hypogenic

Generate and test scientific hypotheses from empirical data and literature.

8|Updated Nov 19, 2025

Search Tags:#data analysis #literature review #hypothesis generation #research tool #scientific discovery #llm application

Ready to run

npx skills add https://github.com/sanand0/scientific-research --skill hypogenic-sanand0

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hypogenic
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/hypogenic
Command: npx skills add https://github.com/sanand0/scientific-research --skill hypogenic-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the generation and testing of scientific hypotheses, significantly accelerating the research discovery process.

Core Features & Use Cases

Automated Hypothesis Generation: Create testable hypotheses directly from observational data or by integrating existing literature.
Hypothesis Testing: Systematically evaluate multiple hypotheses against empirical data.
Use Case: A researcher studying AI-generated content can use this Skill to automatically generate hypotheses about linguistic patterns distinguishing human from AI text, and then test these hypotheses on a dataset of articles.

Quick Start

Use the hypogenic skill to generate 20 hypotheses from the data configuration file located at ./data/your_task/config.yaml.

Frequently Asked Questions about hypogenic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate scientific hypotheses from empirical data?▼

Automated hypothesis generation uses large language models to process empirical data and systematically formulate testable scientific hypotheses derived directly from your observational datasets.

What is the best way to use literature review for hypothesis generation?▼

Literature-based hypothesis generation integrates existing scientific publications to refine and formulate new testable hypotheses, combining text analysis with published research context.

Can I use large language models for automated scientific discovery?▼

Yes, large language models facilitate automated scientific discovery by evaluating empirical data and scientific literature to generate, refine, and test hypotheses across domains like deception detection and mental health.

Does this automated hypothesis testing work with custom research datasets?▼

Automated hypothesis testing works with custom research datasets by evaluating multiple generated hypotheses against your specific empirical data configured through a data configuration file.

What are the limitations of using LLM applications for research tool hypothesis generation?▼

Limitations of LLM applications for research tool hypothesis generation include the need for structured empirical data and the requirement to systematically test outputs, as generated hypotheses must be empirically validated against real datasets.