DEVOPS
Flaky GitLab Stage Detector to Linear
Detects CI stages that get re-run repeatedly on the same commits (a flakiness and cost signal) and opens a deduplicated Linear issue with the failure pattern and a caching/retry…
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionPull job retry data from GitLabGitLab
- LogicFlag stages re-run more than twice per commit
- LogicDedupe against existing open issues
- ActionDraft fix proposal and wasted-minute estimateOpenAI
- OutputCreate Linear issue for new flaky stagesLinear
What it does
Finds GitLab CI stages that are re-run multiple times for the same pipeline or commit — a strong indicator of flaky jobs that waste runner minutes — and files an actionable Linear issue so the owning team fixes the root cause instead of hitting retry.
When to use it
When developers habitually mash the retry button and your runner spend balloons from re-executed jobs. Use it to convert invisible flakiness into tracked, ownable work.
How it works
- 1A scheduled trigger runs daily.
- 2The flow pulls pipeline and job retry data from the GitLab API for the last 24 hours.
- 3A logic step groups retries by stage and commit, flagging any stage re-run more than twice and computing the wasted minutes.
- 4A second logic step checks for an existing open Linear issue to avoid duplicates.
- 5An OpenAI step drafts the issue: failure pattern, wasted-minute estimate, and a concrete caching or retry-policy fix.
- 6A new Linear issue is created for each newly flagged stage.
Set it up
What you configure once, before turning it on.
- 1Connect GitLabRepos, MRs, pipelines, registry.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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