Find your flakiest tests and turn them into tickets, not retries
Flaky tests rot CI from the inside. Engineers re-run jobs, miss real failures, and stop trusting the pipeline until the only signal is "did the third try pass?" This recipe scans recent GitHub Actions and CircleCI runs, ranks tests by flake rate, identifies suspect commits, and creates Linear tickets with reproduction context.
Find and triage flaky tests across CI/CD pipelines for a DevOps engineer. Goal: Help me find the tests causing the most CI noise and turn them into tickets with enough context that they can be fixed, not just retried. Ask me for: - Lookback window in days (default 30) - CI providers in use (GitHub Actions, CircleCI, both) - Repos to scope the audit to - Flake threshold (e.g. failed at least 3 times while the same commit eventually passed) - Whether to create Linear tickets for top offenders Use available integrations this way: - GitHub: pull GitHub Actions run history, job logs, and test reports - CircleCI: pull pipeline history and test failure metadata - Linear: create tickets for the worst offenders with reproduction notes - Slack: post a summary to the team channel - Google Docs: write the full report Output: 1. Top 20 flakiest tests ranked by flake rate, frequency, and time-cost 2. For each test: failure history, suspect commits, last seen passing 3. Tests that flaked exactly once (likely real bugs, not flakes) 4. Linear tickets for the worst offenders with logs linked 5. A Slack summary for the team channel 6. The full report in Google Docs Rules: - Do not quarantine or skip tests directly; produce recommendations only - Show the failure logs for top offenders, not just the test name - Distinguish flaky tests from tests that fail on a specific environment - Do not propose deleting a test without a documented reason - If a test belongs to another team's repo, route the ticket to them
This recipe quantifies CI flake instead of leaving it to vibes. It
pulls recent CI runs, identifies tests that fail and later pass on
the same commit, ranks them by flake rate and developer time lost,
and turns the worst offenders into tickets with enough context to fix.
Hand off the pager without making the next engineer reconstruct the shift
On-call handoffs usually happen fast, right when context is easiest to lose. The next engineer starts their shift digging through incidents, deploys, noisy alerts, and half-finished Slack threads just to understand the current state. This recipe pulls the shift's PagerDuty incidents, deploy activity, Datadog alerts, and open Slack threads into a clean handoff brief the next on-call can use immediately.
Turn rough incident notes into a blameless postmortem with shippable follow-ups
Postmortems often start as rushed incident notes and stay messy until review time. This recipe takes a rough draft, removes blameful language, fills timeline gaps from PagerDuty and Slack, turns vague follow-ups into concrete Linear tickets, and schedules the review.
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