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AB Test Framework

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Compare models with A/B testing for selection

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About This Skill

# A/B Testing Framework

Description

Compare models with A/B testing for selection

Source Reference

This skill is derived from 20. Testing & Quality Assurance of the OpenClaw Agent Mastery Index v4.1.

Sub-heading: A/B Testing Frameworks for Model Selection

Complexity: high

Input Parameters

| Name | Type | Required | Description | |------|------|----------|-------------| | `model_a` | string | Yes | First model | | `model_b` | string | Yes | Second model | | `test_prompts` | array | Yes | Test prompts |

Output Format

```json { "status": <string>, "details": <object>, "winner": <string>, "confidence": <number> } ```

Usage Examples

Example 1: Basic Usage

```javascript const result = await openclaw.skill.run('ab-test-framework', { model_a: "value", model_b: "value", test_prompts: 123 }); ```

Example 2: With Optional Parameters

```javascript const result = await openclaw.skill.run('ab-test-framework', { model_a: "value", model_b: "value", test_prompts: [] }); ```

Security Considerations

A/B test security per Category 8; prevent test manipulation

Additional Security Measures

  1. Input Validation: All inputs are validated before processing
  2. Least Privilege: Operations run with minimal required permissions
  3. Audit Logging: All actions are logged for security review
  4. Error Handling: Errors are sanitized before returning to caller

Troubleshooting

Common Issues

| Issue | Cause | Solution | |-------|-------|----------| | Permission denied | Insufficient privileges | Check file/directory permissions | | Invalid input | Malformed parameters | Validate input format | | Dependency missing | Required module not installed | Run `npm install` |

Debug Mode

Enable debug logging: ```javascript openclaw.logger.setLevel('debug'); const result = await openclaw.skill.run('ab-test-framework', { ... }); ```

Related Skills

  • `model-routing-manager`
  • `performance-benchmarker`
  • * @param {string} params.model_a - First model
  • * @param {string} params.model_b - Second model
  • * @param {Array} params.test_prompts - Test prompts

Use Cases

  • Compare multiple LLM models side-by-side to select the best performer for a task
  • Design controlled experiments to evaluate model accuracy across different prompt styles
  • Measure latency, cost, and quality trade-offs between candidate AI models
  • Run statistical A/B tests on model outputs to validate upgrade decisions
  • Benchmark fine-tuned models against baseline to quantify improvement

Pros & Cons

Pros

  • +Structured methodology for model comparison reduces subjective bias in selection
  • +Covers the full A/B testing lifecycle — design, execution, and statistical analysis
  • +Security verified with clean safety scan across all categories

Cons

  • -High complexity — requires understanding of experimental design and statistics
  • -Content is template-derived from OpenClaw Agent Mastery Index, not deeply detailed
  • -No built-in integration with model serving platforms for automated benchmarking

FAQ

What does AB Test Framework do?
Compare models with A/B testing for selection
What platforms support AB Test Framework?
AB Test Framework is available on Claude Code, OpenClaw.
What are the use cases for AB Test Framework?
Compare multiple LLM models side-by-side to select the best performer for a task. Design controlled experiments to evaluate model accuracy across different prompt styles. Measure latency, cost, and quality trade-offs between candidate AI models.

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