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A formatter that commits changes can keep triggering its own CI workflow when a second formatter undoes those changes. In a reported incident, that disagreement sent a branch through roughly 900 runs overnight, delayed pull-request checks, and occupied shared runners. The durable fix was to make formatting a check that reports a diff, rather than a job that writes and pushes one.
How the formatter disagreement became a CI loop
Sergey Shinder’s incident account says the autoformatting job had run normally for five days. On a Wednesday morning, pull-request checks were queued for about 50 minutes, while the runner pool had been occupied since around 10:30 the previous night. Shinder traced the problem to one pull request containing a construct where two formatters disagreed about a trailing comma.
One formatter added the comma and committed its output. That push started another run; the other formatter removed the comma and pushed again. The next run reversed the change once more. Because the output kept alternating instead of settling on one version, the workflow had no stable result that would stop another push.
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Shinder estimated that the workflow ran roughly 900 times overnight, at about two minutes per run. Those are figures from the author’s account, not independently audited measurements. The shared, first-come, first-served runner pool also served production deployment, pull-request checks, and nightly jobs, so the loop delayed unrelated work. Read Shinder’s incident account on Dev.to.
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Why a push can repeat the work
A workflow becomes vulnerable when it both writes to a branch and responds to the kind of event its own write creates. If a formatter’s changes are committed and pushed, that push may start the workflow again, depending on the event configuration and credentials. If another formatter reverses the edit, the two jobs can produce an endless sequence of commits.
This is not an inevitable property of every GitHub Actions push. GitHub says events caused by the repository’s GITHUB_TOKEN generally do not trigger new workflow runs, as a safeguard against recursion. Other credentials can behave differently. Shinder’s account does not state which token or event configuration the job used, so it does not establish why that particular push triggered another run. GitHub documents how workflow events and GITHUB_TOKEN affect triggering.
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Make formatting a check, not a writer
The incident’s primary fix was to change the formatter job so it checked formatting and failed with a diff instead of committing and pushing its output. This breaks the feedback path: the job can show developers what needs correction, but it does not create a new commit that restarts itself.
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For a workflow whose purpose is to enforce formatting, check-only behavior also makes the required change visible in the pull request. The author or contributor can apply the fix, and the next check evaluates that commit. If a project has a separate reason to automate formatting commits, that writer should be designed so its output converges and its trigger behavior is deliberate; the incident account does not prescribe a general configuration for that alternative.
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Use concurrency to limit duplicate work
Shinder also describes adding a concurrency group per branch with cancel-in-progress. GitHub Actions concurrency can control simultaneous workflow or job runs; with cancellation enabled, a newer run can cancel an active run in the same group. This helps limit overlapping work, but it does not by itself fix a formatter disagreement: a succession of new runs can still keep replacing older ones.
Scope the group key to the work you intend to replace—typically including the workflow and branch—rather than using a broad shared name. GitHub notes that workflows using the same group can affect one another, so an overly broad key may cancel unrelated work. See GitHub’s workflow syntax documentation for concurrency behavior.
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The report says the team added two further measures: a condition to skip runs when the actor was its bot account, and an alert for workflow runs per repository per hour. These are additional defenses, not substitutes for removing the feedback loop. An actor condition can avoid reprocessing a bot’s own changes in the cases it covers, while a volume alert can help surface unusual activity; neither guarantees that every loop will be prevented or detected.
Together, the measures address different failure modes: check-only formatting prevents this class of self-generated push, scoped concurrency limits overlapping execution, the actor guard excludes a particular identity, and volume monitoring can flag a spike. The author’s account does not include workflow YAML, so these measures should not be treated as a ready-to-copy configuration.
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Account for shared runner capacity in recovery plans
The incident’s operational impact came from more than wasted formatter runs. Because the runner pool was shared without priority, runaway automation consumed capacity needed by pull-request checks, nightly jobs, and production deployment. A useful safeguard is therefore to monitor workflow volume and understand which workloads share runners, so responders can identify whether an automation loop is blocking work that matters more urgently.
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