hypogenic-hypothesis-generation

Name: hypogenic-hypothesis-generation
Availability: InStock
Author: jaechang-hits

Generate and test scientific hypotheses on labeled tabular datasets using LLMs.

298|27|Updated Feb 18, 2026

Search Tags:#llm #machine learning #data analysis #hypothesis generation #iterative refinement #scientific research

Ready to run

npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill hypogenic-hypothesis-generation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hypogenic-hypothesis-generation
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/scientific-computing/hypogenic-hypothesis-generation
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill hypogenic-hypothesis-generation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hypogenic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the generation and testing of scientific hypotheses from tabular data, accelerating research by leveraging LLMs.

Core Features & Use Cases

LLM-Driven Hypothesis Generation: Creates testable hypotheses directly from your data.
Iterative Refinement: Improves hypotheses based on validation performance.
Multiple Methods: Supports data-driven (HypoGeniC), literature-integrated (HypoRefine), and combined (Union) approaches.
Use Case: A researcher has a dataset of patient outcomes and wants to discover potential causal relationships. This Skill can automatically generate and rank hypotheses about factors influencing outcomes, saving significant manual ideation time.

Quick Start

Use the hypogenic-hypothesis-generation skill to generate 20 data-driven hypotheses from the provided config file and save them to output/hypotheses.json.

Frequently Asked Questions about hypogenic-hypothesis-generation

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

FAQPage Schema

How do I automate scientific hypothesis generation from tabular data using LLMs?▼

Automating scientific hypothesis generation from tabular data requires using LLMs to formulate and iteratively refine testable hypotheses based on labeled datasets. The process validates performance and ranks discovered relationships automatically.

What is iterative refinement in LLM-driven hypothesis testing?▼

Iterative refinement in LLM-driven hypothesis testing is the process of improving generated hypotheses based on validation performance. It continuously evaluates data-driven results to enhance the accuracy of scientific assumptions over multiple cycles.

Do I need a specific Python package for LLM hypothesis generation?▼

Yes, LLM hypothesis generation requires the specific 'hypogenic' Python package and active LLM API access. These dependencies are necessary to automate hypothesis formulation, multi-hypothesis inference, and data validation.

Can I integrate existing literature with data-driven hypothesis generation?▼

Yes, you can integrate existing literature with data-driven hypothesis generation using the combined or literature-integrated approaches. These methods leverage domain knowledge alongside labeled data to formulate and validate testable hypotheses.

What are the limitations of using LLMs to generate scientific hypotheses?▼

Limitations of using LLMs for scientific hypothesis generation include the strict requirement for labeled tabular datasets and active LLM API access. The automated process may also require manual validation to confirm causal relationships.