Is this compliant with medical research standards?

It is specifically designed to help researchers meet CONSORT standards and provide the necessary documentation for study pre-registration.

Can I use this for digital A/B testing?

Absolutely. While designed for research, the randomization logic is perfect for any software application requiring unbiased assignment between control and test variants.

What randomization methods are included?

The skill supports Simple, Block, Stratified, and Minimization methods, allowing you to choose the best approach based on your sample size and research goals.

Does this skill support reproducible results?

Yes, it utilizes seeded random number generators to ensure that all allocation sequences can be documented and reproduced for peer review or verification.

Research Randomization Logic

Name: Research Randomization Logic
Author: astoreyai

byastoreyai

•

数据科学与机器学习

Implements rigorous randomization procedures for experimental research and unbiased participant allocation.

关于

The Research Randomization skill enables scientists and developers to automate the generation of unbiased experimental groups using industry-standard protocols. It provides a structured framework for simple, block, stratified, and minimization randomization methods, ensuring scientific integrity through reproducible sequences and documented seeds. Designed to help meet CONSORT standards and pre-registration requirements, this skill is essential for minimizing selection bias in clinical trials, A/B tests, and laboratory experiments within the Claude Code environment.

主要功能

Supports multiple methods including Simple, Block, Stratified, and Minimization
Automates documentation for CONSORT standards and pre-registration
2 GitHub stars
Ensures reproducibility with documented seed-based sequence generation
Handles dynamic allocation for small-sample trials via minimization algorithms
Implements allocation concealment protocols to prevent selection bias

使用场景

Assigning participants to control and experimental groups in clinical research
Generating reproducible random sequences for pre-registered scientific studies
Balancing prognostic factors like age and sex through stratified randomization

关于

主要功能

Supports multiple methods including Simple, Block, Stratified, and Minimization
Automates documentation for CONSORT standards and pre-registration
2 GitHub stars
Ensures reproducibility with documented seed-based sequence generation
Handles dynamic allocation for small-sample trials via minimization algorithms
Implements allocation concealment protocols to prevent selection bias

使用场景

Assigning participants to control and experimental groups in clinical research
Generating reproducible random sequences for pre-registered scientific studies
Balancing prognostic factors like age and sex through stratified randomization