Generate and test hypotheses from datasets using large language models.
npx skills add https://github.com/JosephWoodall/noosphere --skill hypogenic-josephwoodall
Please help me install this Agent Skill. Skill: hypogenic Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/hypogenic Command: npx skills add https://github.com/JosephWoodall/noosphere --skill hypogenic-josephwoodall
Hypogenic automates hypothesis generation and testing using large language models to accelerate scientific discovery, reducing manual, error-prone ideation and interpretation cycles.
Run hypogenic_generation with your task config to generate hypotheses, then use hypogenic_inference to evaluate them.
High-intent search queries and answers about installing and using this skill.
Hypothesis generation uses LLMs to evaluate data-driven propositions against your datasets. It supports data-driven methods and literature-informed refinement to accelerate scientific discovery across domains like deception detection and mental health indicators.
Yes, literature integration combines literature-derived and data-driven hypotheses with iterative refinement. Optional reference processing components allow you to incorporate existing research directly into your hypothesis generation and evaluation pipeline.
You need a Python environment that supports CLI and API access for LLM interaction. The workflow relies on YAML-configurable task structures and prompt templates for observations, generation, inference, and relevance checking without external dependencies.
The best approach uses iterative refinement methods like HypoRefine to combine literature-derived and data-driven hypotheses. Adaptive prompt templates perform relevance checking to improve hypothesis accuracy across research domains.
Automated hypothesis generation supports domains such as deception detection, AI-generated content identification, and mental health indicators. The config-driven prompts and data pipelines allow application across diverse scientific research fields.
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