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hypogenic

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Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to combine literature insights with data-driven hypothesis evaluation.

scientific-skills/Protocol Design/hypogenic/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

- **Exploratory analysis on a new dataset** where you want the model to propose multiple *testable* hypotheses from observed patterns (e.g., AI-generated text detection).
- **Benchmarking competing explanations** by generating a hypothesis bank and evaluating them consistently on validation/test splits.
- **Literature-informed research** where you want to extract claims from papers and refine them against real data (e.g., deception cues in reviews).
- **High-coverage hypothesis discovery** when you need both theory-driven and data-driven hypotheses, then merge/deduplicate them (Union workflows).
- **Hypothesis-driven classification/regression pipelines** for domains like fraud detection, content moderation, mental health indicators, or other empirical studies using tabular/JSON datasets.

## Key Features

- **Automated hypothesis generation (HypoGeniC)**: iteratively proposes and improves hypotheses using dataset feedback.
- **Literature + data integration (HypoRefine)**: extracts literature insights from PDFs and refines hypotheses jointly with empirical signals.
- **Union method**: mechanically merges literature-only hypotheses with HypoGeniC/HypoRefine outputs to maximize coverage and reduce redundancy.
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