A high-performance framework for comparing and selecting the best LLMs through automated A/B testing and statistical analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ab-test-framework
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ab-test-framework using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is A/B Testing Framework?
The A/B Testing Framework is a sophisticated tool designed for developers to systematically evaluate and compare the performance of different Large Language Models. Part of the broader Openclaw Skills ecosystem, it allows for head-to-head comparisons between models using a standardized set of test prompts. By leveraging statistical libraries, it provides data-driven insights into which model performs better for specific tasks, ensuring that your AI agents use the most effective backend available.
Derived from the OpenClaw Agent Mastery Index, this skill focuses on high-complexity model selection. It enables teams to move beyond anecdotal evidence by generating empirical data on model quality, response times, and accuracy, making it an essential part of any production-grade AI deployment.
A/B Testing Framework Use Cases
- Evaluating model performance for specific prompt engineering tasks.
- Comparing cost-to-quality ratios between different LLM providers.
- Validating model upgrades or fine-tuned versions before deploying them to production environments.
- Identifying the most reliable model for niche domain-specific queries within the Openclaw Skills ecosystem.
- Automating the selection process for multi-model routing architectures.
How A/B Testing Framework Works
- Initialize the framework by specifying model_a and model_b along with a set of diverse test prompts.
- Execute the prompts against both models simultaneously using the openclaw/llm dependency.
- Collect and normalize responses from both model endpoints to ensure a fair and unbiased comparison.
- Analyze the results using integrated statistical libraries to determine performance metrics and quality scores.
- Output a detailed JSON report including the identified winner and a confidence score for the selection.
A/B Testing Framework Setup
To integrate this component into your project, ensure you have the necessary dependencies installed. Use the following commands to set up your environment for Openclaw Skills development:
npm install @openclaw/llm stats-library
Once installed, you can call the skill directly within your application logic by providing the required model identifiers and an array of test prompts.
A/B Testing Framework Data Schema & Taxonomy
The framework utilizes a structured data schema to ensure consistency across tests and easy integration with other Openclaw Skills. The output follows this format:
| Key | Type | Description |
|---|---|---|
| status | string | The execution status of the test suite |
| details | object | Detailed breakdown of responses and raw scores per model |
| winner | string | The identifier of the model that achieved higher performance |
| confidence | number | The statistical confidence level of the test result |
Input parameters must include model_a (string), model_b (string), and test_prompts (array of strings).
A/B Testing Framework Advanced Features
- Direct integration with model-routing-manager for automated model switching based on real-time test results.
- Comprehensive audit logging and security validation to prevent test manipulation.
- Support for high-complexity testing scenarios as defined in the OpenClaw Agent Mastery Index v4.1.
- Customizable confidence thresholds to ensure only statistically significant wins trigger model changes.
- Least privilege execution mode for secure enterprise-level testing.
SKILL.md
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