ENGINEERING

Daily Sentry cluster digest with batched GitLab issue creation

Runs each morning, groups the day's unresolved Sentry clusters by suspected owning team using blame data, and creates one batched GitLab issue per team plus a Slack summary.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled morning run
  • ActionList 24h unresolved Sentry clustersSentrySentry
  • ActionMap top frames to owning team via blame + CODEOWNERSGitLabGitLab
  • LogicGroup and rank clusters per team
  • ActionCreate one batched GitLab issue per teamGitLabGitLab
  • OutputPost cross-team digest to SlackSlack

What it does

Each morning it sweeps the unresolved Sentry issues from the last 24 hours, attributes each one to a likely owning team using GitLab blame and CODEOWNERS, and groups them. It then creates a single rolled-up GitLab issue per team listing their clusters by frequency, and posts a digest to Slack so leads see the day's error landscape at a glance.

When to use it

Use it when per-error issues create too much noise and you'd rather give each team one prioritized daily worklist. Good for organizations with multiple squads sharing one Sentry project who need ownership-based routing instead of a flat firehose.

How it works

  1. 1A scheduled trigger runs every morning.
  2. 2The flow lists unresolved Sentry issues from the trailing 24 hours with event counts.
  3. 3For each, it reads the top frame and maps the file to an owning team via GitLab blame and CODEOWNERS.
  4. 4It groups clusters by team and sorts each group by frequency.
  5. 5It creates one batched GitLab issue per team with the ranked cluster list.
  6. 6It posts a cross-team digest to Slack linking every issue created.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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