ml-hypogenic

Name: ml-hypogenic
Availability: InStock
Author: biomaps-infra

Automates LLM-driven hypothesis generation and testing from tabular datasets.

Updated Mar 13, 2026

Search Tags:#llm #machine learning #data analysis #research automation #hypothesis generation #scientific discovery

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill ml-hypogenic

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-hypogenic
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/ml-hypogenic
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill ml-hypogenic

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 from tabular datasets, accelerating the discovery process by leveraging LLMs and literature insights.

Core Features & Use Cases

Automated Hypothesis Generation: Create testable hypotheses directly from your data or by integrating existing research.
Data-Driven Exploration: Systematically explore patterns and generate novel research questions.
Literature Integration: Combine theoretical knowledge with empirical evidence for more robust hypotheses.
Use Case: Use this Skill to automatically generate and test hypotheses about factors influencing customer churn based on user behavior data and relevant academic research.

Quick Start

Use the ml-hypogenic skill to generate 20 hypotheses from the provided configuration file.

Frequently Asked Questions about ml-hypogenic

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

FAQPage Schema

How do I automate hypothesis generation from tabular datasets?▼

Automated hypothesis generation from tabular datasets is achieved by using LLMs to explore empirical data patterns and generate testable research questions. This process combines literature insights with data-driven hypothesis testing methods.

How do I integrate existing research literature with data-driven hypothesis testing?▼

Integrating research literature with data-driven hypothesis testing combines theoretical knowledge with empirical evidence to produce robust hypotheses. Literature and data integration methods refine generated hypotheses against existing academic findings.

Do I need Redis and Python to run LLM-driven hypothesis generation?▼

Yes, you need Python and pip installed to run LLM-driven hypothesis generation. Redis is potentially required for caching to optimize the systematic exploration of empirical data patterns during the testing process.

Can I combine multiple sets of generated hypotheses from different methods?▼

Yes, you can combine multiple sets of generated hypotheses using Union methods. This merges outputs from data-driven generation and literature-data integration approaches into a single unified set of testable hypotheses.

What is the best way to explore empirical data patterns for scientific discovery?▼

Exploring empirical data patterns for scientific discovery is best done by automating LLM-driven hypothesis generation and testing. This systematically combines literature insights with data-driven exploration to accelerate the discovery process.