ENGINEERING

Detect Sentry regressions and reopen the original Linear bug

When Sentry marks a resolved issue as regressed, finds the Linear bug previously linked to that fingerprint, reopens it, and notifies the owning team's Slack channel instead…

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry issue-regressed eventSentrySentry
  • LogicLook up closed Linear bug by fingerprintLinearLinear
  • LogicBranch: reopen existing vs. create new
  • ActionReopen Linear bug + add regression commentLinearLinear
  • ActionResolve owning team from CODEOWNERSGitHubGitHub
  • OutputNotify owning team's Slack channelSlack

What it does

Catches the specific case where an error you already fixed comes back. Instead of opening a fresh duplicate, it reopens the exact Linear bug that was closed for that Sentry fingerprint, flags it as a regression, and alerts the owning team in Slack.

When to use it

Use it when regressions are slipping through as brand-new tickets, losing the prior investigation history. Best for teams that close Linear bugs when Sentry issues resolve and want the full timeline preserved on a single ticket across the fix-regress cycle.

How it works

  1. 1Sentry emits a regression event when a previously resolved issue starts firing again.
  2. 2The flow extracts the fingerprint and looks up the matching closed Linear issue.
  3. 3A branch decides: if a prior issue exists it reopens it; if not, it creates a fresh bug.
  4. 4On reopen it posts a regression comment with the new occurrence count and Sentry link.
  5. 5It resolves the owning team from CODEOWNERS and pings that team's Slack channel with the reopened ticket.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  3. 3
    Connect GitHubRepos, issues, pull requests, actions.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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