Software Engineering Made Simple

About

Fri, 08 May 2026 00:00:00 +0000

Hi, I’m Mor Dabastany.

A seasoned technologist working at the intersection of AI-TestOps and AI-Automation. Currently at Palo Alto Networks, based out of Israel.

What I work on

AI-driven testing & automation - pushing quality engineering past hand-written assertions into agent-based validation.
Quality engineering transformation - taking teams from brittle manual QA to engineered, scalable test systems.
AI agent skills & validation - how do you test something that doesn’t behave deterministically? That’s the question.
Internal knowledge hubs - putting company knowledge in front of AI so engineers actually find what they need.
Prompt engineering & agentic workflows - practical, not theatrical.

Why this site

I’ve been writing on LinkedIn for years. Reach is fine, platform is not - posts vanish into a feed, formatting is rough, code blocks unreadable. So everything moves here: searchable, properly typeset, with an RSS feed for people who still believe in the open web.

Nobody Tests Their AI Agent Skills. Here's Why That's a Problem (and How to Fix It)

Wed, 15 Apr 2026 00:00:00 +0000

There are 47,000+ AI agent skills across 6,300+ repositories (according to a SkillsBench study that queries GitHub and others for the numbers). Almost none of them are tested beyond a “vibe check” - try it a few times, looks good, ship it.

I was assigned to come up with a framework-agnostic testing architecture for agent skills, and the research surfaced some things worth sharing.

The core insight: skills need 4 types of testing, not 1

Most people think “testing a skill” means running the agent and seeing if it works. That’s only one layer. We identified four distinct testing concerns:

An Internal Knowledge Hub, Built 100% by AI

Sun, 15 Mar 2026 00:00:00 +0000

The Problem

Every engineering team accumulates internal tools, frameworks, guides, and resources over time. But discovering and accessing them? That’s a different story. Links get buried in Slack threads, wikis grow stale, and onboarding new team members means repeating the same “here’s where you find X” conversations. Needless to say, that many teams are also using Confluence/Jira systems which complicates it even further.

We needed a single, clean, internal portal, a front door to everything our team builds and maintains. But we didn’t want to spin up a backend, manage a database, or wrestle with a CMS.

Claude Sauce - Spicey, Yet Nicey

Tue, 13 Jan 2026 00:00:00 +0000

TL;DR

We built an internal framework - Claude Sauce - that lets teams package domain knowledge, workflows, and automation into reusable modules for Claude. Think of it as creating “expert modes” that anyone can use, share, and improve.

The Problem We Set Out to Solve

Claude is extremely capable out of the box - but every organization has its own:

Deep domain knowledge no general model can fully capture
Established workflows that require specific sequences and checks
Tooling and integrations unique to its tech stack
Business logic that changes how tasks should be executed

As a result, every specialized use case meant the same friction: people re-explained context, re-wrote instructions, and hoped they didn’t forget critical nuances. Knowledge stayed trapped in individuals instead of becoming reusable.

Don't Waste Time to Style Your Code. Black It.

Tue, 30 Sep 2025 00:00:00 +0000

As developers, we’ve all been there: spending precious minutes debating whether to use single or double quotes, arguing over line breaks in code reviews, or reformatting entire files to match team standards.

What if I told you there’s a tool that eliminates these discussions entirely and gives you back hours each week? And the bonus part - it’s free and easy to use.

The Time-Saving Reality

Let me break down where Black saves you real time:

How to Deploy a pytest Testing Project Using Cookiecutter

Mon, 08 Sep 2025 00:00:00 +0000

Introduction

Cookiecutter is a powerful command-line utility that creates projects from templates, making it an excellent tool for standardizing and deploying pytest-based automation projects. Whether you’re setting up test automation for web applications, APIs, or mobile apps, Cookiecutter can help you bootstrap projects quickly while maintaining consistency across your organization.

In this article, we’ll explore how to create and deploy a comprehensive pytest automation project template using Cookiecutter, covering everything from basic setup to advanced deployment strategies.

MCP Made Simple (Even for Testing!) Using FastMCP

Sun, 24 Aug 2025 00:00:00 +0000

I’m not going to explain what MCP is - most of you are already onboarded to the hype. Instead I’m going to talk about FastMCP, a framework that makes building Model Context Protocol (MCP) servers easy and fast.

What is MCP (quick recap)?

The Model Context Protocol lets AI models connect to external tools and data. Instead of building complex integrations from scratch, you create an MCP server that exposes your functionality in a standard way.

Kickstarting AI-Enhanced Automation Infrastructure: A Practical Guide Using Claude Code

Wed, 30 Jul 2025 00:00:00 +0000

Imagine you’re a software developer - or better yet, an automation infrastructure engineer - tasked with building a test framework from scratch. Sounds exciting, right? Well… that depends on how well you execute it.

There are countless considerations when designing a resilient automation infrastructure. But the real secret? Making it extensible with minimal effort.

In this short guide, I’ll walk you through a method I’ve designed to streamline the design and implementation process using AI as a development partner.

Soft Asserting With pytest Made Fun

Thu, 10 Jul 2025 00:00:00 +0000

As Python developers or automation engineers, we’ve all been there. You’re running a comprehensive test suite (unit, component, or end-to-end), and one assertion fails early in a test function. The test stops executing, and you’re left wondering about the other potential failures that might exist further down in the same test. You fix that one assertion, re-run the test, and discover another failure. Fix-run-test-fail-repeat, isn’t it?

Meet pytest-check

Unlike traditional pytest assertions that halt execution at the first failure, pytest-check allows you to continue executing tests even after assertions fail. This means you can collect multiple failures in a single test run, dramatically improving your debugging efficiency and ability to explore multiple failure areas.

The Open Source Coding Companion: Harnessing OLLaMA 3 With PyCharm and Continue

Sun, 12 May 2024 00:00:00 +0000

Introduction

In the ever-evolving landscape of artificial intelligence and natural language processing, open-source models have emerged as powerful tools for developers and researchers alike. Among these, the OLLaMA 3 model stands out for its impressive capabilities and accessibility.

In this article, we’ll explore how to leverage OLLaMA 3 as an offline model and seamlessly integrate it with PyCharm using the Continue plugin, unlocking a world of possibilities for enhanced coding productivity.

Reusing Core Functions for Locust and pytest: A Path to Efficient Testing

Thu, 02 May 2024 00:00:00 +0000

In the world of software development, testing plays a crucial role in ensuring the quality and reliability of applications. Two widely used testing frameworks, Locust and pytest, have gained popularity due to their versatility and ease of use. However, as projects grow in complexity, it becomes essential to adopt practices that promote code reusability and maintainability.

This article explores how to leverage the power of reusable functions across Locust and pytest, streamlining the testing process and improving overall efficiency.

Effective Unit Testing With Hypothesis and pytest

Mon, 15 Apr 2024 00:00:00 +0000

Unit testing is a crucial aspect of software development, ensuring that individual components of a system work as expected. While traditional unit testing involves manually crafting test cases, tools like Hypothesis and pytest can significantly streamline and enhance the testing process.

Introduction to Hypothesis

Hypothesis is a powerful library for property-based testing in Python. Unlike traditional unit testing, where you explicitly define test cases, Hypothesis lets you specify the properties or invariants your code should satisfy. It then automatically generates a diverse set of test cases to validate those properties.

Sweet Debugging With IceCream for Python

Sat, 06 Apr 2024 00:00:00 +0000

We’ve all been there - staring at lines of Python code, trying to figure out why our script isn’t behaving as expected. Debugging is a crucial skill for any programmer, but printing variables (e.g. print()) all over the place can quickly become messy and cumbersome.

Enter IceCream - a sweet little debugging library that makes logging variables and expressions a piece of cake.

IceCream is inspired by the popular debugging approach in JavaScript where you can sprinkle console.log() statements throughout your code. Similarly, with IceCream you simply import the library and use the ic() function to log variables, data structures, and even complex objects with a single line.

Elevate Your GitHub Profile to Stand Out

Tue, 02 Apr 2024 00:00:00 +0000

Your GitHub profile is your virtual resume, showcasing your coding skills, projects, and contributions to the open-source community. By optimizing your profile, you can attract potential employers, collaborators, and peers, ultimately advancing your career in the tech industry.

Here’s how you can enhance your GitHub profile to leave a lasting impression.

Complete your profile information

Provide a comprehensive overview of your skills, experiences, and interests. A well-written bio and a professional profile picture make a significant difference in how others perceive you. Add your location, website, and social media links to increase visibility and connectivity.

Extending pytest With Hooks and Plugins

Wed, 27 Mar 2024 00:00:00 +0000

It’s no secret that I believe pytest is one of the most powerful Python-based test frameworks. One of its great features is the ability to be extended through hooks and plugins. Hooks let you tap into specific points during the testing process, while plugins provide a way to bundle hooks and other pytest extensions into a single distributable package.

Utilizing hooks

Hooks are a way to extend or modify pytest’s behavior without modifying the core pytest code. pytest provides numerous hooks that you can define and implement in your tests. Here’s an example of using the pytest_runtest_setup hook to perform an action before each test runs:

Streamlining Asynchronous System Testing With Flask and pytest

Sun, 24 Mar 2024 00:00:00 +0000

For developers and QE engineers dealing with asynchronous systems, testing can be a significant challenge. These systems often respond with only a request ID, making it difficult to verify the actual response data. However, by leveraging the power of Flask and pytest, we can create a robust testing framework that simplifies the process.

The asynchronous system challenge

In an asynchronous system, when a request is sent, the system typically responds with a request ID rather than the actual data. This ID is then used to query the system for the result at a later time. This behavior can make testing difficult, as the traditional request-response cycle is disrupted.

pytest Parametrization: Injecting Data Into Tests

Wed, 20 Mar 2024 00:00:00 +0000

As already mentioned in a previous article, pytest is a powerful testing framework for Python that offers a wide range of features, including parametrization. Parametrization allows you to run the same test with different sets of input data, making it easier to test multiple scenarios without duplicating code.

Parameterizing tests

pytest provides the @pytest.mark.parametrize decorator to parameterize tests. This decorator takes two arguments: the name of the parameter(s) and a list of input data. Here’s a simple example:

Simplifying API Testing With pytest and Requests

Tue, 19 Mar 2024 00:00:00 +0000

As software developers, we often work with APIs, whether consuming third-party services or building our own. Ensuring our API interactions are functioning correctly is crucial, which is where testing comes into play. In this article, we’ll explore how to leverage pytest, a powerful Python testing framework, along with the requests library, to streamline API testing.

What is pytest?

pytest is a robust, feature-rich testing framework for Python that simplifies writing, organizing, and running tests. It offers a simple and intuitive syntax, making it easy to get started. Additionally, pytest supports a wide range of plugins that extend its functionality, enabling developers to tailor the testing experience to their specific needs.

Open Source Projects

Mon, 01 Jan 0001 00:00:00 +0000

A live list of my public GitHub repositories, sorted by most recently updated.

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Privacy Policy

Mon, 01 Jan 0001 00:00:00 +0000

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