AI-Assisted Migration
Using GitLab Duo and frontier models for planning, conversion, validation, and cleanup.
AI tools — GitLab Duo first, frontier models as a fallback — speed up the parts of a migration that are repetitive and pattern-matching: workflow conversion, documentation rewrites, pipeline debugging, and post-import cleanup.
This page is grounded in what AI is actually good at here, plus prompt patterns that work. Verify whatever the model generates before committing it.
Where AI helps
- Planning. Generate a per-repository migration plan and complexity assessment from your workflow files and integration list.
- Conversion. Translate
.github/workflows/*.ymlto.gitlab-ci.yml, including matrix builds, conditional rules, and artifact handling. - Documentation. Rewrite README, CONTRIBUTING, and badge URLs from GitHub to GitLab conventions.
- Validation. Cross-check imported counts, surface missing items, and explain partial-failure logs.
- Debugging. Read CI/CD job logs and propose fixes. GitLab Duo's Root Cause Analysis runs this directly on a failed job.
- Cleanup. Suggest improvements specific to GitLab — caching,
needs:DAGs, CI/CD Components, security scanners.
GitLab Duo
The in-platform option, and the right default. Duo has the GitLab schema baked in and runs from inside the project, so it gets context most external tools don't.
- Code Suggestions while editing
.gitlab-ci.ymlin the Web IDE or with the Duo extension. - Duo Chat for ad-hoc questions ("What's the GitLab equivalent of
actions/cache?"). - Root Cause Analysis on failed pipeline jobs — opens the relevant log and proposes a fix.
- Merge request summaries when you open the MR that lands the migration.
Duo is available on Premium and Ultimate tiers. Enable it under User settings → GitLab Duo.
Frontier models
When Duo isn't available, frontier models (Claude, GPT-class) handle the same tasks competently. They lack live access to your GitLab instance, so paste in the relevant context.
Prompt patterns
Convert a workflow
Convert this GitHub Actions workflow to GitLab CI/CD.
Preserve: matrix builds, job dependencies, artifacts, conditional rules.
Use stages where they make the pipeline easier to read.
Add a comment on any line where the GitLab semantics differ from GitHub.
[paste workflow]
Pre-migration audit
I'm migrating this repository from GitHub to GitLab.
Stack: [language, framework, deploy target]
Workflows: [count, brief description]
Integrations: [Dependabot, security scanners, third-party services]
Output:
1. A prioritised checklist
2. The riskiest items in this stack
3. Rough time estimate for each
4. Recommended order
Update the README
Rewrite this README for GitLab.
Replace GitHub-specific badges, URLs, and CLI examples.
Change "Pull Request" to "Merge Request".
Keep all other content intact.
[paste README]
GitLab project URL: [url]
Debug a failing pipeline
This GitLab CI/CD job is failing.
Job log:
[paste log]
Relevant .gitlab-ci.yml:
[paste section]
Identify the cause and suggest the minimal fix.
Propose GitLab-specific improvements
Here's a translated .gitlab-ci.yml. Suggest changes that take advantage of
GitLab features the original GitHub workflow couldn't:
- caching strategy
- DAG via needs:
- CI/CD Components from the Catalog
- security scanners (SAST, Dependency, Secret Detection)
[paste .gitlab-ci.yml]
Pitfalls
Watch out for these
- Don't paste secrets. Strip tokens, keys, and internal hostnames before sending a prompt to any external model.
- Verify generated YAML. Run it through
your-gitlab-instance/-/ci/lintand a real pipeline before merging. - Don't trust action mappings blindly. Some popular Actions have no clean GitLab equivalent; the model will sometimes invent one. Cross-check against Actions to CI/CD.
- Models drift on version-specific behavior. GitLab 18.x added details (collaborator group restriction, Enterprise Server attachment limits) that older training data doesn't reflect. Confirm against the import docs.
- Read what you commit. AI output is a draft, not a finished change.