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

0•

データサイエンスとML

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

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.

主な機能

01Structured experimental design templates for lab and observational studies

02Systematic evidence synthesis from literature and observations

03Formulation of specific, quantitative predictions for hypothesis testing

04Rigorous quality assessment using falsifiability and parsimony criteria

050 GitHub stars

06Generation of multiple competing mechanistic hypotheses

ユースケース

01Developing research frameworks and experimental protocols for new scientific studies

02Exploring alternative explanations for anomalous or conflicting experimental data

03Conducting literature-based discovery to identify and fill gaps in existing research

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills hypothesis-generation

For use in Claude.ai and ChatGPT

主な機能

01Structured experimental design templates for lab and observational studies

02Systematic evidence synthesis from literature and observations

03Formulation of specific, quantitative predictions for hypothesis testing

04Rigorous quality assessment using falsifiability and parsimony criteria

050 GitHub stars

06Generation of multiple competing mechanistic hypotheses

ユースケース

01Developing research frameworks and experimental protocols for new scientific studies

02Exploring alternative explanations for anomalous or conflicting experimental data

03Conducting literature-based discovery to identify and fill gaps in existing research

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills hypothesis-generation

For use in Claude.ai and ChatGPT