Generate and evaluate testable research hypotheses from tabular datasets using LLM pipelines.
npx skills add https://github.com/shushuzn/Rairos --skill hypogenic-shushuzn
Please help me install this Agent Skill. Skill: hypogenic Source: https://github.com/shushuzn/Rairos/tree/main/skills/hypogenic Command: npx skills add https://github.com/shushuzn/Rairos --skill hypogenic-shushuzn
Hypogenic removes the trial-and-error burden of coming up with testable research hypotheses and evaluating them systematically, so you can move from raw observations to evidence-backed claims faster.
Run automated hypothesis generation from your dataset and configuration by instructing the AI with the command: hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20
High-intent search queries and answers about installing and using this skill.
To generate testable research hypotheses from tabular datasets, you use LLM-driven pipelines that iteratively refine patterns from your observational data. This automated hypothesis generation produces a bank of testable claims for subsequent inference and scoring.
Yes, you can integrate literature PDFs with data to generate theory-grounded hypotheses using literature-integrated strategies. This method preprocesses research papers with GROBID to combine extracted insights with empirical patterns.
The best way to automate hypothesis generation for predictive modeling is defining a YAML config with dataset paths and prompt templates. This configures the LLM inference pipeline to generate and evaluate a hypothesis bank for your specific modeling task.
Yes, you need a YAML configuration to run automated hypothesis testing because it defines your dataset paths and prompt templates. The pipeline requires this configuration to execute LLM inference and output your hypothesis bank.
Hypothesis generation can handle deception detection and mental health indicator exploration by analyzing linguistic or behavioral patterns in observational datasets. It generates hypotheses about these patterns to measure which best predict the labels.
To reduce redundancy when merging literature-derived and data-derived hypotheses, you apply union hypothesis strategies. This method merges both hypothesis banks while actively reducing overlaps to maintain diverse coverage.
dakesanGenerate testable scientific hypotheses with mechanistic explanations and experimental predictions.
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