migration-patterns

name: migration-patterns description: "Migrate workloads from Kubernetes, Docker Compose, or Helm to Control Plane. Use when the user asks to convert k8s manifests, a docker-compose.yml, or Helm charts, or to move an existing app onto the platform."

Migrating to Control Plane

Tool availability: some MCP tools named here live in the full toolset profile — if one is not advertised on this connection, tell the user to reconnect the MCP server with ?toolsets=full (or use the cpln CLI fallback). Reads and deletes work on every profile via the generic list_resources / get_resource / delete_resource tools.

Each source format has its own converter, and they are not interchangeable: Kubernetes through cpln convert, Docker Compose through cpln stack, a Helm chart of Control Plane resources through cpln helm. All three are CLI-only — there is no MCP converter. The dominant failure is hand-translating a Compose/k8s/Helm artifact into Control Plane YAML — even one "small enough to do by hand" — instead of running the tool and then reviewing what it left behind. The converter gets the mechanical translation right; your value is the gap analysis on top of it. If asked to translate by hand, push back: convert first, then work through the fix-ups.

Pick the conversion path

cpln helm does not convert Kubernetes manifests — its charts must render only Control Plane kinds. There is no cpln stack convert; cpln stack manifest previews the generated YAML.

Kubernetes (cpln convert)

cpln convert -f FILE [--gvc GVC] reads a single file, a directory (recursive), or - for stdin, and writes Control Plane YAML. cpln apply -f FILE --k8s true runs the same converter and applies the result in one step. Resources that become their own Control Plane resource:

Resources that shape the conversion without becoming their own resource: Service (port-protocol inference, public-exposure detection that sets the workload firewall, ingress route resolution), HorizontalPodAutoscaler (workload minScale/maxScale/scaleToZeroDelay/CPU target), ServiceAccount (image pull-secret extraction), PersistentVolume + StorageClass (volumeset capacity, performance class, filesystem), EndpointSlice (pod mapping for selectorless services).

Workload type — cron > stateful > standard: a Job/CronJob becomes cron; otherwise any container mounting a volumeset (from a PVC or volumeClaimTemplates) becomes stateful; everything else is standard. The converter never emits serverless or vm — switch a workload to those yourself after converting.

Secret type mapping:

PVC performance class: io1, io2, pd-extreme, UltraSSD_LRS, thick, fast, persistent_1 map to high-throughput-ssd (matched on the StorageClass parameter value); everything else — gp2, gp3, the default — maps to general-purpose-ssd.

Port protocol (when --protocol is not forced): Service appProtocol wins outright; otherwise the converter gathers hints from the Service and container port-name prefixes, the probe type, and the port number, then picks the most specific (grpc > http2 > http > tcp); default tcp.

The converter auto-creates an identity identity-<workload> and policy policy-<workload> granting reveal for every workload that references secrets. When --gvc is omitted, workload links carry a {{GVC}} placeholder — replace it before applying.

What cpln convert leaves for you

The converter translates structure faithfully but warns on only two things — a ConfigMap/Secret name collision (it renames the ConfigMap with a -config suffix) and an acceptAll* domain needing a dedicated load balancer. Everything below changes or disappears silently, so diff the source against the output.

• Scaling is pinned, not autoscaled. A converted workload gets minScale = maxScale = the Deployment's replicas (or 1 if unset) with capacityAI: false — no headroom. An HPA, if present, supplies min/max and a CPU target. Raise maxScale above minScale for anything that should scale, keep customer-facing minScale ≥ 2, and consider Capacity AI (autoscaling-capacity skill).
• Silently dropped from the pod spec (the workload runs, but differently): envFrom (bulk ConfigMap/Secret env — re-add the keys as env or a mounted dictionary secret), initContainers (migrations/setup — run as a separate cron workload or an entrypoint step), startupProbe (only liveness/readiness carry over), container-level securityContext (only the pod-level securityContext.fsGroup carries over, as filesystemGroupId), and hostPath volumes. emptyDir becomes a scratch:// volume.
• Not converted at all (no resource, no warning): NetworkPolicy, PodDisruptionBudget, RBAC, ResourceQuota/LimitRange, ServiceMonitor and other CRDs, and Namespaces — every namespace collapses into the one target GVC. Re-express network rules as the workload firewall (firewall-networking skill) and RBAC as policies (access-control skill).
• Images stay literal, sizing is minimal. image: nginx:1.25 is kept verbatim, not rewritten to an internal //image/ ref. imagePullSecrets (pod or ServiceAccount) carry over as //secret/NAME, but the secret must already exist for a private registry to pull. A container with no resources set defaults to a tiny 50m CPU / 128Mi memory — size it for production.

Docker Compose (cpln stack)

cpln stack deploy (alias up) builds and deploys; cpln stack manifest previews the generated YAML without deploying; cpln stack rm (alias down) tears down. All take --dir/--directory and --compose-file. --build defaults true for deploy (local docker build for linux/amd64, then push as <service>:1.0) and false for manifest. Conversion rules:

• Workload type: standard, or stateful if the service attaches a named volume.
• Volumes: a named volume becomes a VolumeSet (stateful). A file bind mount becomes an opaque secret mounted at the target path. A directory bind mount is rejected with an error — split it into individual file bind mounts.
• Ports: "PORT[:TARGET]/PROTO" where PROTO is http, http2, tcp, or grpc; with no /PROTO suffix the port has no protocol set. Example: "50051:50051/grpc".
• Resources: default cpu: 42m, memory: 128Mi (override via deploy.resources.limits). A GPU forces a minimum cpu: 2000m, memory: 7168Mi.
• Firewall: external inbound is opened (0.0.0.0/0) when the service has ports or network_mode: host; outbound is open unless network_mode: none.
• Secrets/configs: compose secrets and configs become opaque secrets with an auto-created identity and a reveal policy.

x-cpln override block: any top-level key under a service's x-cpln replaces that entire spec.<key> section wholesale (it does not deep-merge) — there is no allowlist, so any spec field works (type, containers, defaultOptions, firewallConfig, identityLink, …). Overriding containers means restating the full container spec.

services:
  api:
    image: my-api:latest
    x-cpln:
      type: serverless              # replaces the derived workload type
      defaultOptions:               # replaces the whole defaultOptions block
        capacityAI: false
        autoscaling: { minScale: 2, maxScale: 10 }

cpln stack does not rewrite service URLs in your code or config. Update them to the internal form <workload>.<gvc>.cpln.local[:<port>] (e.g. http://redis:6379 becomes http://redis.GVC.cpln.local:6379).

Bind-mounts: content vs config

The decisive question for any bind-mounted file: does it change between environments, or is it identical in dev/staging/prod?

Baking config into the image couples that image to one environment: a hostname or feature-flag change then forces a rebuild. Mounting application content as secret volumes is the opposite mistake — it decouples content from its image version and makes rollbacks strange. A single container's migration is usually mixed, decided file by file. Rule of thumb: if changing the file between environments would not count as a code change, it is config and belongs in a secret volume.

Workloads mount secrets as read-only files via cpln://secret/<name> volumes (the workload/stateful-storage skills own the mechanism; get_resource_schema for workload gives the exact shape):

• Opaque (single config file): the path needs at least one subpath; the last segment becomes the file name and holds the payload.
• Dictionary (multi-key ConfigMap): mount the secret at a directory path and each key becomes a file.
• Docker / GCP / Azure SDK: mounted as a single file ___cpln___.secret in the path directory.

So an nginx workload migrates mixed: index.html baked into a custom image, while nginx.conf mounts from a cpln://secret/<name> opaque secret at /etc/nginx/conf.d/default.conf.

Helm (cpln helm)

cpln helm install|upgrade|uninstall|list|template manages releases of charts that render only Control Plane kinds. A rendered object carrying apiVersion or metadata, or an unknown kind, aborts with ERROR: Some resources in the rendered template are not CPLN resources. To migrate an existing Kubernetes Helm chart, render it first and pipe through the converter: helm template R ./chart | cpln convert -f -.

• cpln.org and cpln.gvc (plus globals.cpln.* / global.cpln.*) are injected as --set overrides — don't define a top-level cpln key in values.yaml, it gets clobbered.
• Release state is an opaque secret per revision; cpln helm list is org-scoped and takes no --gvc.
• GVC-scoped kinds (workload, identity, volumeset) need --gvc or a profile GVC; org-scoped kinds like domain do not.

Release-name rules, --history-limit, OCI charts, and --wait are general helm-release operations — the gitops-cicd and cpln skills own those.

Exporting to Terraform / IaC

When the target is Infrastructure-as-Code rather than live resources, turn the converted Control Plane YAML into HCL with mcp__cpln__convert_to_terraform (dry-run validated against the API first, so the HCL always matches a schema-valid resource), or capture already-created resources with mcp__cpln__export_terraform. mcp__cpln__list_terraform_kinds and mcp__cpln__export_terraform_batch are in the full profile. The iac-terraform-pulumi skill owns the full Terraform/Pulumi story, including terraform import.

Verify

• After cpln convert: confirm each workload's derived type, scaling (maxScale raised where needed), port protocols (gRPC/HTTP2), ingress-to-domain routes, and that any {{GVC}} placeholder is replaced.
• After create/apply: cpln apply -f cpln.yaml --ready, or poll mcp__cpln__list_deployments until each workload reports ready. Pair every mutation with a read.

Troubleshooting

Quick reference

MCP tools

• mcp__cpln__create_workload / create_gvc / create_secret_opaque (and the other create_secret_<type>) / create_identity / create_volumeset — author converted resources with production-grade defaults
• mcp__cpln__get_resource_schema — exact shape before hand-editing or re-authoring a converted manifest
• mcp__cpln__list_deployments — poll converted workloads to ready
• mcp__cpln__convert_to_terraform / mcp__cpln__export_terraform — converted YAML or live resources to HCL (iac-terraform-pulumi skill)

The converters themselves (cpln convert, cpln stack, cpln helm, cpln apply --k8s) are CLI-only. In CI/CD, CPLN_TOKEN + cpln apply -f applies the converted manifest headlessly.

Related skills

Documentation

• cpln convert
• Compose Deploy
• cpln helm
• cpln apply
• Workload Volumes