ENGINEERING

Nightly Flaky-Test Scoring from CI History

Runs nightly to score every test by its pass/fail flip rate over the last 30 days, flags tests above a flakiness threshold.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule
  • ActionQuery 30-day test outcomesPostgreSQLPostgres
  • LogicScore flip rate and apply threshold
  • ActionUpsert quarantine registryPostgreSQLPostgres
  • OutputOpen or refresh GitHub issue per offenderGitHubGitHub

What it does

Each night this workflow analyzes recent CI results to compute a flakiness score per test — how often it flips between pass and fail without code changes. Tests exceeding the threshold are added to a Postgres quarantine registry that your test runner reads, and a GitHub tracking issue is created or refreshed with the latest score and trend.

When to use it

Use this for data-driven quarantine decisions instead of reacting to single failures. It catches slow-burn flakiness that individual runs miss and keeps the quarantine list current as tests stabilize or degrade.

How it works

  1. 1A nightly schedule triggers the run.
  2. 2The bot queries the Postgres results table for the last 30 days of test outcomes.
  3. 3A scoring step computes flip rate and total runs per test, then flags any test over the configured flakiness threshold and de-flags tests that have gone quiet.
  4. 4It upserts the quarantine registry table so the test runner can skip flagged tests automatically.
  5. 5For each newly flagged test it opens or updates a GitHub issue with the score, run count, and a 30-day trend summary.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  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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