Generate and test hypotheses from tabular data using LLMs.
npx skills add https://github.com/fuzzy-dynamics/strings --skill hypogenic-fuzzy-dynamics
Please help me install this Agent Skill. Skill: hypogenic Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/hypogenic Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill hypogenic-fuzzy-dynamics
Hypogenic automates generation and testing of hypotheses from tabular data using large language models, accelerating scientific discovery by combining data-driven insights with literature-informed reasoning.
Install the hypogenic package and run a minimal example to start generating hypotheses from a sample dataset.
High-intent search queries and answers about installing and using this skill.
Automate hypothesis generation by processing tabular data with LLMs to extract data-driven insights and formulate testable theories. The workflow uses configurable dataset templates and prompt templates to ensure experiments remain reproducible across observational datasets.
Yes, literature integration blends theoretical insights with empirical patterns to refine generated hypotheses. Optional literature processing allows you to combine domain-specific text with observational data, producing literature-informed hypotheses for data-driven theory development.
The best way to generate testable hypotheses for deception detection is applying the HypoGeniC, HypoRefine, or Union methods. These end-to-end workflows process observational data to produce data-driven, reproducible experimental results for content analysis and detection tasks.
Yes, configurable dataset templates, prompt templates, and optional literature processing are required to ensure reproducible LLM hypothesis testing. These configurable components standardize the data-driven theory development workflow, guaranteeing consistent and verifiable experimental outputs.
LLM data analysis accelerates scientific discovery by combining data-driven insights with literature-informed reasoning, overcoming manual limitations. It automates hypothesis generation and testing from tabular data, providing reproducible workflows that manual observational analysis cannot efficiently scale.
Limitations of LLM hypothesis generation include reliance on tabular data quality and the need for properly configured dataset templates. It suits domains like deception detection and content analysis but requires structured observational data, optional literature processing, and prompt engineering to maintain reproducibility.
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yf8578Automates scientific hypothesis generation and testing using LLMs and literature analysis.
