AB Test Framework
VerifiedCompare models with A/B testing for selection
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# 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
- Input Validation: All inputs are validated before processing
- Least Privilege: Operations run with minimal required permissions
- Audit Logging: All actions are logged for security review
- 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
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