Can it generate visual diagrams for my research?

Yes, it mandates the use of the scientific-schematics tool to produce publication-quality figures such as mechanistic pathways, experimental flowcharts, and prediction decision trees.

What is the primary output format for the hypotheses?

The skill produces a professional LaTeX document using specialized templates, divided into a concise executive report for quick review and comprehensive technical appendices.

What fields of science is this skill suitable for?

The skill is designed for cross-domain scientific inquiry, including biomedical research (leveraging PubMed integration), physical sciences, and social science research.

How does it ensure the quality of the generated hypotheses?

Each hypothesis is evaluated against a set of quality criteria including testability, falsifiability, parsimony, explanatory power, and alignment with established scientific principles.

Scientific Hypothesis Generation

Name: Scientific Hypothesis Generation
Author: K-Dense-AI

byK-Dense-AI

•

235

Data Science & ML

Generates testable scientific hypotheses, experimental designs, and professional reports from observations and literature synthesis.

About

Scientific Hypothesis Generation is a specialized Claude Code skill designed to facilitate systematic scientific inquiry across diverse domains. It guides users through a rigorous workflow from initial observation and comprehensive literature synthesis—including specialized PubMed integration—to the formulation of competing mechanistic hypotheses and testable predictions. By integrating AI-powered scientific schematics and professional LaTeX reporting, it enables researchers to produce publication-quality hypothesis frameworks complete with experimental protocols and rigorous quality assessments, ensuring all theories are falsifiable and evidence-based.

Key Features

Detailed experimental design planning including controls, measurements, and statistical approaches
235 GitHub stars
Systematic generation of 3-5 competing mechanistic hypotheses with supporting evidence
Professional LaTeX report generation with structured boxes for executive summaries and technical appendices
Automated literature synthesis using PubMed and multi-domain web search strategies
Mandatory generation of publication-quality scientific schematics and mechanistic diagrams

Use Cases

Synthesizing large volumes of scientific literature to identify knowledge gaps and mechanistic pathways
Designing rigorous experimental protocols and quantitative predictions for academic or clinical studies
Developing novel research directions and testable theories from preliminary observational data

About

Key Features

Detailed experimental design planning including controls, measurements, and statistical approaches
235 GitHub stars
Systematic generation of 3-5 competing mechanistic hypotheses with supporting evidence
Professional LaTeX report generation with structured boxes for executive summaries and technical appendices
Automated literature synthesis using PubMed and multi-domain web search strategies
Mandatory generation of publication-quality scientific schematics and mechanistic diagrams

Use Cases

Synthesizing large volumes of scientific literature to identify knowledge gaps and mechanistic pathways
Designing rigorous experimental protocols and quantitative predictions for academic or clinical studies
Developing novel research directions and testable theories from preliminary observational data