ENGINEERING
Triage Flaky Tests by Datadog Flakiness Rate
On a daily schedule, pulls per-test flakiness rates from Datadog CI Visibility, quarantines any test above a threshold by filing a labeled Linear ticket.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionQuery Datadog CI flakiness ratesDatadog
- LogicFilter tests above flake-rate threshold
- ActionResolve owning team from CODEOWNERSGitHub
- ActionCreate labeled Linear ticket per testLinear
- OutputPost quarantine digest to SlackSlack
What it does
Runs a daily sweep over Datadog CI Visibility flakiness metrics, ranks tests by their flake rate, and quarantines any that cross a configured threshold (e.g. >5% over 50 runs). Each quarantined test gets a Linear ticket labeled `flaky`, assigned to the owning team via CODEOWNERS.
When to use it
Use it when you have Datadog Test Optimization wired into CI and want a metrics-driven, non-reactive quarantine policy rather than chasing individual red builds. It catches slow-burn flakiness that single-run detectors miss.
How it works
- 1A daily schedule trigger kicks off the sweep.
- 2It queries Datadog CI Visibility for per-test flake rates over the trailing window.
- 3A filter keeps only tests above the configured flake-rate threshold.
- 4For each, it resolves the owning team from the repo's CODEOWNERS via the GitHub API.
- 5It creates a Linear ticket with the `flaky` label, flake-rate stats, and Datadog deep links, assigned to that team.
- 6It posts the day's quarantine digest to the engineering Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect DatadogMetrics, traces, log search.
- 2Connect LinearIssues, projects, cycles, triage.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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