SUMMARIZATION

Sentry error spike to deduplicated Linear issue

When Sentry alerts on an error spike, clusters the related error groups by root cause and opens a single Linear issue with a plain-English summary.

CategorySummarization
Enginesim
Difficultyintermediate
Triggerwebhook
Steps7
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry metric alert webhookSentrySentry
  • ActionFetch contributing error groups and tracesSentrySentry
  • LogicCluster groups by root-cause signature
  • ActionSearch Linear for existing issue by signatureLinearLinear
  • ActionWrite plain-English regression summaryOpenAI
  • LogicBranch: comment on match or create new
  • OutputCreate or update Linear issueLinearLinear

What it does

Converts a Sentry spike alert into exactly one well-written Linear issue instead of a pile of duplicate tickets. It pulls the error groups behind the spike, clusters them by root-cause signature, summarizes the failure in plain English, and either creates a new Linear issue or comments on the existing one for that root cause.

When to use it

When on-call keeps filing near-identical bugs for the same underlying break, or when alert noise turns into ticket noise. Use it to keep one canonical issue per root cause with a human-readable description.

How it works

  1. 1A Sentry metric-alert webhook fires when an error rate crosses threshold.
  2. 2Fetch the contributing error groups and their stack traces from Sentry.
  3. 3Cluster them by root-cause signature to find the single underlying cause.
  4. 4Search Linear for an open issue already tagged with that signature.
  5. 5An LLM writes a regression summary with impact, first-seen, and suspect commit.
  6. 6If a match exists, comment with the update; otherwise create a new Linear issue.

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 OpenAIModels, embeddings, files.
  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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