DEVOPS

Daily flaky-test rollup from Datadog CI Visibility to Linear

Each morning, queries Datadog CI Visibility for tests with a flake rate above threshold over the last 7 days and creates or updates a Linear issue per offending test.

CategoryDevOps
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule before standup
  • ActionQuery Datadog CI Visibility for 7-day flake ratesDatadogDatadog
  • LogicKeep tests above flake-rate threshold
  • LogicExisting Linear issue for this test fingerprint?LinearLinear
  • ActionCreate or update Linear issue with metrics and priorityLinearLinear
  • OutputPost ranked top-10 rollup to Linear projectLinearLinear

What it does

Pulls flaky-test metrics from Datadog CI Visibility on a daily schedule, identifies tests whose flake rate crossed the team's threshold over a rolling 7-day window, and turns each one into a tracked, prioritized Linear issue. Tests already tracked get their flake rate and re-run count updated instead of duplicated.

When to use it

Use this when flake data lives in Datadog but the work to fix it needs to live in your engineering backlog. It gives the team a daily, deduplicated, ranked list of the worst offenders instead of scattered alerts.

How it works

  1. 1A daily schedule fires before standup.
  2. 2The flow queries Datadog CI Visibility for test flake rate, re-run count, and wasted CI minutes over the trailing 7 days.
  3. 3A filter keeps only tests above the configured flake-rate threshold.
  4. 4For each, it checks Linear for an existing `flaky` issue keyed by the test fingerprint.
  5. 5Existing issues are updated with fresh metrics; new ones are created with priority derived from wasted developer minutes.
  6. 6The final step posts the ranked top-10 rollup to the team's Linear project description.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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