypoGeniC and HypoRefine?

HypoGeniC generates hypotheses purely from observational data patterns, whereas HypoRefine integrates insights from existing research literature (PDFs) with empirical data for a grounded discovery process.

How does it handle research papers and PDFs?

The skill uses the GROBID service and s2orc-doc2json to parse and convert PDF research papers into structured text that the LLM can analyze for hypothesis extraction.

Do I need a Redis server to use Hypogenic?

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

What dataset format is required?

Hypogenic requires datasets in the HuggingFace JSON format with consistent keys for text features and ground-truth labels across training, validation, and testing files.

Can I use local LLMs with this skill?

Yes, Hypogenic supports both API-based providers like Anthropic and OpenAI, as well as local LLM deployments for enhanced data privacy and reduced operational costs.

Hypogenic Scientific Hypothesis Engine

Name: Hypogenic Scientific Hypothesis Engine
Author: x-cmd

byx-cmd

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Data Science & ML

Accelerates scientific discovery by automatically generating, testing, and refining hypotheses from datasets and research literature using LLMs.

Hypogenic is a specialized framework for automating the scientific method's ideation phase. It enables researchers to generate testable hypotheses directly from observational data (HypoGeniC) or by synergistically combining existing literature with empirical patterns (HypoRefine). Designed for complex empirical research tasks—such as deception detection, AI content identification, and mental health analysis—this skill provides iterative refinement loops, parallel processing, and performance optimization via Redis caching. It transforms raw datasets into structured, validated insights, significantly outperforming traditional few-shot baselines and manual literature reviews.

Key Features

01Synergistic literature integration via PDF processing and GROBID synchronization

02Modular configuration for custom prompt engineering and domain-specific label extraction

038 GitHub stars

04Automated data-driven hypothesis generation with iterative refinement cycles

05Performance-optimized execution with Redis caching to reduce LLM API costs

06Multi-hypothesis inference and systematic testing against observational data

Use Cases

01Developing behavioral hypotheses for deception detection within empirical social science datasets

02Identifying linguistic markers to distinguish between human-written and AI-generated content

03Synthesizing theoretical research insights with new clinical data to discover mental health indicators

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add x-cmd/skill hypogenic

For use in Claude.ai and ChatGPT

Download Skill