How do AI agents execute these tests?

The agents parse your YAML scenarios and use specialized drivers to perform actions like clicking, typing, or waiting, then validate the resulting state against your expected outcomes.

What is outside-in testing?

Outside-in testing is a methodology that verifies application behavior from an external user's perspective, focusing on the interface and outputs rather than the internal code implementation.

Which platforms can I test with this skill?

This skill supports testing for Command Line Interfaces (CLI), Terminal User Interfaces (TUI), Web applications, and Electron desktop apps.

Will my tests break if I refactor my code?

No. Because these tests focus on the 'What' (behavior) rather than the 'How' (implementation), they remain valid as long as the user-facing interface stays the same.

Does this skill provide evidence of test results?

Yes, the underlying framework generates comprehensive reports including screenshots, terminal output captures, and logs for every step of the execution.

Outside-In Agentic Testing

Name: Outside-In Agentic Testing
Author: rysweet

byrysweet

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Security & Testing

Generates and executes behavior-driven test scenarios for CLI, TUI, Web, and Electron applications using AI agents.

About

The Outside-In Testing skill enables developers to create declarative, implementation-agnostic test scenarios that focus on user-facing behavior rather than internal code logic. By leveraging the gadugi-agentic-test framework, users can define YAML-based tests for diverse platforms including command-line interfaces, terminal UIs, web apps, and Electron desktop applications. AI agents interpret these scenarios to execute actions, observe outcomes, and provide detailed evidence reports, making it an ideal solution for smoke testing, refactoring safety, and cross-platform quality assurance.

Key Features

Implementation-agnostic testing that survives internal code refactors
Cross-platform support for CLI, TUI, Web, and Electron applications
AI-powered execution and observation for complex user interactions
Declarative YAML-based test scenario definitions that serve as documentation
Automatic evidence collection including screenshots, logs, and execution reports
16 GitHub stars

Use Cases

Ensuring refactoring safety by verifying that external behavior remains unchanged
Creating smoke tests to validate critical user flows across different interfaces
Writing readable acceptance criteria that can be automatically verified by AI agents

About

Key Features

Implementation-agnostic testing that survives internal code refactors
Cross-platform support for CLI, TUI, Web, and Electron applications
AI-powered execution and observation for complex user interactions
Declarative YAML-based test scenario definitions that serve as documentation
Automatic evidence collection including screenshots, logs, and execution reports
16 GitHub stars

Use Cases

Ensuring refactoring safety by verifying that external behavior remains unchanged
Creating smoke tests to validate critical user flows across different interfaces
Writing readable acceptance criteria that can be automatically verified by AI agents