ENGINEERING

Flaky Test Detector to ClickUp Tech-Debt Item

Watches GitHub Actions for tests that fail then pass on re-run, confirms the flakiness, and opens a tracked tech-debt task in ClickUp so the intermittent failure does not get lost.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerwebhook
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitHub Actions workflow_run completedGitHubGitHub
  • ActionFetch job attempts and per-test resultsGitHubGitHub
  • LogicKeep tests that failed then passed on retry
  • ActionSearch ClickUp for existing flaky-test taskClickUpClickUp
  • LogicSkip if a task already tracks this test
  • OutputCreate ClickUp tech-debt task with run linkClickUpClickUp

What it does

It catches tests that fail intermittently in CI and turns each one into a tracked piece of tech debt. When a GitHub Actions run reports a test that failed on one attempt but passed on a retry, the workflow flags it as flaky and files a ClickUp task with the test name, the failing job, and a link to the run.

When to use it

Use it when your team re-runs red builds until they go green and the underlying flaky tests never get fixed. This stops the silent erosion of CI trust by making every intermittent failure visible and owned.

How it works

  1. 1A GitHub Actions workflow_run completes and fires a webhook into the workflow.
  2. 2The workflow pulls the run's job attempts and compares per-test results across retries.
  3. 3A logic step keeps only tests that failed on an early attempt and passed later in the same run (the flaky signature).
  4. 4For each confirmed flaky test it checks ClickUp for an existing open task to avoid duplicates.
  5. 5If none exists, it creates a ClickUp tech-debt task tagged "flaky-test" with the test path, owning team, and run link.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect ClickUpDocs + tasks + chats in one workspace.
  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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