auto-test

Run a local dry-run of an AI agent against Azure DevOps data.

6|3|Updated Mar 21, 2026

Search Tags:#automation #testing #devops #pipeline #ai-agent #ado #dry-run

Ready to run

npx skills add https://github.com/easingthemes/dx-aem-flow --skill auto-test

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: auto-test
Source: https://github.com/easingthemes/dx-aem-flow/tree/main/plugins/dx-automation/skills/auto-test
Command: npx skills add https://github.com/easingthemes/dx-aem-flow --skill auto-test

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Run a local dry-run of an AI automation agent against real Azure DevOps data to verify connectivity and agent outputs without posting or committing.

Core Features & Use Cases

Dry-run execution to validate agent behavior against ADO data without modifying state.
Supports multiple agents (e.g., dor, dod, pr-review, pr-answer, bugfix, qa, devagent, docagent, estimation) with clear logging and status.
Provides a safe pre-live testing workflow to confirm output expectations before deployment.

Quick Start

Run the agent locally in dry-run mode to validate end-to-end behavior against sample ADO data.

Frequently Asked Questions about auto-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I test AI automation agents against Azure DevOps data without modifying state?▼

You can test AI automation agents against Azure DevOps data safely by running a local dry-run. This verifies agent outputs and connectivity without posting or committing changes to your live environment.

What credentials do I need to run a local dry-run of an AI agent against ADO?▼

Running a local dry-run against ADO requires the ANTHROPIC_API_KEY and ADO_MCP_AUTH_TOKEN. These credentials must be stored in a local .env file to authenticate the agent securely.

Can I test different AI agent prompts for work items and pull requests before live deployment?▼

Yes, you can test different AI agent prompts against work items or pull requests in a controlled local environment. This pre-live testing workflow confirms output expectations before actual deployment.

What types of AI automation agents can I validate in a local dry-run mode?▼

You can validate multiple AI automation agents in dry-run mode, including agents for dor, dod, pr-review, pr-answer, bugfix, qa, devagent, docagent, and estimation. Each run provides clear logging and status.

What is the best way to verify AI agent connectivity and behavior before deploying to an Azure DevOps pipeline?▼

The best way to verify AI agent connectivity is a local dry-run against real ADO data. It safely validates end-to-end agent behavior and integration before introducing the agent into a live DevOps pipeline.

Are there limitations when testing AI automation in dry-run mode against Azure DevOps?▼

The primary limitation of dry-run mode is that it prevents posting or committing any changes to Azure DevOps. It is strictly designed for local validation and cannot execute live state modifications.