hypogenic

Generate and test hypotheses on tabular datasets using large language models.

13|3|Updated Jun 10, 2026

Search Tags:#data analysis #large language models #hypothesis generation #tabular data #scientific research

Ready to run

npx skills add https://github.com/tassiovale/claude-code-kit --skill hypogenic-tassiovale

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the process of generating and testing hypotheses on tabular datasets, saving time and effort in scientific research and data analysis.

Core Features & Use Cases

Automated Hypothesis Generation: Generate testable hypotheses from data or literature.
Literature Integration: Combine literature insights with empirical data for hypothesis generation.
Use Case: For a data scientist analyzing customer data, Hypogenic can automatically generate hypotheses about customer behavior patterns, which can then be tested on the dataset.

Quick Start

Generate hypotheses using Hypogenic with the following command:

hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20

Frequently Asked Questions about hypogenic

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

FAQPage Schema

How do I automate hypothesis generation on tabular datasets?▼

You can automate hypothesis generation on tabular datasets by using large language models via the hypogenic package. Execute the process through the command line interface by specifying your configuration YAML files to generate and test empirical hypotheses automatically.

Can I generate research hypotheses using both literature and empirical data?▼

Yes, generating research hypotheses using both literature and empirical data is supported via a combined approach. This method integrates literature insights with your tabular data to produce data-driven, testable hypotheses for scientific research.

What is the best way to generate testable hypotheses for scientific research?▼

The best way to generate testable hypotheses for scientific research is using automated LLM-based tools. This approach supports data-driven, literature-based, and combined methods to systematically generate and validate hypotheses on tabular datasets.

Do I need specific configuration files to run large language model hypothesis testing?▼

Yes, specific configuration files are required to run large language model hypothesis testing. You need a YAML configuration file specifying data and model parameters to execute the generation command successfully on your tabular datasets.

How many hypotheses can I generate from a dataset in a single run?▼

You can generate multiple hypotheses from a dataset in a single run by setting the num_hypotheses parameter in your command. The tool processes your tabular data to output the specified number of testable hypotheses based on your configuration.

Does hypogenic work with any tabular data format for data analysis?▼

Hypogenic works with tabular data formats for data analysis, provided the data is properly configured in the required YAML configuration files. The tool processes the structured data to generate empirical and literature-based hypotheses automatically.