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AI for GitLab CI/CD Difficulty: Advanced ClaudeChatGPT

GitLab CI/CD Merge Train Throughput & Failure Recovery Prompt

Tune an existing merge train so it merges MRs faster, recovers cleanly from a failing train member, and stops thrashing during peak hours.

Target user
Maintainers operating merge trains on a busy default branch
Difficulty
Advanced
Tools
Claude, ChatGPT

The prompt

You are a senior delivery engineer who specializes in GitLab merge trains and merge-throughput tuning.

I will provide:
- Whether merge trains and merged results pipelines are enabled
- Average pipeline duration and MR volume per day
- Symptoms (trains restarting, long queues, frequent train drops)
- My `interruptible:` and `workflow:rules` setup

Your job:

1. **Model the train** — explain how a failing MR forces re-runs of every MR behind it, and quantify the throughput cost.
2. **Speed the critical path** — recommend `interruptible: true` on safe jobs, `needs:` DAG ordering, and `auto-cancel` so superseded pipelines stop fast.
3. **Train-only fast lane** — show how to use `$CI_MERGE_REQUEST_EVENT_TYPE == "merge_train"` in `rules` to skip non-essential jobs on the train.
4. **Failure recovery** — define a policy for when to remove a failing MR from the train vs retry, and how `allow_failure` should NOT mask train-breaking jobs.
5. **Concurrency** — tune train parallelism and runner capacity to avoid starvation.
6. **Guardrails** — protect against flaky tests dropping good MRs.

Output as: (a) the tuned `.gitlab-ci.yml` rules/needs snippets, (b) a runbook for a stuck or thrashing train, (c) before/after throughput estimate.

Highlight any change that lets unverified code merge to the default branch, and give a back-out to disable merge trains.
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