What scientific domains are covered?

The skill is designed for cross-domain scientific inquiry, including biomedical research, life sciences, and physical sciences.

Does it help with experimental design?

Yes, it provides structured frameworks for proposing specific tests, including defining controls, measurements, and appropriate sample sizes for each hypothesis.

Can this skill search for specific scientific papers?

Yes, it utilizes WebFetch and specialized search strategies for PubMed and general web sources to ground all generated hypotheses in current peer-reviewed literature.

How does it evaluate hypothesis quality?

Every hypothesis is assessed against rigorous scientific criteria including testability, falsifiability, parsimony, explanatory power, and consistency with established principles.

Scientific Hypothesis Generator

Name: Scientific Hypothesis Generator
Author: Zehong-Wang

byZehong-Wang

0•

Data Science & ML

Generates testable, evidence-based scientific hypotheses and experimental designs across multiple research domains.

The Scientific Hypothesis Generation skill empowers researchers and scientists to move from raw observations to rigorous, testable theories. By leveraging a systematic workflow that includes comprehensive literature searches—utilizing specialized strategies for PubMed and general web sources—evidence synthesis, and the evaluation of competing mechanisms, this skill helps formulate high-quality hypotheses that meet standards for falsifiability, parsimony, and explanatory power. It is an essential tool for designing robust experiments, identifying knowledge gaps, and planning mechanistic studies in biomedical and general scientific fields.

Key Features

01Experimental design planning and testable prediction mapping

02Quality assessment based on testability, falsifiability, and parsimony

03Mechanistic explanation generation for competing scientific theories

040 GitHub stars

05Systematic evidence-based hypothesis formulation from observations

06Integrated literature search strategies for PubMed and web sources

Use Cases

01Designing new laboratory or clinical research experiments based on preliminary data

02Translating comprehensive literature reviews into actionable research directions and testable predictions

03Exploring alternative mechanistic explanations for anomalous or unexpected data patterns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos hypothesis-generation

For use in Claude.ai and ChatGPT

Download Skill