How does this skill assist in the scientific process?

It provides a systematic workflow to move from raw observations to testable, evidence-backed hypotheses, ensuring your research is grounded in existing literature and structured for validation.

Can I use this for biomedical research specifically?

Yes, the skill includes specialized strategies for biomedical topics, including WebFetch integration for PubMed literature synthesis and review.

What criteria are used to evaluate hypotheses?

Hypotheses are assessed against key scientific standards: testability, falsifiability, parsimony, explanatory power, scope, and consistency with established principles.

Does this skill provide experimental protocols?

It generates high-level experimental designs and patterns, helping you define measurements, controls, and methods required to test your specific predictions.

Scientific Hypothesis Generator

Name: Scientific Hypothesis Generator
Author: lifangda

bylifangda

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データサイエンスとML

Generates testable, evidence-based scientific hypotheses and experimental designs from observations or literature.

概要

This skill enables Claude to act as a rigorous scientific research assistant by systematically formulating and evaluating hypotheses. It guides users through a comprehensive workflow that includes analyzing complex phenomena, conducting evidence-based literature searches via PubMed and web tools, and synthesizing findings into mechanistic explanations. By providing structured frameworks for experimental design and quality assessment, this skill is invaluable for researchers and scientists looking to explore competing explanations, identify knowledge gaps, and develop quantifiable predictions for academic or industrial inquiry.

主な機能

Integrated literature search strategies for PubMed and general scientific domains
16 GitHub stars
Mechanistic hypothesis formulation from preliminary observations
Quantifiable prediction generation to distinguish between competing theories
Structured experimental design patterns for controlled and observational studies
Systematic evaluation based on falsifiability, parsimony, and testability

ユースケース

Planning data-driven studies and identifying potential confounding variables
Developing research questions and experimental frameworks for academic papers
Exploring alternative mechanistic explanations for unexpected laboratory results

概要

主な機能

Integrated literature search strategies for PubMed and general scientific domains
16 GitHub stars
Mechanistic hypothesis formulation from preliminary observations
Quantifiable prediction generation to distinguish between competing theories
Structured experimental design patterns for controlled and observational studies
Systematic evaluation based on falsifiability, parsimony, and testability

ユースケース

Planning data-driven studies and identifying potential confounding variables
Developing research questions and experimental frameworks for academic papers
Exploring alternative mechanistic explanations for unexpected laboratory results