Catalog

Find CI configurations that need attention, review proposed migrations, and plan the changes with the teams who own those pipelines.

Review a merge request against your project's compliance policy, with an explanation for each result, any checks still needed, and a suggested label.

Find guides and runbooks left out of date by a code change, approve the suggested edits, and review the checked updates in a separate merge request.

Follow a failed pipeline through Duo's diagnosis, review its suggested fix, and check the next pipeline. Learn when a project setting is the better answer.

Compare a proposed Flow change with a version you know works, review the test results, and decide whether to adopt it or restore the earlier version.

Find projects in a GitLab group whose CI includes don't match your team's policy, then review the findings before creating issues for their owners.

Use incident observations and GitLab records to understand what happened and plan work to prevent it from happening again, while leaving production operations to your team.

Check whether an issue gives an agent enough information to start work, with suggestions for missing details grounded in the repository.

Organize a migration across projects, approve each group of projects before its work begins, and track progress through their issues.

Compare Duo's review before and after adding project-specific instructions, then fix the issue and check the next review.

Turn a clear issue into a tested merge request, with a person reviewing the plan before implementation begins.

Review an advisory across current dependency scans, keep missing checks visible, and approve follow-up issues before project owners make and verify repairs.

Use GitLab Duo with Orbit to find callers, check what a library change means for them, and choose a useful regression test.

Delegate routine implementation to AI. Get working code with tests from requirements.

Get consistent, thorough code reviews at scale. Enforce patterns across all MRs automatically.

Enforce team code style consistently across all MRs. Eliminate bikeshedding in reviews.

Review database migrations for safety, reversibility, and performance impact. Prevent production issues.

Ensure API documentation is complete with examples, error responses, and authentication details.

Implement AWS-native features with best practices, infrastructure code, and compliance requirements.

Identify related open MRs that might conflict. Coordinate changes across team members.

Catch compliance violations early before they block deployment. Pre-check for HIPAA, SOC2, GDPR.

Analyze test coverage gaps and suggest specific test cases. Ensure critical paths are tested.

Think through approaches collaboratively. Get challenged on tradeoffs and edge cases you might miss.

Document how your system integrates with internal and external APIs. Prevents breaking changes in integrations.

Understand complex database schemas, relationships, and optimization opportunities. Identify performance bottlenecks in your data model.

Triage security scan results by real risk vs false positives. Prioritize remediation effectively.

Parse complex stack traces to identify error origins and suggest fixes.

Challenge architectural assumptions collaboratively. Explore failure modes and scaling concerns.

Evaluate if an MR includes adequate tests for new functionality. Identify gaps before they reach production.

Assess external dependencies for vulnerabilities, outdated versions, and license compliance.

Audit codebases for compliance violations. Check data handling, access controls, and encryption.

Automate repetitive development tasks with error handling and logging. Eliminate manual toil.

Transform large epics into manageable, well-scoped issues with clear dependencies and acceptance criteria.

Scaffold complete applications with database schemas, API endpoints, and frontend components.

Categorize features based on MoSCoW criteria.

Ensure code follows team conventions and style guides. Consistent enforcement without tedious manual checks.

Understand design decisions and tradeoffs in MRs. Get context beyond just what changed.

See which programming languages have the highest code suggestion acceptance rates to understand where AI assistance is most effective.

Measure code suggestion adoption across baseline and rollout windows to track progress, compare acceptance rates, and identify trends by language and IDE.

Compare code suggestion volume and acceptance rates across IDEs to see where developers are getting the most value from AI assistance. Visualise as a column chart.

Compare implementation approaches with tradeoff analysis. Make informed decisions about performance vs maintainability.

Identify and consolidate duplicate code into reusable components. Apply DRY principles systematically.

Transform external documentation into inline code comments. Keep documentation close to the code.

Get a count of merge requests merged in the current month to track team delivery pace.

Document UI components with usage guidelines, props, and accessibility notes.

Document recovery procedures with RTOs, backup strategies, and failover processes.

Design tests for component interactions, error handling, and timeout scenarios.

Convert support tickets into properly formatted, labeled issues. Streamline your support-to-development workflow.

Create production-ready microservices with proper patterns, health checks, and containerization.

Generate security-focused test cases for input validation, auth bypass, and injection attacks.

Create comprehensive design documents with rationale, diagrams, and success criteria. Document decisions for posterity.

Create self-service troubleshooting guides with diagnostic steps and escalation criteria.

Diagnose CI/CD failures quickly with root cause analysis and fix suggestions.

Find memory leaks by analyzing object lifecycles and retention issues.

Identify race conditions in asynchronous code. Debug intermittent failures systematically.

Track debugging hypotheses and test results systematically. Structured approach to complex bugs.

Define RESTful or GraphQL APIs with proper contracts, authentication, and error handling.

Define architecture, components, and integration points for new features. Start projects with solid foundations.

Build UI components matching design specs with proper accessibility and responsiveness.

Parse customer logs to diagnose issues and suggest solutions. Accelerate support resolution.

Explore GitLab capabilities you might not know about. Find features that match your needs.

Create memo including objectives and success criteria.

Verify code meets regulatory compliance standards: SOC2, HIPAA, PCI-DSS, GDPR.

Break down work and estimate development time. Improve sprint planning accuracy.

Compare technologies or frameworks with structured tradeoff analysis. Make informed stack decisions.

Get step-by-step explanations of complex algorithms and legacy code. Learn the why behind the how.

Assess how proposed changes will affect system performance. Catch complexity issues and bottlenecks early.

Explain technical concepts without jargon. Bridge the gap between engineering and users.

Understand why tests fail, identify root causes, and determine if the code or test needs fixing.

Get clear explanations of complex code or merge requests without manual investigation. Perfect for code reviews and onboarding.

Explore unfamiliar codebases conversationally. Understand architecture and key dependencies interactively.

Discover how specific patterns are implemented in your existing codebase. Learn from your team's established conventions.

Discover correct API endpoints with parameters, authentication, and example requests.

Trace bugs from symptoms to root causes. Find where problems actually originate.

Navigate call graphs to understand how functions interconnect throughout your codebase. Reveals hidden dependencies instantly.

Assess framework upgrade impact with breaking change analysis and migration planning.

Generates risk assessment based on the content of the work item.

Generate audit-ready compliance reports with evidence and remediation status.

Generate standard CRUD operations with validation, error handling, and basic tests.

Transform technical errors into user-friendly messages with clear next steps.

Create reusable CI pipeline templates with security scanning and deployment strategies.

Generate hardware interface code for I2C, SPI, UART protocols with proper interrupt handling.

Create standardized issue templates for bugs, features, and tasks. Ensure consistent issue quality.

Auto-generate comprehensive MR descriptions including what changed, testing steps, and related issues.

Automatically compile release notes from merged MRs. Group changes by type: features, fixes, breaking changes.

Document security controls for auditors and stakeholders. Maintain compliance evidence.

Create realistic test datasets including valid records, edge cases, and boundary conditions.

Generate comprehensive unit tests covering happy paths, edge cases, and error conditions automatically.

Detect if changes break existing APIs, contracts, or functionality. Prevent downstream breakages.

Find similar code patterns that could be consolidated into shared utilities. Reduce maintenance burden through DRY principles.

Identify security vulnerabilities during code review. Catch SQL injection, XSS, and other risks early.

Optimize CI/CD pipelines for speed, security, and reliability. Apply GitLab best practices.

Automate data lifecycle management per retention policies. Maintain compliance automatically.

Apply design patterns correctly with complete implementations and usage examples.

Make layouts work across all screen sizes with optimized touch targets and performance.

Implement granular permissions with RBAC patterns, admin interfaces, and audit logging.

Add ARIA labels, keyboard navigation, and screen reader support. Meet WCAG 2.1 standards.

Generate a structured summary of a user's contributions (MRs, reviews, work items) across one or more groups or projects for any review or reporting purpose.

Connect applications to corporate identity providers with SAML/OIDC integration.

Pinpoint performance bottlenecks through profiling analysis. Know exactly what to optimize.

Spot trends in throughput by comparing how many issues were closed month over month.

Identify merge requests that have been open for more than 30 days to help teams reduce review bottlenecks and improve flow.

Visualize how different components depend on each other. Understand the blast radius of potential changes.

Get a directional read on merge request throughput during a GitLab Duo trial by comparing cohort vs non-cohort activity across baseline and trial windows.

Migrate code between frameworks while preserving functionality and adopting modern patterns.

Update legacy code to use current language features and best practices. Modernize without breaking functionality.

Implement tenant boundaries for SaaS applications. Ensure complete data separation.

Find relevant code sections in unfamiliar projects without knowing the structure. Accelerates exploration of new codebases.

Get a breakdown of open issues by priority and health status to help teams focus on what matters most.

Improve algorithmic efficiency while maintaining correctness. Optimize hot paths for performance gains.

Optimize slow queries with index suggestions and query rewrites. Improve database performance.

Optimize code for memory-constrained embedded devices. Balance performance and power consumption.

Coordinate multiple specialized agents for complex multi-step workflows.

Spot trends in CI/CD health by comparing pipeline failure rates and durations week over week.

Compare CI pipeline reliability across all projects in a group to find which projects have the most pipeline failures.

Chart how much CI activity your project is generating over time as a line chart, to spot capacity issues or unexpected dips.

Design safe database migrations with rollback plans. Prevent data loss and minimize downtime.

Transform large epics into implementable issues with prioritization and dependency mapping.

Categorize review comments by severity—critical blockers vs nice-to-have suggestions. Focus effort effectively.

Analyze performance profiles to identify bottlenecks, hot paths, and optimization priorities.

Get a cross-cutting view of project delivery health combining work item flow, merge request throughput, and pipeline reliability into a single narrative.

Break down complex functions into testable, maintainable components. Reduce cognitive load for future developers.

Improve code readability with better names, simplified logic, and clearer structure. Make code self-documenting.

Find and safely remove unused code, deprecated methods, and commented-out blocks. Keep your codebase lean.

Proactive security review for injection vulnerabilities, auth flaws, and data exposure risks.

Catch logical errors and edge cases that syntax checkers miss. Go beyond linting to find real bugs.

Find untested code paths and prioritize which areas need coverage most urgently.

Explain problems systematically to reveal solutions. Always-available rubber duck debugging partner.

Find relevant GitLab features and documentation for your specific use case.

Triage vulnerabilities by exploitability and business impact. Focus on real threats first.

Creates update for stakeholders based on the content of the work item.

Surface open bug issues with no recent activity to help teams identify and act on work that may be falling through the cracks.

Receive actionable feedback on code quality, performance, and maintainability. Elevate your team's coding standards.

Identify the right reviewers based on code ownership and domain expertise. Route MRs efficiently.

Catch up on long-running MRs or issues by getting a summary of key decisions, changes, and action items.

Identify the most frequently failing CI/CD jobs to help teams prioritize pipeline reliability improvements.

Map authentication flows and permission checks across your system. Essential for security audits and debugging access issues.

Quickly locate all call sites for any method or function across your entire codebase. Invaluable when planning refactoring work.

Internationalize applications with proper i18n, date formatting, and RTL support.

Rapidly diagnose production incidents with systematic troubleshooting guidance.

Understand asynchronous code, promises, and concurrent operations. Identify potential race conditions before they cause bugs.

Map out class hierarchies and identify which methods are overridden. Critical for understanding object-oriented architectures.

Analyze code dependencies and potential impacts before modifying unfamiliar code sections. Essential for preventing breaking changes during refactoring.

Decode configuration files and environment setup requirements. Speeds up onboarding and project switching.

Follow data as it moves through multiple components, tracking each transformation step. Essential for debugging data issues.

Learn how errors are handled throughout your application. Identify inconsistencies and gaps in error handling.

Interpret cryptic third-party library errors. Understand what you're doing wrong and how to fix it.

Identify which documentation needs updating after code changes. Prevent documentation drift.

A failed pipeline doesn't always mean the latest code change caused it. A passing retry might point to an intermittent test, a runner problem, or a service that recovered. Give this Skill the pipeline URL in a GitLab Duo, Claude, or Codex session with GitLab access. The agent reads the job attempts and logs, compares them with a relevant successful run when one exists, and recommends a check to narrow down the cause.

Add this free AI skill to your existing agent or AI tool (Cursor, VS Code, Claude, or any MCP-compatible client) and convert GitHub Actions workflows to GitLab CI/CD in seconds.

A free AI skill that drafts and validates GitLab CI pipelines directly in your local editor, including Cursor, VS Code, Claude Code, and any agent you already use.

You've built a Flow that gives a good answer when you ask it to. Before letting everyday project activity start it automatically, try a few less convenient requests: an issue that's missing details, a comment that asks it to skip review, and an event that happens twice.