How does this skill search scientific literature?

The skill utilizes WebFetch for targeted PubMed searches and general WebSearch to access preprints, reviews, and primary research to ground hypotheses in existing evidence.

Does it provide specific experimental protocols?

It generates high-level experimental designs, including control selection and measurement strategies, which serve as a blueprint for developing detailed laboratory protocols.

Is this skill limited to biomedical research?

No, while it includes specific biomedical optimizations like PubMed integration, the underlying workflow is designed for scientific inquiry across all domains including physics, chemistry, and social sciences.

How are the generated hypotheses evaluated?

Hypotheses are assessed against rigorous criteria including testability, falsifiability, parsimony, explanatory power, and consistency with established principles.

Scientific Hypothesis Generation

Name: Scientific Hypothesis Generation
Author: Microck

byMicrock

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데이터 과학 및 ML

Generates rigorous, testable scientific hypotheses and detailed experimental designs based on observations and existing literature.

소개

Scientific Hypothesis Generation is a specialized skill designed to transform raw observations into structured scientific inquiries. It guides Claude through a systematic workflow that includes comprehensive literature synthesis via PubMed and web searches, the development of competing mechanistic explanations, and the evaluation of hypothesis quality based on falsifiability and parsimony. This skill is essential for researchers and students who need to plan experiments, explore alternative explanations for data patterns, and formulate specific, measurable predictions across any scientific domain.

주요 기능

Detailed experimental design and quantitative prediction modeling
81 GitHub stars
Mechanistic explanation development for complex scientific phenomena
Structured reporting using standardized scientific output templates
Quality assessment based on testability, falsifiability, and parsimony
Systematic literature synthesis using PubMed and web-based evidence

사용 사례

Synthesizing existing literature to identify gaps and propose new theories
Developing research questions and experimental frameworks for new studies
Evaluating alternative mechanisms for unexpected laboratory observations

소개

주요 기능

Detailed experimental design and quantitative prediction modeling
81 GitHub stars
Mechanistic explanation development for complex scientific phenomena
Structured reporting using standardized scientific output templates
Quality assessment based on testability, falsifiability, and parsimony
Systematic literature synthesis using PubMed and web-based evidence

사용 사례

Synthesizing existing literature to identify gaps and propose new theories
Developing research questions and experimental frameworks for new studies
Evaluating alternative mechanisms for unexpected laboratory observations