[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/customizing-gitlab-duo-chat-rules-prompts-workflows":3,"navigation-en-us":39,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-Itzik Gan Baruch":701,"blog-related-posts-en-us-customizing-gitlab-duo-chat-rules-prompts-workflows":715,"blog-promotions-en-us":756,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":38},"blogPosts/en-us/blog/customizing-gitlab-duo-chat-rules-prompts-workflows.yml","Customizing Gitlab Duo Chat Rules Prompts Workflows",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"customizing-gitlab-duo-chat-rules-prompts-workflows",false,"BlogPost",{"tags":15,"category":9,"date":20,"heroImage":21,"authors":22,"description":24,"title":25,"body":26},[16,17,18,19],"AI/ML","product","features","tutorial","2026-01-14","https://res.cloudinary.com/about-gitlab-com/image/upload/v1765809212/noh0mdfn9o94ry9ykura.png",[23],"Itzik Gan Baruch","Learn how to customize GitLab Duo Agent Platform to match your team's workflow. Configure chat rules, craft system prompts, set up agent tools, and tailor flows for your specific needs.","Customizing GitLab Duo Agent Platform: Chat rules, prompts, and workflows","*Welcome to Part 8 of our eight-part guide, [Getting started with GitLab Duo Agent Platform](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/), where you'll master building and deploying AI agents and workflows within your development lifecycle. Follow tutorials that take you from your first interaction to production-ready automation workflows with full customization.*\n\n**In this article:**\n* [Introduction to customization](#introduction-to-customization)\n* [Customize agent behavior](#part-1-customize-agent-behavior)\n* [Extend capabilities with MCP](#part-2-extend-capabilities-with-mcp)\n* [Create custom agents and flows](#part-3-create-custom-agents-and-flows)\n\n> 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today! \n## Introduction to customization\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) delivers powerful capabilities right away, and you can unlock even greater value by tailoring it to your team's specific needs. GitLab offers flexible customization options across multiple levels:\n\n- **User-level**: Personal preferences that apply across all projects (custom rules, AGENTS.md, MCP config)\n- **Workspace-level**: Project-specific configurations (custom rules, AGENTS.md, MCP config)\n- **Project-level**: Custom agents and flows you create and manage within a specific project\n\n## Part 1: Customize agent behavior\n\n### Custom rules\n\n[Custom rules](https://docs.gitlab.com/user/gitlab_duo/customize_duo/custom_rules/) provide instructions for agents and flows, ensuring consistent behavior across your team without requiring repetition. For example, in development style guides or how to execute tests.\n\nNavigate to **IDE workspace or user configuration directory**.\n\n### User-level custom rules\n\nUser-level rules apply to all your projects and workspaces.\n\nFor detailed instructions on creating user-level custom rules, see the [GitLab documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/custom_rules/#create-user-level-custom-rules).\n**Create the file** `~/.gitlab/duo/chat-rules.md` in your home directory.\n**Example rules:**\n\n```markdown - Always use TypeScript for new code, never JavaScript\n- Include JSDoc comments for all functions\n- Use single quotes for strings\n- Follow the existing code style in the repository\n- Write concise explanations, avoid lengthy descriptions\n- Suggest tests for any code changes\n- Use async/await instead of promises\n```\n\n### Workspace-level custom rules\n\nWorkspace rules apply only to a specific project. They override user-level rules for that project.\n\n**Create the file** `.gitlab/duo/chat-rules.md` in your project root.\n\n**Example rules for a Vue.js project:**\n\n```markdown\n- Use Vue 3 Composition API with `\u003Cscript setup>`\n- Always include TypeScript types for props\n- Use scoped styles with SCSS\n- Follow the Slippers UI design system\n- Keep components under 300 lines\n- Use kebab-case for component names\n- Include accessibility attributes (aria-*, role)\n```\n\n### Best practices for custom rules\n\n- **Be specific**: \"Use single quotes\" is better than \"follow style guide.\"\n- **Prioritize**: List most important rules first.\n- **Team-focused**: Rules should reflect your team's standards, not personal preferences.\n- **Actionable**: Rules should be clear enough for an AI agent to follow.\n- **Maintainable**: Update rules when your standards change.\n- **Avoid conflicts**: Don't contradict your codebase's actual style.\n\n**Tip:** Use Code Owners to manage who approves changes to `.gitlab/duo/chat-rules.md`.\n\nFor a detailed use case tutorial for custom rules, see the [Custom rules in GitLab Duo Agentic Chat for greater developer efficiency deep-dive blog post](https://about.gitlab.com/blog/custom-rules-duo-agentic-chat-deep-dive/).\n\n## AGENTS.md for customizing agent behavior\n\n[AGENTS.md](https://agents.md/) is an industry-standard file for customizing agent behavior. It allows you to define how agents should behave in your chat conversations, foundational flows, and custom flows without modifying the agents themselves.\n\n**Difference to custom rules:** AGENTS.md are consumed by all agents and flows (foundational and custom). It also follows an industry standard that other AI tools can use, for example, Claude Code as [external agent](https://docs.gitlab.com/user/duo_agent_platform/agents/external/). Use AGENTS.md when you want your instructions to apply across multiple contexts.\n\n**User-level** (applies to all your projects and workspaces):\n- macOS/Linux: `~/.gitlab/duo/AGENTS.md`\n\n- Windows: `%APPDATA%\\GitLab\\duo\\AGENTS.md`\n\n**Workspace-level** (applies to a specific project):\n- Create `AGENTS.md` in your project root.\n\n**Subdirectory-level** (applies to specific directories in monorepos):\n- Create `AGENTS.md` in subdirectories for context-specific instructions.\n\n**How it works:**\n- User-level AGENTS.md applies globally across all projects.\n- Workspace-level AGENTS.md applies to a specific project.\n- Subdirectory-level AGENTS.md files provide context for specific parts of your codebase.\n- Agents and flows combines instructions from all applicable levels.\n- Newly added or updated AGENTS.md instructions require triggering new flows, or starting a new chat with a (custom) agent.\n\n### What AGENTS.md controls\n\n- Agent personality and tone\n- Project-specific instructions\n- Coding standards and conventions\n- Tool usage preferences\n- Output formatting requirements\n- Repository structure and organization\n\n### Example AGENTS.md\n\n```markdown\n# Agent Customization for Our Project\n## General Guidelines\n- Always prioritize code quality over speed\n- Follow our project's architecture patterns\n- Reference existing code examples when suggesting changes\n- Ask for clarification if requirements are ambiguous\n## Code Style\n- Use TypeScript for all new code\n- Follow ESLint configuration in the project\n- Include unit tests for all new functions\n- Use descriptive variable names (no single letters except loops)\n## Documentation\n- Add JSDoc comments to all public functions\n- Update README.md if adding new features\n- Include examples in code comments\n## Security\n- Never suggest hardcoding secrets or API keys\n- Always validate user input\n- Use parameterized queries for database operations\n- Flag potential security issues immediately\n```\n\n### Best practices for AGENTS.md\n\n- **Be specific**: Include concrete examples from your project.\n- **Keep it concise**: Focus on what's unique to your project.\n- **Version control**: Commit to your repository and track changes.\n- **Team alignment**: Discuss with your team before finalizing.\n- **Update regularly**: Refine as your project evolves.\n- **Document repository structure**: Help agents understand your codebase organization.\n\n### Requirements\n- GitLab 18.8 or later\n- For VS Code: GitLab Workflow extension 6.60 or later\n- For JetBrains: GitLab plugin 3.26.0 or later\n- For flows: Update flow configuration to access the `user_rule` context\n\n[Learn more about AGENTS.md](https://docs.gitlab.com/user/gitlab_duo/customize_duo/agents_md/).\n\n### Custom review instructions\n\n[Custom review instructions](https://docs.gitlab.com/user/gitlab_duo/customize_duo/review_instructions/) provide specific guidelines for the [Code Review foundational flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/). The instructions ensure consistent code review standards, and can be tailored to specific file types in your project.\n\n\n**Create the file** `.gitlab/duo/mr-review-instructions.yaml` in your project root.\n\n**Example review instructions:**\n\n```yaml\ninstructions:\n  - name: Ruby Style Guide\n    fileFilters:\n      - \"*.rb\"           # Ruby files in the root directory\n      - \"lib/**/*.rb\"    # Ruby files in lib and its subdirectories\n      - \"!spec/**/*.rb\"  # Exclude test files\n    instructions: |\n      1. Ensure all methods have proper documentation\n      2. Follow Ruby style guide conventions\n      3. Prefer symbols over strings for hash keys\n\n  - name: TypeScript Source Files\n    fileFilters:\n      - \"**/*.ts\"        # TypeScript files in any directory\n      - \"!**/*.test.ts\"  # Exclude test files\n    instructions: |\n      1. Ensure proper TypeScript types (avoid 'any')\n      2. Follow naming conventions\n      3. Document complex functions\n\n```\n\n**Best practices for custom review instructions:**\n- **Be specific and actionable**: Clear, numbered instructions work best.\n- **Use glob patterns**: Target specific file types with `fileFilters`.\n- **Focus on important standards**: Prioritize the most critical review points.\n- **Explain the \"why\"**: Help reviewers understand the reasoning.\n- **Test patterns**: Ensure glob patterns match the intended files.\n\n**Tip:** Use Code Owners to protect changes to `.gitlab/duo/mr-review-instructions.yaml`.\n\nFor detailed setup instructions and examples, see the [Custom Review Instructions documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/review_instructions/).\n\n## Part 2: Extend capabilities with MCP\n\nModel Context Protocol (MCP) enables agents to access external systems like Jira, Slack, AWS, and more. This section covers MCP configuration for extending agent capabilities.\n\n> **🎯 Try it now:** [Interactive demo of MCP](https://gitlab.navattic.com/mcp) - Explore how to use Model Context Protocol.\n\n### MCP configuration for external integrations\n\nModel Context Protocol (MCP) enables agents to access external systems like Jira, Slack, AWS, and more.\n\n**Scope:** User-level (applies to all workspaces) or Workspace-level (project-specific, overrides user config)\n\n**Create user configuration:**\n- **macOS/Linux**: `~/.gitlab/duo/mcp.json`\n- **Windows**: `C:\\Users\\\u003Cusername>\\AppData\\Roaming\\GitLab\\duo\\mcp.json`\n- **VS Code**: Run command `GitLab MCP: Open User Settings (JSON)`\n\n**Create workspace configuration:**\n- **Create file**: `.gitlab/duo/mcp.json` in your project root\n\n**Best practices:**\n- **Security first**: Use MCP servers that require OAuth and not plain-text password tokens.\n- **Minimal scope**: Only enable MCP servers you actually use and trust.\n- **Test locally**: Verify MCP connections and authorization work before sharing across teams.\n- **Document integrations**: Explain what each MCP server provides.\n- **Version control**: Store configuration in `.gitlab/duo/mcp.json` with Code Owners' approval.\n\nFor detailed setup instructions and configuration examples, see [Part 7: Model Context Protocol (MCP) Integration](/blog/duo-agent-platform-with-mcp/).\n\n## Part 3: Create custom agents and flows\n\nCustom agents and flows allow you to automate your team's specific workflows. Before diving into customization, it's helpful to understand what they are and how they work. Here are parts of the [Getting started with GitLab Duo Agent Platform guide](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/) that can help.\n- **[Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/)** — Learn about foundational, custom, and external agents, and when to use each type.\n- **[Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/)** — Discover how flows orchestrate multiple agents to solve complex problems.\n- **[Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/)** — Learn how to discover, create, and share agents and flows across your organization.\nOnce you understand the basics, this section provides an overview of customization options with links to detailed guides.\n\n### System prompts for custom agents\n\nSystem prompts define an agent's personality, expertise, and behavior. A well-crafted prompt makes agents more effective and aligned with your team's needs.\n\n**What are system prompts?** System prompts are instructions that tell an agent how to behave, what expertise it has, and how to respond to requests. They're the foundation of custom agent behavior.\n\n**Key elements of a strong system prompt:**\n- **Role definition**: What the agent is and what it does\n- **Expertise areas**: Specific domains or technologies\n- **Behavior guidelines**: How it should interact and respond\n- **Output format**: Structure of responses\n- **Constraints**: What it should avoid\n\n**Best practices:**\n- **Be detailed**: More specific prompts produce better results.\n- **Use examples**: Show the agent what good output looks like.\n- **Define scope**: Clearly state what the agent should and shouldn't do.\n- **Test iteratively**: Refine prompts based on agent behavior.\n- **Version control**: Track prompt changes in your repository.\n\nFor detailed guidance on crafting system prompts and creating custom agents, see [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/).\n\n### Custom agents and flows\n\nThere is a lot to learn, and for easier reading, the tutorials are split:\n\n**Custom agents:**\n- Learn how to create agents with custom system prompts, configure tools, and manage permissions.\n- See [Part 3: Understanding agents - Custom agents section](/blog/understanding-agents-foundational-custom-external/#custom-agents).\n\n**Custom flows:**\n- Learn how to create multi-step workflows, configure components, and set up event-driven automation.\n- See [Part 4: Understanding flows — Custom flows section](/blog/understanding-flows-multi-agent-workflows/#custom-flows).\n\n**Agent tools:**\n- Tools determine what actions agents can perform. Configure tools based on your agent's purpose and security requirements.\n- See [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/) for tool configuration details.\n\n\n## Quick reference: When to use customizations\n\n| Tool | Best For | Location |\n|------|----------|----------|\n| **Custom Rules** | Guiding Chat responses in IDE (tone, style, behavior) | `~/.gitlab/duo/chat-rules.md` (user) or `.gitlab/duo/chat-rules.md` (workspace) |\n| **AGENTS.md** | Enforcing standards across chat, flows, and other AI tools | `~/.gitlab/duo/AGENTS.md` (user) or `AGENTS.md` (workspace root) |\n| **Custom Review Instructions** | Guiding code review standards for specific file types | `.gitlab/duo/mr-review-instructions.yaml` (workspace only) |\n| **System Prompts** | Customizing individual agent behavior | AI Catalog when creating an agent |\n| **MCP Configuration** | Connecting agents to external tools | `~/.gitlab/duo/mcp.json` (user) or `.gitlab/duo/mcp.json` (workspace) |\n| **Custom Agents** | Creating specialized agents for team-specific tasks | Automate → Agents or AI Catalog |\n| **Custom Flows** | Orchestrating multiple agents in workflows | Automate → Flows or AI Catalog |\n\n## What's next?\n\nCongratulations! You've completed the entire GitLab Duo Agent Platform series. You now understand:\n- How to use agents and flows across the entire SDLC, tailored to your use cases\n- How to discover and share solutions in the AI Catalog\n- How to monitor and manage your AI workflows\n- How to extend capabilities with MCP integrations\n- How to customize every aspect of GitLab Duo Agent Platform for your team\n\n**[Return to complete series overview](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)** to review all parts and explore specific topics in depth.\n\n## Resources\n\n- [Custom Rules documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/custom_rules/)\n- [AGENTS.md documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/agents_md/)\n- [Custom Review Instructions documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/review_instructions/)\n- [Custom Agents documentation](https://docs.gitlab.com/user/duo_agent_platform/agents/custom.html)\n- [Custom Flows 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and Vertex AI on Google Cloud: Advancing agentic software development","Learn how Google Cloud customers are standardizing on GitLab and Vertex AI for foundation models, enterprise controls, and Model Garden breadth.\n",[721,722],"Regnard Raquedan","Rajesh Agadi","GitLab Duo Agent Platform is helping redefine how organizations build, secure, and deliver software. Since its general availability in January 2026, the platform is bringing agentic AI to every phase of the software development lifecycle. Duo Agent Platform is an intelligent orchestration layer where software teams, and their specialized agents plan, code, review, and remediate security vulnerabilities together.\n\nThrough this exciting partnership, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) automates software development orchestration and lifecycle context via its integration with Vertex AI on Google Cloud, which powers the model tier for agent calls. Software teams keep working on issues, merge requests, pipelines, and security workflows while inference follows the Google Cloud posture they already defined. \n\nAdvances in Google Cloud’s Vertex AI models expand how Google Cloud customers can use GitLab Duo Agent Platform in their environment. Customers get an AI-powered DevSecOps control plane in GitLab, backed by a rapidly advancing AI infrastructure foundation in Vertex AI and Duo Agent Platform’s flexible deployment and integration options. The combination enables more capable, governed agentic workflows that operate at enterprise scale.\n\n![Conceptual illustration of the GitLab Duo Agent Platform integrated with Google Cloud's Vertex AI to power agentic software development and governed AI workflows](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776165990/b7jlux9kydafncwy8spc.png)\n\n## Agents that work across the full lifecycle\n\nMany AI tools focus on a single task: generating code faster. GitLab Duo Agent Platform goes further. It orchestrates AI agents across the entire software development lifecycle (SDLC), from planning through security review to delivery, across many teams with many projects and releases. At this scale, AI coding assistants are necessary for continuous innovation but not sufficient. \n\nSingle-purpose coding assistants rarely see the full state of a project. Backlog shape, open merge requests, failing jobs, and security findings live in GitLab, but a separate chat window in a coding assistant does not inherit that full picture of the SDLC. The gap shows up as manual handoffs, duplicate explanations to an AI that lacks context, and governance teams trying to map data flows across tools that were never designed as one system.\n\nGitLab Duo Agent Platform helps close that gap by running agents and flows on the same objects engineers use every day. Vertex AI then supplies the models and services those agents call when Google Cloud is your chosen inference home, with GitLab’s AI Gateway mediating access so administrators keep a clear map of what connects to what. For instance, GitLab Duo Planner Agent analyzes backlogs, breaks epics into structured tasks, and applies prioritization frameworks to help teams decide what to build next. Security Analyst Agent triages vulnerabilities, details risks in plain language, and recommends remediation in priority order. Built-in flows connect these agents into end-to-end processes, without requiring developers to manage every handoff manually.\n\nAgentic Chat in GitLab Duo Agent Platform ties the experience together for developers. They query in natural language to get context-aware responses with multi-step reasoning that draws on the full state of a project: its issues, merge requests, pipelines, security findings, and codebase. Because GitLab serves as the system of record for the SDLC with a unified data model, GitLab Duo agents operate with lifecycle context that falls outside the reach of standalone, tool-specific AI assistants.\n\n### Amplified by Vertex AI\n\nGitLab Duo Agent Platform is designed to be model-flexible, routing different capabilities to different models based on what performs best for a given task. That architectural choice pays off on Google Cloud, where Vertex AI acts as the managed environment for foundation models and related services, providing a broad model ecosystem and managed infrastructure that helps push the platform's capabilities further.\n\nThe latest generations of AI models available through Vertex AI bring significant improvements in reasoning, tool use, and long-context understanding compared to previous iterations — the same properties that GitLab's agents rely on across many projects and teams with large, complex codebases. Longer context windows and richer tool integration in the underlying models expand what agents can accomplish in a single pass, which is especially important for workloads like deep backlog analysis or monorepo security review.\n\n[Vertex AI Model Garden](https://cloud.google.com/model-garden), with access to a wide range of foundation models, gives customers the breadth to make these choices based on performance, cost, and regulatory requirements rather than vendor lock-in.\n\nMoreover, GitLab customers can use Bring Your Own Model (BYOM) for Duo Agent Platform so approved providers and gateways land where your security model expects them. GitLab’s [18.9 launch coverage of self-hosted Duo Agent Platform and BYOM](https://about.gitlab.com/blog/agentic-ai-enterprise-control-self-hosted-duo-agent-platform-and-byom/) describes how that wiring works. With this deployment option, customers gain access to a wider set of model options they can tailor to their software development process: the right model for the right workflow, with the right guardrails.\n\nFor GitLab, the decision to build on Vertex AI was driven by the need for enterprise-grade reliability and unparalleled model breadth. Vertex AI and Model Garden completely abstract the heavy lifting of LLM hosting — meaning rapid version delivery, robust security, and strict governance are seamlessly built into the integration. Beyond offering Gemini models, Vertex AI provides global, low-latency access to a vast catalog of third-party and open-source models. \n\nCombined with Google Cloud's industry-leading approach to data privacy and model protection, Vertex AI emerged as the clear choice to power GitLab's next-generation developer experience. \n\nBy integrating Vertex AI Model Garden into its backend, GitLab supercharges its DevSecOps platform without passing any complexity on to users. Development teams are not burdened with evaluating or managing underlying LLMs; instead, they experience a streamlined, AI-assisted workflow for building their applications. \n\nGitLab completely abstracts cloud orchestration, enabling developers to focus entirely on writing great code, while Vertex AI powers the features and functionality that assist them.\n\n## What this means for customers on Google Cloud\n\nGitLab Duo Agent Platform already delivers AI agents that operate across the full software lifecycle within a single, governed system of record. On Google Cloud, it enables rapid innovation as Vertex AI continues to advance the model and infrastructure layers. \n\nFor Google Cloud customers, this integration means streamlined software delivery while maintaining strict enterprise governance. For platform engineering groups, it means normalizing which Vertex-backed models power suggestions, analysis, and remediation inside GitLab instead of cataloging dozens of client-side tools. Security programs benefit when agents propose and validate fixes in the same place developers already triage findings, cutting context switching and reducing work that would otherwise spill into unmanaged channels.\n\nFrom a cloud economics and policy angle, drawing agent inference toward Vertex from within GitLab keeps usage nearer to the agreements and controls you already run on Google Cloud, which helps avoid duplicate spend and shadow paths that bypass procurement.\n\nBecause Vertex AI is an underlying infrastructure provider for GitLab Duo Agent Platform, organizations are enabled to dramatically lift developer productivity without the overhead and risk of managing fragmented AI toolchains. Teams stay aligned within a single, secure system of record, helping them build applications faster and ship with confidence.\n\nThe GitLab and Google Cloud collaboration has been building since 2018. Today, it represents one of the most comprehensive paths for organizations moving from AI experiments to fully governed, agentic software development on Google Cloud. As both platforms continue to advance — GitLab expanding its agent orchestration and developer context, and Vertex AI pushing the boundaries of model capability and agent infrastructure — the value for joint customers will continue to grow.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/free-trial/) to experience the power of GitLab and Vertex AI on Google Cloud.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2026-04-14",[16,276,727,728,17],"google","news",{"featured":29,"template":13,"slug":730},"gitlab-and-vertex-ai-on-google-cloud",{"content":732,"config":741},{"heroImage":733,"title":734,"description":735,"authors":736,"date":738,"category":9,"tags":739,"body":740},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643639/sapu29gmlgtwvhggmj6k.png","Extend GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[737],"Albert Rabassa","2026-03-05",[9,17,19],"Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian  MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n   * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n   * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n   * Click **Create** → **OAuth 2.0 integration**\n\n   * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n   * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n   * Navigate to **Permissions** in the left sidebar.\n\n   * Add **Jira API** and configure the following scopes:\n\n     * `read:jira-work` — Read issues, projects, and boards\n\n     * `write:jira-work` — Create and update issues\n\n     * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n   * Go to **Authorization** in the left sidebar\n\n   * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n   * Save your changes\n\n5. **Retrieve credentials**\n\n   * Navigate to **Settings**\n\n   * Copy your **Client ID** and **Client Secret**\n\n   * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n  \"mcpServers\": {\n    \"atlassian\": {\n      \"type\": \"http\",\n      \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n      \"auth\": {\n        \"type\": \"oauth2\",\n        \"clientId\": \"YOUR_CLIENT_ID\",\n        \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n        \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n        \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n      },\n      \"approvedTools\": true\n    }\n  }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":12,"template":13,"slug":742},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"content":744,"config":754},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"body":751,"category":9,"tags":752},"10 AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[748],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[16,753],"DevOps platform",{"featured":12,"template":13,"slug":755},"10-ai-prompts-to-speed-your-teams-software-delivery",{"promotions":757},[758,771,782,794],{"id":759,"categories":760,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[17,569],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":781},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":50,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":505,"config":817},{"href":54,"dataGaName":55,"dataGaLocation":815},1776442965492]