[{"data":1,"prerenderedAt":789},["ShallowReactive",2],{"/en-us/blog/custom-rules-duo-agentic-chat-deep-dive":3,"navigation-en-us":35,"banner-en-us":435,"footer-en-us":445,"blog-post-authors-en-us-Michael Friedrich":685,"blog-related-posts-en-us-custom-rules-duo-agentic-chat-deep-dive":699,"assessment-promotions-en-us":740,"next-steps-en-us":779},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":30,"tagSlugs":31,"__hash__":34},"blogPosts/en-us/blog/custom-rules-duo-agentic-chat-deep-dive.yml","Custom Rules Duo Agentic Chat Deep Dive",[7],"michael-friedrich",null,"engineering",{"slug":11,"featured":12,"template":13},"custom-rules-duo-agentic-chat-deep-dive",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"category":9,"tags":21,"body":24},"Custom rules in GitLab Duo Agentic Chat for greater developer efficiency","Discover how AI can understand your codebase, follow your conventions, and generate production-ready code with minimal review cycles.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-08-12",[22,23],"DevSecOps","AI/ML","Transform GitLab Duo from a generic AI assistant into your team's personalized coding expert with custom rules. Stop constantly correcting AI suggestions that use wrong Java versions, incorrect Python binaries, or violate your style guides. This deep-dive shows you how to create intelligent custom rules that automatically enforce your development standards.\n\nWe'll cover:\n\n- Version control: Lock AI to Java 8, handle Python3 environments, and generate multi-platform C++ code\n- Style enforcement: Prevent C `goto` anti-patterns, enforce VueJS design patterns, and ensure Ansible linter compliance\n- DevSecOps automation: Bootstrap projects with proper CI/CD security scanning and documentation standards\n\nEach example includes working GitLab projects to fork, complete configurations, and before/after demonstrations. Learn how banking systems stay Java 8 compliant, IoT collectors work cross-platform, and VueJS components follow GitLab's production standards.\n\n## Table of Contents\n\n- [First steps with custom rules for Duo Agentic Chat](#first-steps-with-custom-rules-for-duo-agentic-chat)\n  - [Requirements](#requirements)\n  - [Quick start: 5-minute success](#quick-start-5-minute-success)\n  - [Guidelines for custom rule development](#guidelines-for-custom-rule-development)\n  - [Ask GitLab Duo Chat about existing development style guides](#ask-gitlab-duo-chat-about-existing-development-style-guides)\n- [More custom rules use cases](#more-custom-rules-use-cases)\n  - [Use cases: Version and platform support](#use-cases-version-and-platform-support)\n    - [Java version requirements](#java-version-requirements)\n    - [C++ Multi-platform support (Windows, Linux, macOS)](#c-multi-platform-support-windows-linux-macos)\n  - [Use case: Development environments](#use-case-development-environments)\n    - [Python 3 development environment](#python-3-development-environment)\n    - [Ansible linter compliance](#ansible-linter-compliance)\n  - [Use case: Design patterns](#use-case-design-patterns)\n    - [Avoid anti-patterns with C and goto statements](#avoid-anti-patterns-with-c-and-goto-statements)\n    - [Frontend style guides for VueJS 3](#frontend-style-guides-for-vuejs-3)\n  - [Use case: DevSecOps workflows](#use-case-devsecops-workflows)\n    - [Issue and MR templates](#issue-and-mr-templates)\n    - [Build tools](#build-tools)\n    - [CI/CD configuration preferences](#cicd-configuration-preferences)\n    - [Security scanning preferences](#security-scanning-preferences)\n    - [Tests and linters](#tests-and-linters)\n    - [Documentation generation](#documentation-generation)\n    - [Refactoring and code change requirements](#refactoring-and-code-change-requirements)\n    - [Onboarding, requirements, licenses](#onboarding-requirements-licenses)\n    - [Git flows](#git-flows)\n- [Distribution and testing of custom rules](#distribution-and-testing-of-custom-rules)\n  - [Control custom rules editing](#control-custom-rules-editing)\n  - [Custom rules resources](#custom-rules-resources)\n- [Fun activity: Explore behavior changes](#fun-activity-explore-behavior-changes)\n- [Conclusion](#conclusion)\n\n\n## First steps with custom rules for Duo Agentic Chat\n\nFollow the [documentation](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/#create-custom-rules) to create custom rules for GitLab Duo Agentic Chat in the `.gitlab/duo/chat-rules.md` directory in a new or existing GitLab project in your IDE.\n\nYou can start with free-form text instructions, and iterate on the best outcome. Custom rules support [Markdown](https://docs.gitlab.com/user/markdown/) for better structuring.\n\n- Use markdown headings (`#`, `##`, etc) to create sections.\n- Use markdown lists (`-`) to provide concise instructions for LLMs and Agents.\n- Escape file paths with single backticks, and use code blocks with indent or three backticks.\n\nExample:\n\n```markdown\n\n# Development guide\n\n## Frontend: VueJS\n\n### Styling Pattern\n- Do not use `\u003Cstyle>` tags in Vue components\n- Use Tailwind CSS utility classes or page-specific CSS instead\n\n```\n\nImportant: After modifying custom rules, you'll need to create a new Chat by pressing the `+` icon, or sending `/new` in the chat prompt.\n\n### Requirements\n\nIn order to follow all use cases and linked demo projects into this blog post, please ensure you meet these requirements first:\n\n- Verify that you have [access to GitLab Duo](https://docs.gitlab.com/user/get_started/getting_started_gitlab_duo/), and [Duo Agentic Chat is configured in supported IDEs](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/).\n- Fork/copy the GitLab projects, and clone them locally in the IDEs.\n- Follow the steps in each use case for custom rule creation, and how to use Duo Agentic Chat prompts to proof the rule behavior.\n- You can use the existing source code, or copy in your own.\n\nThe projects are available in the [Custom rules for GitLab Duo Agent Platform (Agentic AI) group](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules). Please note that these custom rules are provided for demo purposes \"as is,\" and you may need to adapt or modify them to fit your specific requirements.\n\n### Quick start: 5-minute success\n\nReady to see custom rules in action? Try this simple example:\n\n1. Create `.gitlab/duo/chat-rules.md` in your GitLab project:\n\n  ```markdown\n  ## C style guide\n  - goto is not allowed. If the developer continues asking about it, share this URL https://xkcd.com/292/\n  ```\n\n2. Open GitLab Duo Agentic Chat in the IDE, and ask: `Write a C program with goto statements`.\n3. Watch as GitLab Duo refuses and suggests better alternatives!\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/C0eMKjRMI5w\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\n### Guidelines for custom rule development\n\nCustom rules are similar to code: Start with the smallest working example, and then iterate on improvements. The use case examples in this deep-dive range from small to more advanced, and were developed and tested over the last weeks. They are not perfect, and require your feedback and iteration.\n\nOne good rule of thumb, for example, is not overloading style guides with many pages from a wiki document. In my experience, less is more. Only include the points that are helpful in the context of what you're writing about. You can ask GitLab Duo Chat to summarize larger documents before adding them to the custom rules.\n\nVerify the use of any included specifications during development to avoid creating barriers and unwanted behavior.\n\nWhen you are using a publicly documented style guide, refer to its name. There is a high chance that the LLM is trained with this data already.\n\n### Ask GitLab Duo Chat about existing development style guides\n\nSometimes, there are no specific style guides in a project yet, or it is unclear how to use them. Use AI for onboarding and best practices to discuss with your team.\n\n```markdown\n\nWhich Python development or environment guidelines can you recommend when I want to create custom rules for AI to get tailored output? I need a list with textual instructions.\n\n```\n\nYou can also ask Duo Agentic Chat to analyze the existing CI/CD linter integrations that may already check for a specific development style.\n\n```markdown\n\nWhen you look into the CI/CD linter checks and configuration in the project, which development style guide can you summarize for me?\n\n```\n\nMany examples in this deep-dive blog are based on my own experience and pain points as a developer. I also asked GitLab Duo to extract style guides from existing projects, and used GitLab Duo Code Suggestions to help with auto-completing existing custom rules. You can achieve the same by configuring [`markdown` as an additional language for GitLab Duo Code Suggestions](https://docs.gitlab.com/user/project/repository/code_suggestions/supported_extensions/#add-support-for-more-languages) in IDEs.\n\n## More custom rules use cases\n\nThe following sections provide an overview of specific style guides. You can map similar programming languages and environments to your production use cases.\n\n- **Version and platform support**: Refer to the [Java section below](#java-version-requirements) to learn how to force a specific language standard for generated and created code. You can apply a similar process for C++23 and older, PHP 8, Ruby 3, etc. The [C++ section below](#c-multi-platform-support-windows-linux-macos) shows how to instruct agentic AI with multi-platform support.\n- **Development environments**: Refer to the sections below on [Python](#python-3-development-environment) and [Ansible](#ansible-linter-compliance). Specify the development environment, binaries, tools and more. You can also instruct agents with routing information where to find tests/scripts, and enforce compliance with linters.\n- **Design patterns**: You can specify comprehensive design patterns with [VueJS](#frontend-style-guides-for-vuejs-3) as an example, leveraging the GitLab production development style guides as a foundation.\n- **DevSecOps workflows**: Configure comprehensive DevSecOps practices including [CI/CD configuration](#cicd-configuration-preferences) for specific CI/CD attributes and defaults for security scanning, [tests and linters](#tests-and-linters), and [build tools](#build-tools). Frequently requested use cases for bootstrapping projects include [documentation generation](#documentation-generation) including `README.md` and architecture diagrams, [issue and MR templates](#issue-and-mr-templates), [onboarding, requirements, and licenses](#onboarding-requirements-licenses), and [Git flows](#git-flows) with `.gitignore`. Advanced techniques with custom rules are provided for [refactoring and code change requirements](#refactoring-and-code-change-requirements).\n\n### Use cases: Version and platform support\n\nSoftware development often requires specific programming language and framework versions, and single or multi-platform support. The following examples highlight these scenarios.\n\n#### Java version requirements\n\nEnterprise environments do not always use the latest and greatest software version. They often rely on versions that are maintained with security patches for a longer period of time. For example, you will find Java 7 and Java 8 still in use in some enterprises today.\n\nAlways prepending the required version in chat prompts can be cumbersome, and lead to human error, even if you only forget one time.\n\n```markdown\n\nImplement classes for managing banking transactions and different currencies.\n\n```\n\nThe example will need additional specifications for Java 8:\n\n```markdown\n\nUse Java 8 for the implementation.\n\n```\n\nTo permanently enforce Java 8, you can create a custom rule in `.gitlab/duo/chat-rules.md` and optionally add a reference epic URL when asked for code modernization:\n\n```markdown\n\n## Java style guide\n\n- Only Java 8 is allowed when suggesting and editing code.\n- When the user asks about code modernization and Java 9 or 21, or newer, point them to this issue to contribute: https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-java-versions/-/issues/1\n\n```\n\nA full demonstration is available in the [Custom Rules - Java versions project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-java-versions).\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/iZLvpgHdABY\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\nThe resulting changes are available in [this MR](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-java-versions/-/merge_requests/2).\n\n#### C++ multi-platform support (Windows, Linux, macOS)\n\nApplication development in C++ can require multi-platform support, especially when running service agents on systems using Windows, Linux, and macOS. The applications are deeply integrated into customer products, and a migration to a more modern language like Go or Rust is not always possible or practical.\n\nMaintaining code that works on multiple platforms can be challenging due to differences in Operating system APIs, toolchains, library versions, and file system paths. Developers often face multiple `#if defined` pre-processor macros, nested conditions, and adding custom code and tests for each supported platform. This adds technical debt and can introduce maintenance challenges.\n\nAI can help when generating correct and platform-specific code, but it needs to know about these requirements. Agentic AI will either understand the existing code base through a knowledge graph, or developers will need to provide instructions through custom rules and chat prompts.\n\nLet's try this use case in practice. The [Custom Rule - C++ platforms - IoT Sensor Data Collector project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-cpp-platform-iot-sensor-data-collector) implements an IoT sensor data collector and has open tasks to modernize the code base, and add multi-platform support for Linux, Windows, and macOS. You can fork the project and clone it locally.\n\nOpen the `.gitlab/duo/chat-rules.md` file and review or add the following custom rules:\n\n```markdown\n\n## C++ style guide\n\n- The application runs on Linux, macOS and Windows. Generate code that handles the OS API differences.\n- Use pre-processor macros for Windows and POSIX conventions for Unix (Linux, macOS).\n\n## CI/CD Configuration\n\n- Ensure that GitLab CI/CD jobs cover the different platform support. Use CI/CD job templates with extends where applicable.\n\n```\n\nStart a new Chat, and ask to restructure code for multi-platform support.\n\n```markdown\n\nPlease help me restructure the code and ensure multi-platform support.\n\n```\n\nYou can also refer to the issue number, or URL ([issue 3](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-cpp-platform-iot-sensor-data-collector/-/issues/3)). GitLab Duo will automatically fetch the issue content from the GitLab platform, and put it into AI context.\n\n```markdown\n\nPlease help me implement issue 3\n\nPlease help me implement https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-cpp-platform-iot-sensor-data-collector/-/issues/3\n\n```\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/C5NxOjB0R1Q\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\n### Use case: Development environments\n\nDevelopment environments often vary between operating systems and developers. This can be confusing for AI models generating code or suggesting changes. The following use cases illustrate these environment problems and their solutions with custom rules.\n\n#### Python 3 development environment\n\nA Python development environment usually comes with the `python` executable and `pip` package manager. However, on systems like MacOS or Ubuntu, you need to use `python3` and `pip3` to get access to more recent Python 3 versions. This can create confusion running Python scripts, creating virtual environments, and installing package dependencies.\n\nFor this custom rules use case, I installed Python using [Homebrew](https://brew.sh/) which results in a binary executable called `python3` and a package manager `pip3`.\n\nAs an example, set up a Python virtual environment, install dependencies using `pip`, and run the application:\n\n```shell\n\npython -m venv myenv\nsource myenv/bin/activate\n\npip install -r requirements.txt\n\npython script.py\n\n```\n\nThis doesn't work as expected because we need to use specific binaries with version `3`:\n\n```shell\n\npython3 -m venv myenv\nsource myenv/bin/activate\n\npip3 install -r requirements.txt\n\npython3 script.py\n\n```\n\nWe can test this problem with Agentic Chat for both, suggested code blocks, and the approval request for commands to execute. The [Custom Rule - Python3 Env Shop app project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-python-3-env-shop-app) implements a web shop application in Python, and provides the default Python executable paths in its `README.md` file which typically gets added into Agentic Chat context.\n\n![Duo Agentic Chat, proposing the wrong binary commands](https://res.cloudinary.com/about-gitlab-com/image/upload/v1769123901/c0jltxbgxi2suobxhwwq.png)\n\nIn order to overcome the problem, review `.gitlab/duo/chat-rules.md` which contains the following custom rules to enforce the Python executable names.\n\n```markdown\n\n## Python style guide\n\n- For Python binaries, always use python3 and pip3 when suggesting or running shell commands.\n- Detect the Python environment automatically when possible.\n\n```\n\nYou can also instruct agents with pre-defined routes to gather additional information through tool calling and/or MCP, when they do not attempt this automatically already.\n\nOpen a new Agentic Chat, and ask `How to run this application?` to see custom rules in action, using `python3` and `pip3` as desired.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/UQ2_OCvUmF0\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\n\nThe full source code is available in the [Custom Rule - Python3 Env Shop app project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-python-3-env-shop-app).\n\n#### Ansible linter compliance\n\nModern Ansible for Infrastructure-as-Code tasks enforces a strict style guide, which can be verified using `ansible-lint`: It detects when Boolean values (`true`/`false`) are required instead of strings (`yes`/`no`), builtin module actions requiring the FQCN (Fully Qualified Collection Name) as parameter names, and trailing whitespaces that need trimming. CLI and IDE integrations, such as the [VS Code Ansible extension by Red Hat](https://developers.redhat.com/learning/learn:ansible:get-started-ansible-visual-studio-code-extension/resource/resources:install-and-configure-ansible-extension-visual-studio-code) help visualize these errors to developers. LLMs and chat agents might not always generate correct Ansible code and need manual work to fix it.\n\nLet's look at the problem with a concrete use case. The following example implements a basic Ansible playbook in the [Custom Rule - Ansible Environment project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-ansible-environment) to set up a GitLab server on Ubuntu. You'll notice the Boolean values are incorrectly typed as strings (`yes`/`no`), and additional problem reports for builtin module actions and whitespace trimming.\n\n![VS Code with Ansible lint error: Wrong boolean type](https://res.cloudinary.com/about-gitlab-com/image/upload/v1769123900/eofxonbvbs7o2qt48tmc.png)\n\n![VS Code with Ansible lint error: Builtin module action FQCN](https://res.cloudinary.com/about-gitlab-com/image/upload/v1769123900/qnzwk8b1dslmcqercxul.png)\n\n![VS Code with Ansible lint error: trailing whitespaces](https://res.cloudinary.com/about-gitlab-com/image/upload/v1769123900/fsrceusoksp16xj42yix.png)\n\nLet's see how we can create custom rules to help Duo Agents fix the Ansible linter errors, and prevent them from happening in the future.\n\nFork and clone the [Custom Rule - Ansible Environment project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-ansible-environment) and open `.gitlab/duo/chat-rules.md` in the IDE inspect the custom rules:\n\n```markdown\n\n## Ansible styleguide\n\n- Boolean values in Ansible should be typed as \"true\" or \"false\" and never as string.\n- Ansible module builtin actions must use the FQCN (Fully Qualified Collection Name).\n- Always trim whitespaces in Ansible YAML.\n\n```\n\nOpen a new GitLab Duo Agentic Chat prompt, and ask Duo Agent for the same Ansible playbook:\n\n```markdown\n\nPlease help me fix the Ansible linter errors\n\n```\n\nThe Agents will analyze the repository, ask to run `ansible-lint` commands, and investigate how to fix the problems followed the defined custom rules.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/P465U8IfScE\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\nYou can inspect the custom rules and Ansible code changes in [this MR](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-ansible-environment/-/merge_requests/1) in the [Custom Rule - Ansible Environment project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-ansible-environment).\n\nAsync exercise: Start a new project where custom rules are configured as default already, and verify the correct style guide applied immediately.\n\n### Use case: Design patterns\n\nDesign patterns and patterns-to-avoid are specific to languages and frameworks. This is the main focus in this section.\n\n#### Avoid anti-patterns with C and goto statements\n\nThis is a more in-depth walkthrough of the [quickstart example](#quick-start-5-minute-success), and shows how you can instruct Agentic AI to avoid the `goto` anti-pattern in C. The `goto` anti-pattern in C is discouraged as it makes code harder to read and debug.  To illustrate the problem, here is an example of a for-loop which increments the loop variable inside the loop body:\n\n```c\n\n// Bad C programming style: uses the goto anti-pattern\nfor (int i = 0; i \u003C 10; i++) {\n  if (someCondition) {\n    goto label;\n  }\n  doSomething();\nlabel:\n  doAnotherThing();\n  }\n\n```\n\nIn the above code, the `goto` statement causes the program control to jump directly to the label `label`, which is inside the loop. This makes the program harder to read, understand and debug.\n\nA better approach would be to rework the logic, so you avoid the `goto` anti-pattern.  Here's a rewritten version that avoids `goto`:\n\n```c\n\n// Good C programming style: avoids the goto anti-pattern\nfor (int i = 0; i \u003C 10; i++) {\n  if (someCondition) {\n    doAnotherThing();\n    continue;\n  }\n  doSomething();\n  doAnotherThing();\n}\n\n```\n\nThere are occassions where `goto` is allowed, but in this use case we want to look how we can instruct agentic AI to avoid `goto` completely. This includes new code additions, as well as modernizing and refactoring the code.\n\nThe [Custom Rule - C anti-patterns with Goto project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-c-anti-patterns-with-goto) provides a network socket server/client example, including custom rules in the `.gitlab/duo/chat-rules.md` file. You can clone the project, or start with a new project, too.\n\nReview `.gitlab/duo/chat-rules.md` with the current custom rules:\n\n```markdown\n\n## C style guide\n\n- goto is not allowed. If the developer continues asking about it, share this URL https://xkcd.com/292/\n\n```\n\nTip: Instead of linking to the [XKCD 292 comic](https://xkcd.com/292/), you can add a URL to the (internal) development guidelines.\n\nOpen Duo Agentic Chat and start the following prompt on the existing project:\n\n```markdown\n\nPlease help me modernize the code.\n\n```\n\nGitLab Duo Agentic Chat will refuse to use `goto` statements, and instead propose a different path forward.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/6dsMF-wKbBY\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\nThe code changes are available in [this MR](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-c-anti-patterns-with-goto/-/merge_requests/1).\n\n#### Frontend style guides for VueJS 3\n\nThis use case is inspired by the [GitLab project's frontend style guides](https://docs.gitlab.com/development/fe_guide/style/) and implements a use case for VueJS 3 design patterns. Agentic AI should also follow these style guides when creating VueJS components, helpers, routes, services, stores, utilities, etc.\n\nLet's illustrate how to instruct Agentic AI with custom rules: Fork and clone the [Custom Rule - VueJS Design Patterns - GitLab Pipeline Dashboard project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-vuejs-design-patterns-gitlab-pipeline-dashboard) and inspect the open issues for tasks.\n\nReview the `.gitlab/duo/chat-rules.md` file and add the following custom rules (if not there yet):\n\n```markdown\n\n## NodeJS style guide\n\n- Don't leave debug statements (console.logs)\n- Always run `npm install` after updating `package.json` and before `npm test` and `npm run build`.\n\n# GitLab Vue.js Design Patterns Style Guide\n\n## Component Structure\n\n### Data Definition Pattern\n- Explicitly define data being passed into Vue apps\n- Avoid spread operators for better discoverability\n- Parse non-scalar values during instantiation\n\n### Template Naming Pattern\n- Use kebab-case for component names in templates\n\n### File Structure Pattern\n- Use `.vue` files for Vue templates\n- Do not use `%template` in HAML\n\n### Styling Pattern\n- Do not use `\u003Cstyle>` tags in Vue components\n- Use Tailwind CSS utility classes or page-specific CSS instead\n\n[...]\n\n```\nThe full custom rules are available in the [`.gitlab/duo/chat-rules.md` file](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-vuejs-design-patterns-gitlab-pipeline-dashboard/-/blob/main/.gitlab/duo/chat-rules.md?ref_type=heads&plain=1).\n\nNext, open GitLab Duo Agentic Chat and ask how to [add new pipeline mini charts](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-vuejs-design-patterns-gitlab-pipeline-dashboard/-/issues/6), or other tasks you come across. Tip: You can reference only the issue, or alternatively paste the full issue URL, and Agentic Chat will look up both and extract the title and description for the current context.\n\n```markdown\n\nPlease help me implement issue 6\n\n```\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/KbczS-OVb90\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\nThe resulting code changes are available in [this MR](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-vuejs-design-patterns-gitlab-pipeline-dashboard/-/merge_requests/3).\n\nNote: The VueJS style guide was extracted from the [gitlab-org/gitlab project](https://gitlab.com/gitlab-org/gitlab) asking GitLab Duo Agentic Chat with the following prompt sequence:\n\n```markdown\n\nWhat is the development style guide for VueJS?\n\nCan you print the styleguide as Markdown formatted list with headings.\n\nCreate a file in the repo, and only print the style guide rules there, no codeblocks.\n\n```\n\n![IntelliJ IDEA with `gitlab-org/gitlab` source code, and GitLab Duo Agentic Chat writing the styleguide file](https://res.cloudinary.com/about-gitlab-com/image/upload/v1769123901/prf63nqoqsqzn7lh9pnq.png)\n\n### Use case: DevSecOps workflows\n\nDevSecOps workflows range from best practices for bootstrapping a project with issue/MR templates, `.gitignore`, GitLab CI/CD configuration, `README.md` documentation, licenses and much more. The following section explores a variety of use cases. You can use them as inspiration for your own custom rules.\n\nCommon DevSecOps automation with custom rules:\n\n- **Project bootstrap**: Auto-create README, .gitignore, CI/CD config\n- **Security defaults**: Enforce SAST, dependency scanning, secrets detection\n- **Documentation**: Generate issue/MR templates, architecture diagrams\n\nA combined use case example is available in the [Custom Rule - DevSecOps workflows - Git README build tools issue MR templates project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules/custom-rule-devsecops-workflows). You can inspect the custom rules, and fork/clone it locally to ask Agentic Chat with a new prompt such as: `I need to bootstrap this project. Please help me with that`.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe width=\"560\" height=\"315\"\n  src=\"https://www.youtube.com/embed/hKpLcBtbC4g\" frameborder=\"0\"\n  allowfullscreen=\"true\">\n  \u003C/iframe>\n\u003C/figure>\n\nThe next sections provide detailed prompts, and most of them are shown in the recording, too.\n\n#### Issue and MR templates\n\nYou can instruct agentic AI to create issue/MR templates when they are missing, and offer to add project-specific information or labels. Agents will automatically query the GitLab API in the background, and put the current project structure into a template.\n\n```markdown\n\n## Issue and MR templates\n\n- If no issue templates for `Default` and `Feature Proposal` exist in .gitlab/issue_templates, create them using the following raw template sources:\n\n        Default: https://gitlab.com/gitlab-org/gitlab-vscode-extension/-/raw/main/.gitlab/issue_templates/Default.md\n        Feature Proposal: https://gitlab.com/gitlab-org/gitlab-vscode-extension/-/raw/main/.gitlab/issue_templates/Feature%20Proposal.md\n\n- If no default MR template `Default` exists in `.gitlab/merge_request_templates`, create them using the following raw template sources:\n\n        Default: https://gitlab.com/gitlab-org/gitlab-vscode-extension/-/raw/main/.gitlab/merge_request_templates/Default.md\n\n- Update the project URLs, and available labels in the fetched templates accordingly, or remove anything unknown and let the user know about TODOs.\n- Create a test issue/MR, when bootstrapping a new project.\n\n```\n\n#### Build tools\n\nThere is a variety of build tools, package managers, compilers, container builders available per programming language. When asking about updates and dependencies, agentic AI can use these default tools without asking for input.\n\n```markdown\n\n## Build tools\n\n- Always use a virtual env with Python, and set it up before executing any Python commands\n- For C/C++: Prefer CMake, and gcc on Linux, clang on macOS, MSVC on Windows.\n- For Python: Always use pip\n- For Java: Always use Gradle\n- For Node.js, suggest to use npm/yarn.\n- For Rust: Always use cargo\n- For Go: Always use go.mod\n- For Ruby: Always use Bundler\n- For PHP: Always use Composer\n- For .NET: Always use .NET CLI\n- For Scala: Always use SBT\n- For Elixir: Always use Mix\n- For Haskell: Always use Cabal\n- For Swift: Always use Swift Package Manager\n- For Kotlin: Always use Gradle or Maven.\n- For TypeScript: Always use npm or yarn.\n- Always suggest using a package manager or build tool based on the main programming language of the project.\n- When asking for dependencies, assume that the user wants to update all current dependencies to the latest version available.\n\n- Always suggest to create a Dockerfile if there isn't one. Always use a minimal image and use a tag for security scanning.\n- Always add a `Dockerfile` with the base image and the entrypoint if one does not exist.\n- Always include a `.dockerignore` and use it in the Docker build process.\n\n```\n\n#### CI/CD configuration preferences\n\nUse rules to prefer specific container images, variable and job name patterns, etc.\n\n```markdown\n\n## CI/CD Configuration\n\n- If no GitLab CI/CD configuration exists in `.gitlab-ci.yml`, ask the user for approval to create.\n- Create a GitLab CI/CD configuration automatically when a new project gets bootstrapped\n\n- Always use alpine as container image to build the application.\n- Add caching for detected programming languages and frameworks.\n\n```\n\n#### Security scanning preferences\n\nSecurity scanners can also be enforced in GitLab CI/CD configuration. The following example instructs agents to always include Advanced SAST, dependency scanning, and secret detection templates. It has been successfully tested across other use cases in this tutorial.\n\n```markdown\n\n## Security scanning\n\n- Always use Advanced SAST.\n- Always include SAST, Dependency Scanning, Secrets Detection templates, similar to the following format:\n\n    include:\n        - template: Jobs/SAST.gitlab-ci.yml\n        - template: Jobs/Secret-Detection.gitlab-ci.yml\n        - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n\n    variables:\n        GITLAB_ADVANCED_SAST_ENABLED: 'true'\n\n```\n\n![VS Code with a Java app, GitLab CI/CD, custom rules, and Agentic Chat adding Advanced SAST](https://res.cloudinary.com/about-gitlab-com/image/upload/v1769123900/avokij2uelhkx711wp1q.png)\n\n#### Tests and linters\n\nYou can also directly instruct Agentic Chat where to find the tests, and how to run them. The same idea applies to calling linter commands. Thanks Jessie Young for sharing this neat tip!\n\n```markdown\n\n## Tests and linting details\n\n- Tests in this project are located in __ directory and are run using the ___ command\n- Linting is done with the ___ command\n\n```\n\n#### Documentation generation\n\nBest of documentation custom rules.\n\n```markdown\n\n- If a README is missing, ask the user if they want to create one. If the user agrees, create a basic `README.md` for them.\n- When the user asks for an architecture proposal, always respond with generating an architecture diagram in Mermaid, and ask the user if they want you to add it to the README.md or another documentation file.\n- For documentation in Markdown, always use GitLab flavored Markdown.\n- Always add correct code block syntax highlighting support.\n\n```\n\n#### Refactoring and code change requirements\n\nWhen a project should gradually be modernized with newly generated code, and not result in large refactors, the following rules can be helpful.\n\n```markdown\n\n## Keep the changes minimal\n\n- The project uses \u003Cthis standard and version>. For newly generated code, use this standard.\n- Do not attempt to refactor code already created in a project to this standard, but for new code, always ensure this standard is used.\n- If unsure whether a file requires modification or refactoring, document this as a todo task.\n- Never fix detected problems, whitespaces, code formatting, unless the user instructs you specifically in a comment.\n- The code must be retained in its original format and only changes specific to solving the user request are allowed.\n\n## Summaries\n\n- List all items to address at the bottom in a summary section with TODO: followed by a textual description.\n- Identify 3 critical items, and ask the user if you should create GitLab issues from those items.\n\n```\n\n#### Onboarding, requirements, licenses\n\nAlways include a specific documentation link, and guidelines to follow in chat responses.\n\n```markdown\n\n## Link to guidelines\n- Always refer to our developer guidelines when answering questions. You can find those guidelines here: https://docs.gitlab.com/development/\n\n## Context and planning\n- Always start with finding existing issues with the desired topic. Only then propose new implementation work.\n\n## License\n- Always add the MIT license into `LICENSE` and use `GitLab B.V.` as copyright holder.\n\n```\n\n#### Git flows\n\nFollow a specific Git branching flow for suggestions and executed commands.\n\n```markdown\n\n## Git flows\n\n- Examine the project and involved development environment and programming languages. Always add a `.gitignore`.\n- Consider more best practices when bootstrapping a new project.\n- For existing projects, offer to add a `.gitignore` when missing, but only when asked about the state of the project, or what is missing.\n\nWhen a user requests to start with a new feature, always create a new branch, called \"feature/\u003Cshortname>\" and describe the behavior. Ask for the user's approval.\n\n```\n\n## Distribution and testing of custom rules\n\nYou can create [GitLab project templates](https://docs.gitlab.com/administration/custom_project_templates/) with well-tested custom rule prompts, and ensure that new projects always start with the best practices applied.\n\nSince LLMs and AI agents are not predictable, testing the expected outcome becomes more challenging. A golden rule for custom rules is that they are never perfect, and require iterations based on your team's feedback. This might be required especially when newer models and flows are introduced that change their behavior when responding to custom rules. It's recommended to keep checking on the generated output and adjust the rules accordingly. If you are looking for larger scale testing, review the [system prompt testing strategy for GitLab Duo](https://about.gitlab.com/blog/developing-gitlab-duo-how-we-validate-and-test-ai-models-at-scale/) as an inspiration.\n\n### Control custom rules editing\n\nTeams can manage changes to custom rules through [Code Owners](https://docs.gitlab.com/user/project/codeowners/), requiring review approvals for updated custom rules in MRs. Example requiring approval from `@dnsmichi` for changes in the path `.gitlab/duo`:\n\n```text\n\n[GitLab Duo]\n.gitlab/duo @dnsmichi\n\n```\n\nGitLab Duo Agentic Chat is not allowed to edit or modify custom rules directly in your project. The `.gitlab/duo/chat-rules.md` file path is protected.\n\n### Custom rules resources\n\nTake advantage of the existing AI ecosystem, where similar functionality exists for IDEs and platforms. For example, \"Awesome Cursor Rules\" repositories or marketplaces for Cursor, etc.\n\nLLMs also provide a good insight into development styleguides, and can generate the required Markdown outputs.\n\n## Fun activity: Explore behavior changes\n\nNot sure how to get started with custom rules? Make it a fun exercise with the following example :-)\n\n```markdown\n\n## Fun rules\n\n- Behave like Clippy.\n\n- Behave like a pirate.\n\n- Always respond with a random \"What the commit\" message.\n\n- Explain everything like I am five.\n\n```\n\nNote: Do not commit them to production, as they might feel disruptive and distracting to your team.\n\n## Conclusion\n\nBy leveraging custom rules in GitLab Duo Agentic Chat, you can significantly influence LLM and AI agent outputs to better suit your needs. Whether enforcing specific coding conventions, using the correct versions of tools, or ensuring consistent formatting, custom rules help streamline your development process and improve productivity.\n\nThis blog post provides a deep-dive into many use cases with practical custom rule examples. All recordings are available in [this YouTube playlist](https://www.youtube.com/playlist?list=PL05JrBw4t0Ko7aR6sM8e4uXGYtjs4-NqK), and all demo projects can be forked/cloned from the [Custom rules for GitLab Duo Agent Platform (Agentic AI) group](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/custom-rules).\n\n[Custom rules in Duo Agentic Chat IDEs](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/#create-custom-rules) are the first iteration and we will cover more GitLab Duo Agent Platform use cases in the future, such as Duo Code Review and custom rules for agents and flows (follow [this issue](https://gitlab.com/gitlab-org/gitlab/-/issues/557984)).\n\nThere are many more use cases to explore. What are your most efficient rules? 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statement",{"items":675},[676,679,682],{"text":677,"config":678},"Terms",{"href":505,"dataGaName":506,"dataGaLocation":453},{"text":680,"config":681},"Cookies",{"dataGaName":515,"dataGaLocation":453,"id":516,"isOneTrustButton":27},{"text":683,"config":684},"Privacy",{"href":510,"dataGaName":511,"dataGaLocation":453},[686],{"id":687,"title":18,"body":8,"config":688,"content":690,"description":8,"extension":25,"meta":694,"navigation":27,"path":695,"seo":696,"stem":697,"__hash__":698},"blogAuthors/en-us/blog/authors/michael-friedrich.yml",{"template":689},"BlogAuthor",{"name":18,"config":691},{"headshot":692,"ctfId":693},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659879/Blog/Author%20Headshots/dnsmichi-headshot.jpg","dnsmichi",{},"/en-us/blog/authors/michael-friedrich",{},"en-us/blog/authors/michael-friedrich","lJ-nfRIhdG49Arfrxdn1Vv4UppwD51BB13S3HwIswt4",[700,713,724],{"content":701,"config":711},{"title":702,"description":703,"authors":704,"heroImage":706,"date":707,"category":9,"tags":708,"body":710},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[705],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[257,607,709],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":712,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":714,"config":722},{"title":715,"description":716,"authors":717,"heroImage":19,"date":718,"category":9,"tags":719,"body":721},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[705],"2025-12-10",[607,257,720],"product","Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":723,"featured":27,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":725,"config":738},{"category":9,"tags":726,"body":729,"date":730,"updatedDate":731,"heroImage":732,"authors":733,"title":736,"description":737},[727,728,104],"tutorial","git","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n    subgraph Prerequisites\n        direction TB\n        A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n        A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n        B --> I[\"Users enablement and\u003Cbr/>change management\"]\n    end\n    \n    subgraph MigrationPhase[\"Migration phase\"]\n        direction TB\n        C[\"Migrate source code\"]\n        C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n        D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n    end\n    \n    subgraph PostMigration[\"Post-migration steps\"]\n        direction TB\n        F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n        F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n        G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n    end\n    \n    Prerequisites --> MigrationPhase\n    MigrationPhase --> PostMigration\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style I fill:#FC6D26\n    style C fill:#8C929D\n    style D fill:#8C929D\n    style E fill:#8C929D\n    style F fill:#FFA500\n    style G fill:#FFA500\n    style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n    subgraph GitLab\n        direction TB\n        A[\"Top-level Group\"]\n        B[\"Subgroup (optional)\"]\n        C[\"Projects\"]\n        A --> B\n        A --> C\n        B --> C\n    end\n\n    subgraph AzureDevOps[\"Azure DevOps\"]\n        direction TB\n        F[\"Organizations\"]\n        G[\"Projects\"]\n        H[\"Repositories\"]\n        F --> G\n        G --> H\n    end\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style C fill:#FC6D26\n    style F fill:#8C929D\n    style G fill:#8C929D\n    style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n  - main\n\nvariables:\n  imageName: myapp\n\nstages:\n  - stage: Build\n    jobs:\n      - job: Build\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Build Docker image\n            inputs:\n              command: build\n              repository: $(imageName)\n              Dockerfile: '**/Dockerfile'\n              tags: |\n                $(Build.BuildId)\n\n  - stage: Test\n    jobs:\n      - job: Test\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          # Example: run tests inside the container\n          - script: |\n              docker run --rm $(imageName):$(Build.BuildId) npm test\n            displayName: Run tests\n\n  - stage: Push\n    jobs:\n      - job: Push\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Login to ACR\n            inputs:\n              command: login\n              containerRegistry: '\u003Cyour-acr-service-connection>'\n\n          - task: Docker@2\n            displayName: Push image to ACR\n            inputs:\n              command: push\n              repository: $(imageName)\n              tags: |\n                $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n  imageName: myapp\n\nstages:\n  - build\n  - test\n  - push\n\nbuild:\n  stage: build\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n  only:\n    - main\n\ntest:\n  stage: test\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n  only:\n    - main\n\npush:\n  stage: push\n  image: docker:latest\n  services:\n    - docker:dind\n  before_script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n    - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n  only:\n    - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n  - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n  - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n  - Azure DevOps PAT with required scopes.\n\n  - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n  - Repos only: use GitLab's built-in import (Repo by URL)\n\n  - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n  - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n  - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n  - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n  - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n  - Enforce change freezes in ADO during each wave.\n\n  - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab                                                           | Azure DevOps                                 | Similarities & Key Differences                                                                                                                                          |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group                                                            | Organization                                 | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects.                                                   |\n| Group or Subgroup                                                | Project                                      | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects.                                                |\n| Project (includes a Git repo)                                    | Repository (inside a Project)                | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project.                                                  |\n| Merge Request (MR)                                               | Pull Request (PR)                            | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners.                                                                     |\n| Protected Branches, MR Approval Rules, Status Checks             | Branch Policies                              | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks.                                                                    |\n| GitLab CI/CD                                                     | Azure Pipelines                              | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml.                                                               |\n| .gitlab-ci.yml                                                   | azure-pipelines.yml                          | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers.                                                                   |\n| Runners (shared/specific)                                        | Agents / Agent Pools                         | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs.                                                         |\n| CI/CD Variables (project/group/instance), Protected/Masked       | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags.                                                                          |\n| Integrations, CI/CD Variables, Deploy Keys                       | Service Connections                          | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available.                                                                 |\n| Environments & Deployments (protected envs)                      | Environments (with approvals)                | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab.                                                                                 |\n| Releases (tag + notes)                                           | Releases (classic or pipelines)              | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately.                                                                               |\n| Job Artifacts                                                    | Pipeline Artifacts                           | Persist job outputs. Retention/expiry configured per job or project.                                                                                                  |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.)           | Azure Artifacts (NuGet/npm/Maven, etc.)      | Package hosting. Auth/namespace differ; migrate per package type.                                                                                                     |\n| GitLab Container Registry                                        | Azure Container Registry (ACR) or others     | OCI images. GitLab provides per-project/group registries.                                                                                                             |\n| Issue Boards                                                     | Boards                                       | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group.                                                                         |\n| Issues (types/labels), Epics                                     | Work Items (User Story/Bug/Task)             | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level.                                                                              |\n| Epics, Parent/Child Issues                                       | Epics/Features                               | Hierarchy of work. Schema differs; use epics + issue relationships.                                                                                                   |\n| Milestones and Iterations                                        | Iteration Paths                              | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group.                                                                                       |\n| Labels (scoped labels)                                           | Area Paths                                   | Categorization/ownership. Replace hierarchical areas with scoped labels.                                                                                              |\n| Project/Group Wiki                                               | Project Wiki                                 | Markdown wiki. Backed by repos in both; layout/auth differ slightly.                                                                                                  |\n| Test reports via CI, Requirements/Test Management, integrations  | Test Plans/Cases/Runs                        | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements.                                                                     |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions         | Control read/write/admin. Models differ; leverage group inheritance and protected resources.                                                                          |\n| Webhooks                                                         | Service Hooks                                | Event-driven integrations. Event names/payloads differ; reconfigure endpoints.                                                                                        |\n| Advanced Search                                                  | Code Search                                  | Full-text repo search. Self-managed GitLab may need Elasticsearch/OpenSearch for advanced features.                                                                   |\n","2025-12-03","2026-01-16","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658924/Blog/Hero%20Images/securitylifecycle-light.png",[734,735],"Evgeny Rudinsky","Michael Leopard","Guide: Migrate from Azure DevOps to GitLab","Learn how to carry out the full migration from Azure DevOps to GitLab using GitLab Professional Services migration tools — from planning and execution to post-migration follow-up tasks.",{"featured":27,"template":13,"slug":739},"migration-from-azure-devops-to-gitlab",{"promotions":741},[742,756,767],{"id":743,"categories":744,"header":746,"text":747,"button":748,"image":753},"ai-modernization",[745],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":749,"config":750},"Get your AI maturity score",{"href":751,"dataGaName":752,"dataGaLocation":239},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":754},{"src":755},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":757,"categories":758,"header":759,"text":747,"button":760,"image":764},"devops-modernization",[720,32],"Are you just managing tools or shipping innovation?",{"text":761,"config":762},"Get your DevOps maturity score",{"href":763,"dataGaName":752,"dataGaLocation":239},"/assessments/devops-modernization-assessment/",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":768,"categories":769,"header":771,"text":747,"button":772,"image":776},"security-modernization",[770],"security","Are you trading speed for security?",{"text":773,"config":774},"Get your security maturity score",{"href":775,"dataGaName":752,"dataGaLocation":239},"/assessments/security-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":780,"blurb":781,"button":782,"secondaryButton":787},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":783,"config":784},"Get your free trial",{"href":785,"dataGaName":46,"dataGaLocation":786},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":491,"config":788},{"href":50,"dataGaName":51,"dataGaLocation":786},1772652059193]