Generate with AI

If you already have a GitHub Actions workflow, you can use an AI coding assistant to produce the equivalent Mélodium pipeline in minutes, without having to learn the language first.

What You Need

Your existing .github/workflows/ directory (the agent will read it directly).
An AI coding assistant running locally in your project (Claude Code, Cursor, Zed, or similar).
The Mélodium skill installed in your AI tool. See Install the Mélodium Skill.

Step 1: Make Sure the Migration Reference Is Included

The general Mélodium skill covers the language and project layout. For a GitHub Actions migration, the AI also needs skills/melodium/references/github-migration.md, which contains the exact treatment patterns for runAction, prepareContexts, githubJobResult, matrix jobs, and status reporting.

With Claude Code, this is included automatically when the skill is installed under ~/.claude/skills/melodium. The skill loads its reference files on demand.

With Cursor or Zed, make sure your rule file includes it:


# Cursor
cat skills/melodium/SKILL.md \
    skills/melodium/references/github-migration.md \
    skills/melodium/references/projects.md \
    > .cursor/rules/melodium.md
 
# Zed
cat skills/melodium/SKILL.md \
    skills/melodium/references/github-migration.md \
    skills/melodium/references/projects.md \
    > .rules

Step 2: Ask for the Migration

Your AI agent is already running inside your project and can read your workflow files directly. A simple prompt is enough:


Read the workflows in .github/workflows/ and migrate them to Mélodium.
Generate a complete project under .melodium-ci/ with Compo.toml and .mel files.
Use simpleStep for container jobs, localStep for self-hosted jobs.
Report status back to GitHub using the token and project parameters.

If your workflows reference composite actions stored in .github/actions/, mention them so the agent reads those too:


Also read the composite actions in .github/actions/ and inline them as treatments.

The AI will produce a ready-to-use Mélodium project with:

A Compo.toml declaring the right dependencies.
One .mel file per workflow, with one treatment per job.
Parallel wiring for matrix jobs.
Artifact streaming between jobs that depend on each other.
GitHub commit status reporting.

Step 3: Review and Adjust

Read through the generated code before running it. Useful things to check:

Resource sizing: cpu, memory, and storage parameters in simpleStep have defaults that may not match your workload. Adjust them to reflect your actual build requirements.
Secrets and tokens: make sure github_token, github_project, and github_sha are wired to the right entrypoint parameters.
Third-party actions (uses:): actions from the marketplace cannot be translated automatically. The AI will inline their logic as shell commands where possible, but complex actions may need manual attention.
Conditional jobs (if:): verify that filterBlock wiring matches the intent of the original condition.

Concept Reference

If you want to understand what the AI produced, or adjust it yourself, this table shows how GitHub Actions concepts map to Mélodium:

GitHub Actions	Mélodium
Workflow file	`.mel` treatment
Job	Treatment with `simpleStep` or `localStep`
`run:` step	`runAction[contexts=contexts]()`
`${{ }}` expression	Evaluated via `JavaScriptEngine`
`jobs.<job>.needs`	Sequential wiring + `includeNeeds`
`strategy.matrix`	Parallel treatment instances with `one<void>()`
`jobs.<job>.if`	`filterBlock<void>()` on the job trigger
Status reporting	`postGithubState` / `postGithubStateContext`

Full API documentation is at doc.melodium.tech .

Going Further

The generated code is a solid starting point and can be extended with any Mélodium element. For distribution across multiple machines and advanced parallelisation, see Simple CI/CD Implementation and Advanced CI/CD Implementation.

Build a Pipeline with AI Automated Migration Tool