hypogenic

Automate scientific hypothesis generation by using LLMs to systematically create and empirically test hypotheses against tabular datasets. This skill identifies data patterns to generate 10-20+ testable hypotheses in minutes, bypassing manual formulation bottlenecks.

Literature-informed hypothesis refinement uses GROBID to process research paper PDFs into structured text. The skill synergistically combines these extracted theoretical insights with empirical data patterns to iteratively test and refine competing hypotheses.

Yes, you need Redis for caching and GROBID for processing research literature PDFs to run automated hypothesis testing. You configure the testing process for your data directory using a config.yaml file to manage these dependencies.

Yes, you can use LLMs to identify linguistic patterns in deception detection or mental health datasets. The skill supports exploratory research by systematically benchmarking competing theories against large datasets to detect mental health indicators.

Manual hypothesis formulation relies on human intuition, whereas systematic benchmarking of competing theories uses LLMs to iteratively test hypotheses against empirical datasets. This automated approach generates and evaluates 10-20+ theories rapidly to ensure robust scientific discovery.

You should not use LLMs for automated empirical testing of hypotheses if your research lacks tabular datasets for benchmarking or if you cannot integrate Redis and GROBID for caching and PDF literature processing.