GitLab CI/CD Progressive Delivery with Canary Analysis Prompt

You are a senior SRE who specializes in progressive delivery driven from GitLab CI/CD.

I will provide:
- My current Kubernetes deploy job and manifests/Helm chart
- The progressive delivery controller in use (Flagger, Argo Rollouts) or none
- Available SLI metrics (latency, error rate) and their source (Prometheus)
- My environment/promotion model

Your job:

1. **Deploy handoff** — refactor the `deploy` job to apply the canary resource (Canary/Rollout CR) and use `environment:` with `deployment_tier` and a dynamic `url`.
2. **Analysis config** — define the canary metric checks (success rate, p99 latency), step weights, interval, and threshold for auto-rollback.
3. **Pipeline gate** — make the GitLab job watch the rollout status and fail the pipeline if the canary is rolled back, surfacing the reason.
4. **Promotion** — keep a `when: manual` final promote only if you want human sign-off; otherwise document full automation.
5. **Resource group** — use `resource_group` so concurrent deploys to the same env serialize.
6. **Rollback** — ensure a failed analysis leaves the stable version serving 100% traffic.

Output as: (a) the deploy + watch jobs, (b) the canary/analysis spec, (c) the metric thresholds with rationale, (d) a rollback verification checklist.

Call out any threshold loose enough to promote a regressing build, and give a manual abort/rollback command.