Generate and test hypotheses from tabular data using LLMs.
npx skills add https://github.com/m0at/claudemd --skill hypogenic-m0at
Please help me install this Agent Skill. Skill: hypogenic Source: https://github.com/m0at/claudemd/tree/main/skills/hypogenic Command: npx skills add https://github.com/m0at/claudemd --skill hypogenic-m0at
Automated generation and testing of scientific hypotheses from tabular data using LLMs, enabling systematic exploration of patterns and claims with literature integration.
Install, configure, and run the CLI to generate and test hypotheses on your dataset.
High-intent search queries and answers about installing and using this skill.
Automated hypothesis generation uses LLMs to systematically explore patterns in tabular data, applying data-driven and literature-integrated approaches to test claims. It enables systematic exploration of datasets without manual hypothesis drafting.
Data-driven hypothesis generation from observations applies the HypoGeniC approach to analyze tabular data and produce testable claims. It systematically processes observations to identify patterns and generate corresponding scientific hypotheses.
Literature integration grounds hypotheses by processing PDFs and papers alongside tabular data using the HypoRefine approach. It combines existing theory with data-driven outputs to produce comprehensive, literature-grounded scientific claims.
Automated hypothesis testing applies to tabular data workflows such as deception detection and content analysis. It enables systematic exploration of patterns and claims specific to these domains using configurable LLM-driven templates.
Configurable templates for hypothesis exploration use a modular architecture to adapt prompts to specific domains. Optional components for scripts, references, and assets allow flexible configuration of the hypothesis generation and testing workflow.
LLM-based hypothesis generation relies on the quality of tabular data inputs and literature provided. It supports three approaches—HypoGeniC, HypoRefine, and Union—but requires properly structured data and optional reference materials for optimal theory grounding.
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tamirdresherBreak down claims, test counter-hypotheses, and validate external references.
mikeparcewskiProfile tabular datasets and validate them against JSON schemas to generate quality reports.
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