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AI for Infrastructure as Code Difficulty: Advanced ClaudeChatGPTCursor

Crossplane Composition Revision Rollout & Pinning Prompt

Roll out changes to a Crossplane Composition safely across many live claims — using CompositionRevisions, revision pinning, and automatic-vs-manual update policies so a bad composition change cannot ripple into every managed resource at once.

Target user
Platform engineers running Crossplane control planes at scale
Difficulty
Advanced
Tools
Claude, ChatGPT, Cursor

The prompt

You are a senior platform engineer who operates a Crossplane control plane where dozens of teams consume XRs/claims backed by a shared Composition. Every edit you make to a Composition can immediately reconcile against every claim that references it, so you treat composition changes like a production deployment with staged rollout, not a git push.

I will provide:
- The XRD/Composition names, the provider(s) involved, and roughly how many live claims consume it
- The change I want to make to the Composition (new patch, added managed resource, provider version bump, function pipeline edit)
- Whether claims currently use `compositionUpdatePolicy: Automatic` or `Manual`, and whether `compositionRevisionRef`/`compositionRevisionSelector` is set
- The blast-radius constraints: which environments/claims are canary-eligible and which are protected production

Your job:

1. **Revision model** — explain how editing a Composition mints a new `CompositionRevision`, how `Automatic` vs `Manual` `compositionUpdatePolicy` on each claim decides whether it jumps to the new revision, and how `compositionRevisionRef` pins a claim to a specific revision. Make the rollout mechanics explicit.

2. **Staged rollout plan** — design a canary: pin the fleet to the current revision first (so the edit does NOT auto-apply everywhere), publish the new revision, move a canary claim/environment to it, observe, then progressively promote. Give the exact `compositionUpdatePolicy`/`compositionRevisionRef` settings for each stage.

3. **Replace-vs-update analysis** — identify which fields in the change are immutable at the provider level and would cause managed resources to be **deleted and recreated** (data loss risk) versus updated in place. Flag every replace-triggering field before rollout.

4. **Observability gate** — the `kubectl` checks that prove a canary is healthy before promotion: XR `Synced`/`Ready` conditions, managed-resource readiness, and `compositionRevisionSelector`/`Ref` on each claim. Give copy-pasteable commands.

5. **Rollback** — how to demote the fleet back to the previous `CompositionRevision` by revision number/label, and why keeping old revisions (not garbage-collecting them) is what makes rollback instant.

6. **GitOps integration** — how to express revision pinning and staged promotion in an Argo CD/Flux-managed repo so the rollout is auditable and reversible via git, not imperative kubectl.

Output as: (a) the revision/update-policy mechanics explained, (b) a stage-by-stage rollout runbook with the exact claim settings per stage, (c) the immutable-field replace-risk list, (d) the readiness verification commands, (e) a one-command rollback procedure. Assume live workloads depend on the managed resources.

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