How does this skill search for scientific literature?

The skill utilizes WebFetch for targeted PubMed URL access and WebSearch to find recent preprints, reviews, and primary research papers across general scientific domains.

Is this limited to biomedical research?

No, while it has specific support for PubMed, the methodology is designed for scientific inquiry across all domains including physics, chemistry, and social sciences.

What criteria are used to evaluate hypothesis quality?

Hypotheses are assessed based on testability, falsifiability, parsimony (simplicity), explanatory power, scope, consistency with known principles, and novelty.

Can this skill help with laboratory experimental design?

Yes, it proposes specific experimental designs including measurements, controls, sample size considerations, and statistical approaches tailored to the hypothesis.

Scientific Hypothesis Generation

Name: Scientific Hypothesis Generation
Author: jimmc414

byjimmc414

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324

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Data Science & ML

Generates testable scientific hypotheses and detailed experimental designs based on observations and literature evidence.

This skill transforms Claude into an autonomous AI scientist, providing a systematic framework for scientific discovery and inquiry. It guides the model through a multi-step process: from initial observation and comprehensive literature synthesis (via PubMed and web searches) to the generation of multiple competing mechanistic hypotheses. By evaluating these theories against rigorous criteria such as falsifiability and parsimony, the skill enables researchers to design robust experimental tests and formulate specific, quantitative predictions across any scientific domain.

Key Features

01Rigorous evaluation against falsifiability and parsimony quality criteria

02Automated literature synthesis from PubMed and global scientific sources

03Mechanistic explanation generation for complex scientific phenomena

04324 GitHub stars

05Systematic multi-step scientific hypothesis formulation workflow

06Comprehensive experimental design planning including controls and methods

Use Cases

01Developing novel research directions from preliminary experimental data

02Designing rigorous experiments to distinguish between competing scientific theories

03Identifying research gaps and formulating mechanistic studies for peer-reviewed papers

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jimmc414/kosmos hypothesis-generation

For use in Claude.ai and ChatGPT

Download Skill