ENGINEERING

Match Sentry spikes to open GitHub issues before filing in Linear

On a Sentry spike, searches open GitHub issues for a matching error signature first; if a tracking issue already exists it links and comments there, otherwise it opens…

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry spike alertSentrySentry
  • ActionSearch open GitHub issues by error signatureGitHubGitHub
  • LogicBranch: GitHub match vs. no match
  • ActionComment spike data on matched GitHub issueGitHubGitHub
  • ActionResolve owning team from CODEOWNERSGitHubGitHub
  • OutputCreate/update deduplicated Linear bugLinearLinear

What it does

Prevents the Sentry-to-Linear pipeline from duplicating work that's already tracked in GitHub Issues. On a spike, it first looks for an open GitHub issue whose title or body matches the error signature. If found, it comments the new spike data there; if not, it falls through to creating a deduplicated Linear bug assigned via CODEOWNERS.

When to use it

Use it when your team tracks some work in GitHub Issues and some in Linear, and you don't want a Linear ticket spun up for an error an engineer is already handling in a GitHub issue. Best for repos in a hybrid tracking setup.

How it works

  1. 1A Sentry spike alert fires for an issue.
  2. 2The flow builds a search signature from the error type and culprit and queries open GitHub issues.
  3. 3A branch checks for a match: if a GitHub issue exists, it comments the spike count and Sentry link there and stops.
  4. 4If no GitHub match, it searches Linear by fingerprint to avoid duplicating there too.
  5. 5It resolves the owning team from CODEOWNERS and creates or updates the Linear bug accordingly.

Set it up

What you configure once, before turning it on.

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

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