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

•

Data Science & ML

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

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.

Key Features

01Integrated literature search strategies for PubMed and general scientific domains

0216 GitHub stars

03Mechanistic hypothesis formulation from preliminary observations

04Quantifiable prediction generation to distinguish between competing theories

05Structured experimental design patterns for controlled and observational studies

06Systematic evaluation based on falsifiability, parsimony, and testability

Use Cases

01Planning data-driven studies and identifying potential confounding variables

02Developing research questions and experimental frameworks for academic papers

03Exploring alternative mechanistic explanations for unexpected laboratory results

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lifangda/claude-plugins hypothesis-generation

For use in Claude.ai and ChatGPT

Download Skill