BeClaude

reproai

New
19GitHub TrendingGeneralby leoyyang

First seen 6/18/2026

Summary

The reproai skill helps developers reproduce and debug AI-related issues by automating the setup of reproducible environments, capturing model outputs, and comparing results across different configurations.

  • It streamlines the process of isolating bugs in machine learning pipelines and ensures consistent experimentation.

Install & Usage

1
Create the skills directory
mkdir -p .claude/skills
2
Download the skill file

Add the configuration to .claude/skills/reproai.md

3
Invoke in Claude Code
/reproai

Use Cases

Reproduce a training run with specific hyperparameters to verify a reported bug.
Compare model outputs across different versions of a library to identify regressions.
Set up a minimal reproducible example for a data preprocessing issue.
Automate the collection of logs and metrics for a failing inference pipeline.
Replicate a deployment environment locally to test a production issue.
Generate a reproducible script for a research experiment to share with collaborators.

Usage Examples

1

/reproai reproduce training run --config configs/experiment.yaml --seed 42

2

Set up a minimal reproducible example for this error: 'RuntimeError: CUDA out of memory'

3

/reproai compare outputs --baseline v1.0 --candidate v2.0 --metric accuracy

View source on GitHub

Security Audits

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Frequently Asked Questions

What is reproai?

The reproai skill helps developers reproduce and debug AI-related issues by automating the setup of reproducible environments, capturing model outputs, and comparing results across different configurations. It streamlines the process of isolating bugs in machine learning pipelines and ensures consistent experimentation.

How to install reproai?

To install reproai: create the skills directory (mkdir -p .claude/skills), then add the config to .claude/skills/reproai.md. Finally, /reproai in Claude Code.

What is reproai best for?

reproai is a other categorized under General. Created by leoyyang.

What can I use reproai for?

reproai is useful for: Reproduce a training run with specific hyperparameters to verify a reported bug.; Compare model outputs across different versions of a library to identify regressions.; Set up a minimal reproducible example for a data preprocessing issue.; Automate the collection of logs and metrics for a failing inference pipeline.; Replicate a deployment environment locally to test a production issue.; Generate a reproducible script for a research experiment to share with collaborators..