Does this skill help with experimental design?

Yes, it generates detailed verification strategies that include positive and negative tests, required controls, and methods to distinguish between alternative explanations.

When should I trigger this skill in my workflow?

Trigger this skill after completing an experiment to analyze results, or when you need to transform a broad observation into a concrete plan for your next experiment.

How does the hypothesis scoring system work?

It evaluates hypotheses on a 12-point scale across four dimensions: Specificity, Testability, Quantitativeness, and Falsifiability to ensure scientific rigor.

What is the main purpose of the Hypothesis-Driven skill?

It helps researchers refine vague scientific ideas into specific, testable, and falsifiable hypotheses after reviewing experimental data or preliminary results.

Can I use this for fields other than bioinformatics?

While the examples and references are tailored for bioinformatics, the core logic for hypothesis refinement and validation is applicable to any data-driven scientific field.

Hypothesis-Driven Research

Name: Hypothesis-Driven Research
Author: dakesan

bydakesan

데이터 과학 및 ML

Refines and validates bioinformatics hypotheses by transforming vague observations into specific, testable experimental strategies.

소개

The Hypothesis-Driven Research skill is a specialized tool for bioinformaticians and researchers designed to bridge the gap between raw experimental results and rigorous scientific inquiry. It provides a structured framework to analyze observations, score hypothesis quality across multiple dimensions like specificity and falsifiability, and design robust verification strategies complete with positive and negative controls. By focusing on post-experiment refinement, this skill helps ensure that follow-up studies are grounded in quantitative predictions and sound scientific methodology, making it an essential companion for iterative research workflows.

주요 기능

Multi-dimensional quality scoring for specificity and falsifiability
Identification of alternative explanations and distinguishing tests
0 GitHub stars
Automated verification strategy and experimental design generation
Structured markdown output for lab notebooks and research documentation
Post-experiment hypothesis refinement and quantitative improvement

사용 사례

Validating if a proposed gene-survival correlation is specific and falsifiable
Refining exploratory RNA-seq findings into confirmatory testable predictions
Designing control-heavy verification strategies for newly discovered biomarkers

소개

주요 기능

Multi-dimensional quality scoring for specificity and falsifiability
Identification of alternative explanations and distinguishing tests
0 GitHub stars
Automated verification strategy and experimental design generation
Structured markdown output for lab notebooks and research documentation
Post-experiment hypothesis refinement and quantitative improvement

사용 사례

Validating if a proposed gene-survival correlation is specific and falsifiable
Refining exploratory RNA-seq findings into confirmatory testable predictions
Designing control-heavy verification strategies for newly discovered biomarkers