Generate and test LLM-driven hypotheses from tabular datasets.
npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill hypogenic-weiwei-mao
Please help me install this Agent Skill. Skill: hypogenic Source: https://github.com/Weiwei-Mao/hydrology-skills/tree/main/hydrology-skills/hypogenic Command: npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill hypogenic-weiwei-mao
This Skill automates the generation and testing of scientific hypotheses from tabular datasets, significantly accelerating the research discovery process.
Use the hypogenic skill to generate 20 hypotheses from the data configuration file located at ./data/your_task/config.yaml.
High-intent search queries and answers about installing and using this skill.
To generate scientific hypotheses from tabular datasets, this Skill uses LLMs to automate data-driven generation and testing. You simply point it to your data configuration file, and it creates testable hypotheses directly from your empirical data patterns.
Yes, you can integrate literature insights with data-driven hypothesis generation. The Skill supports a method that combines theoretical foundations from research papers with empirical findings found in your tabular datasets.
You do not need Redis to run automated hypothesis testing, but it is optionally supported for caching. The required environment is Python and pip, which are necessary to execute the LLM-driven generation and testing workflows.
The best way to systematically explore empirical data patterns is to use an automated method that creates and tests hypotheses directly from your data. This facilitates systematic exploration by combining theoretical foundations with empirical findings.
A limitation when using LLMs for scientific discovery is the dependency on your local environment configuration, specifically requiring Python and pip. Additionally, while optional Redis caching improves performance, complex tabular datasets may require careful configuration to ensure valid empirical testing.
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