How does the skill ensure hypotheses are scientifically valid?

The skill evaluates every generated hypothesis against established quality criteria, including testability, falsifiability, parsimony, and explanatory power, as defined in its internal reference framework.

What kind of visual aids does the skill provide?

The skill mandates the creation of 1-2 AI-generated scientific schematics for every report, such as experimental design flowcharts, mechanistic pathway diagrams, or causal relationship maps.

Can I use this for non-biological scientific fields?

Yes. While it has specific tools for biomedical literature (PubMed), the workflow is domain-agnostic and applies to any field requiring mechanistic inquiry, such as physics, chemistry, or environmental science.

Does it provide citations for the generated hypotheses?

Yes, it targets a bibliography of 50+ references, providing parenthetical citations in the main text and comprehensive literature reviews in the report appendices.

Scientific Hypothesis Generator

Name: Scientific Hypothesis Generator
Author: Weiwei-Mao

byWeiwei-Mao

데이터 과학 및 ML

Formulates testable, evidence-based scientific hypotheses and experimental designs from observations using a structured framework.

소개

The Scientific Hypothesis Generator provides a rigorous, systematic framework for scientific inquiry, enabling users to transform raw observations or experimental data into structured, testable hypotheses. It automates comprehensive literature reviews via PubMed and web searches, develops multiple competing mechanistic explanations, and evaluates them against scientific quality criteria like falsifiability and parsimony. Beyond ideation, the skill assists in designing detailed experimental protocols and generating high-quality LaTeX reports complete with mandatory AI-generated scientific schematics, making it an essential tool for researchers and data scientists performing mechanistic studies.

주요 기능

Automated literature synthesis using PubMed and integrated web search
0 GitHub stars
Detailed experimental design patterns including controls and statistical approaches
Structured hypothesis formulation following the scientific method framework
Professional LaTeX report generation with automated AI-powered scientific schematics
Generation of multiple competing mechanistic explanations and causal diagrams

사용 사례

Synthesizing existing scientific literature to identify gaps and propose new theoretical models
Developing testable mechanistic explanations for novel experimental observations or data patterns
Designing rigorous experimental protocols and quantitative predictions for research validation

소개

주요 기능

Automated literature synthesis using PubMed and integrated web search
0 GitHub stars
Detailed experimental design patterns including controls and statistical approaches
Structured hypothesis formulation following the scientific method framework
Professional LaTeX report generation with automated AI-powered scientific schematics
Generation of multiple competing mechanistic explanations and causal diagrams

사용 사례

Synthesizing existing scientific literature to identify gaps and propose new theoretical models
Developing testable mechanistic explanations for novel experimental observations or data patterns
Designing rigorous experimental protocols and quantitative predictions for research validation