What is the primary output of this skill?

The skill produces a structured report containing background context, competing hypotheses with mechanistic explanations, quality assessments, proposed experimental designs, and specific testable predictions.

Can this skill be used for non-biological sciences?

Yes. While it includes specific optimizations for PubMed in biomedical research, the core workflow is designed for systematic scientific inquiry across all domains, including physics, chemistry, and social sciences.

Does it perform live literature searches?

It provides the systematic strategy and prompts Claude to utilize its integrated search tools, such as WebFetch and WebSearch, to gather and synthesize current scientific evidence.

How does it evaluate hypothesis quality?

The skill assesses hypotheses against a specific framework evaluating testability, falsifiability, parsimony (Occam's razor), explanatory power, scope, and consistency with established principles.

Scientific Hypothesis Generator

Name: Scientific Hypothesis Generator
Author: Activer007

byActiver007

Data Science & ML

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

About

The Scientific Hypothesis Generation skill provides a rigorous, systematic framework for scientific inquiry within Claude Code. It guides users through the entire research process—from clarifying initial observations and conducting deep literature searches via PubMed and web tools to synthesizing evidence and proposing multiple mechanistic explanations. By applying quality criteria such as falsifiability, parsimony, and explanatory power, this skill helps researchers transform raw data into robust experimental plans and measurable predictions across any scientific domain.

Key Features

Structured experimental design templates for lab and observational studies
Systematic evidence synthesis from literature and observations
Formulation of specific, quantitative predictions for hypothesis testing
Rigorous quality assessment using falsifiability and parsimony criteria
0 GitHub stars
Generation of multiple competing mechanistic hypotheses

Use Cases

Developing research frameworks and experimental protocols for new scientific studies
Exploring alternative explanations for anomalous or conflicting experimental data
Conducting literature-based discovery to identify and fill gaps in existing research

About

Key Features

Structured experimental design templates for lab and observational studies
Systematic evidence synthesis from literature and observations
Formulation of specific, quantitative predictions for hypothesis testing
Rigorous quality assessment using falsifiability and parsimony criteria
0 GitHub stars
Generation of multiple competing mechanistic hypotheses

Use Cases

Developing research frameworks and experimental protocols for new scientific studies
Exploring alternative explanations for anomalous or conflicting experimental data
Conducting literature-based discovery to identify and fill gaps in existing research