home / skills / jeremylongshore / claude-code-plugins-plus-skills / a-b-test-config-creator

a-b-test-config-creator skill

/skills/08-ml-deployment/a-b-test-config-creator

This skill guides you through creating a b test config, generates production-ready configs, and validates outputs for ML deployment.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill a-b-test-config-creator

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
2.1 KB
---
name: "a-b-test-config-creator"
description: |
  Create a b test config creator operations. Auto-activating skill for ML Deployment.
  Triggers on: a b test config creator, a b test config creator
  Part of the ML Deployment skill category. Use when writing or running tests. Trigger with phrases like "a b test config creator", "a creator", "a".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# A B Test Config Creator

## Overview

This skill provides automated assistance for a b test config creator tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "a b test config creator" in your request
- Ask about a b test config creator patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

## Instructions

1. Provides step-by-step guidance for a b test config creator
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with a b test config creator"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Deployment** skill category.
Tags: mlops, serving, inference, monitoring, production

Overview

This skill automates creation of A/B test configuration for ML deployments, producing clear, production-ready configs and code snippets. It focuses on safe experimentation patterns, traffic routing, metric collection, and validation. Use it to standardize A/B experiments and accelerate rollout of model variants.

How this skill works

When triggered, the skill inspects requested experiment parameters (variants, traffic splits, metrics, duration) and generates deployment-ready configuration files and code for common serving platforms. It applies industry best practices: default safety checks, data logging hooks, rollout/roll-back rules, and validation tests. It can also output sample monitoring queries and validation scripts to ensure experiment integrity.

When to use it

  • Setting up controlled A/B tests for new model versions or features
  • Creating traffic-splitting and rollout configs for model serving
  • Generating standard experiment templates for CI/CD and MLOps pipelines
  • Validating that experiment configs meet safety and monitoring requirements
  • Producing reproducible configs for audit and compliance

Best practices

  • Define clear primary and secondary metrics before creating configs
  • Use conservative traffic ramps and automatic rollback thresholds
  • Include deterministic seeding and consistent request routing for reproducibility
  • Log sufficient metadata and labels for analysis and attribution
  • Validate configs with dry-run checks and unit tests before deployment

Example use cases

  • Generate Kubernetes/Ingress or service-mesh traffic-splitting manifest for two model variants
  • Create feature-flag based runtime switch with monitoring and rollback rules
  • Produce an experiment config that integrates with your CI pipeline and test harness
  • Output Prometheus/Grafana query examples to evaluate experiment metrics
  • Provide a validation script to run local or staging smoke tests before production rollout

FAQ

What inputs are required to generate a config?

Provide variant names/IDs, desired traffic split, primary metric and threshold, experiment duration, and target deployment platform.

Can it generate configs for multiple serving platforms?

Yes. It can produce templates for common platforms (Kubernetes, Istio/service-mesh, feature-flag systems) and export code snippets for integration.