randomization-gen

What This Skill Does

The randomization-gen skill for OpenClaw is a specialized utility designed to generate scientifically robust, blocked randomization sequences for clinical trials, animal studies, and various experimental designs. It removes the bias from participant allocation by ensuring that treatment group assignments follow a strict block randomization methodology. This prevents selection bias and ensures that study arms remain balanced over the course of participant enrollment. The tool supports configurable block sizes, which must be multiples of the number of treatment groups to guarantee equal distribution.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Ensure you have the necessary permissions to execute local scripts and write to your workspace directory.

Command: clawhub install openclaw/skills/skills/aipoch-ai/randomization-gen

Use Cases

This tool is intended for researchers and data scientists who require auditable and verifiable randomization protocols. Common applications include:

• Clinical Trial Design: Generating allocation sequences for randomized controlled trials (RCTs).
• Animal Study Randomization: Ensuring laboratory subjects are assigned to experimental conditions without bias.
• Stratified Allocation: Creating balanced blocks that account for specific study cohorts.
• Blinded Study Prep: Creating sealed, sequence-ready lists for concealed allocation processes.

Example Prompts

• "Generate a randomization list for 100 subjects with 2 study arms and a block size of 10. Save the result to trial_seq.txt."
• "I am planning a clinical trial with 3 groups and 150 participants. Please generate the randomization schedule using a block size of 6."
• "Run a randomization sequence for 50 subjects across 2 groups using a block size of 4 to ensure equal distribution throughout the enrollment phase."

Tips & Limitations

• Block Size Rule: Ensure your block size is always a multiple of the number of treatment groups. If your block size is not divisible by the group count, the tool will return a validation error.
• File Output: While the default output is 'randomization.txt', you should always provide a unique filename if you are running multiple iterations to avoid overwriting your previous sequences.
• Safety First: This tool executes logic locally. Ensure that your output files are treated as sensitive data, as they represent the allocation sequence for your study participants. Always store generated files in secure directories if the study involves sensitive human data.