A sophisticated multi-agent workflow designed to conduct automated, double-blind A/B testing and comparative evaluations between different AI models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ab-test-agent-workflow
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-agent-workflow 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 Test Agent Workflow?
The A/B Test Agent Workflow provides a structured framework for objectively comparing the performance of multiple AI agents or Large Language Models. By utilizing a multi-role architecture—comprising a Coordinator, parallel Contestants, and a neutral Judge—this skill eliminates evaluation bias and provides data-driven insights into model quality. Integrating this into your development process via Openclaw Skills allows for systematic benchmarking across multiple rounds of testing.
This workflow is essential for developers who need to move beyond subjective testing. It automates the process of prompt distribution, result anonymization, and rubric-based scoring, ultimately producing a comprehensive report that identifies the superior model for a specific task based on quantifiable metrics.
A/B Test Agent Workflow Use Cases
- Benchmarking different LLM providers like Claude, GPT, and Gemini for specific enterprise tasks.
- Evaluating the effectiveness of different system prompts or agent instructions.
- Conducting automated quality assurance for high-stakes AI content generation.
- Testing model performance regressions when upgrading to newer model versions.
- Performing multi-round blind tests to determine the most creative or logically sound agent for complex reasoning.
How A/B Test Agent Workflow Works
- The Coordinator receives the user task, determines the number of rounds, and configures the scoring rubric.
- The Coordinator spawns two independent sub-agent sessions for Contestant A and Contestant B.
- Identical prompts are dispatched to both contestants, who generate their responses in isolation.
- The Coordinator collects the outputs and passes them through an anonymizer to strip model-specific identifiers.
- Anonymized results (Plan 1 and Plan 2) are sent to a Judge agent who scores them based on the Rubric.
- The Coordinator aggregates scores across all rounds and generates a final structured report revealing the winner.
A/B Test Agent Workflow Setup
To deploy this workflow as part of your Openclaw Skills library, you can use the provided runner script to execute or simulate tests. Ensure your environment supports sub-agent spawning.
# Run a self-test to verify workflow logic without consuming sub-agents
python scripts/runner.py --test --rounds 3
# Execute a live A/B test between two models
python scripts/runner.py --prompt "Analyze this dataset" --rounds 5 --model-a model-1 --model-b model-2
A/B Test Agent Workflow Data Schema & Taxonomy
The skill organizes evaluation data into a structured format for easy analysis. The final report follows this taxonomy:
| Key | Description |
|---|---|
| test_summary | Contains the task definition, model names, and rubric dimensions. |
| rounds | A collection of round-by-round data including raw outputs and individual judge scores. |
| judge_scores | Detailed points for accuracy, completeness, expression, and depth. |
| final_result | The aggregate winner, win rates, and a confidence score based on round consistency. |
| metadata | Tracks anonymization logs and session identifiers. |
A/B Test Agent Workflow Advanced Features
- Script-driven execution for automated benchmarking within CI/CD pipelines.
- Automated identity filtering via anonymizer tools to prevent judge bias.
- Customizable Rubric templates for specialized tasks like code generation or logic reasoning.
- Intelligent timeout handling that prioritizes code extraction before session expiry.
- Support for multi-round statistical aggregation to ensure result reliability.
SKILL.md
Loading
Related Openclaw Skills
Desktop Pet
A Python-based virtual companion featuring animated pixel art, four interactive environments, and real-time care mechanics.
Learning Builder
A specialized skill for converting vague learning objectives into structured, output-oriented learning packs backed by authoritative research.
Seedance AI Video Generator
A professional AI video generation skill that uses Volcengine Seedance 2.0 models to transform text, images, and audio into cinematic video content.
Proactive Agent
A sophisticated architecture that shifts AI agents from passive followers into persistent, self-improving, and proactive partners.
Gett Brand Insights
A specialized research skill for analyzing the Gett brand, product offerings, and market positioning.
AI Bikini Photo Editor CLI Skill
An AI-powered command-line tool that transforms standard person photos into natural bikini scenes using customizable prompts.
Featured*
