PROJECT MANAGEMENT

Page the on-call lead when a late GitHub epic blocks shipping issues

When a GitHub milestone epic passes its due date with open work, finds the issues that depend on it via tracked-by links and pages the on-call engineering lead through PagerDuty…

CategoryProject Management
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled milestone check on repo
  • ActionFind overdue milestones with open issuesGitHubGitHub
  • ActionTraverse tracked-by links to collect blocked downstream issuesGitHubGitHub
  • LogicCompute severity from blocked shipping issues and release proximity
  • OutputOpen a PagerDuty incident to the on-call leadPagerDutyPagerDuty

What it does

Treats an overdue GitHub milestone as a milestone-at-risk signal. When such a milestone still has open issues past its date, the flow resolves which downstream issues are blocked through GitHub's tracked-by relationships and escalates to the engineering on-call so a slip cannot silently sink a release.

When to use it

Use it on release-critical repos where an epic dragging late should trigger an active human decision, not a passive notification. Best when you already run PagerDuty for engineering escalation.

How it works

  1. 1A scheduled check runs against the target repository's milestones.
  2. 2The flow flags any milestone past its due date that still has open issues.
  3. 3For each flagged milestone it traverses tracked-by and blocked-by links to assemble the set of downstream issues that cannot proceed.
  4. 4A branch decides severity from how many shipping-labeled issues are blocked and how close the release tag is.
  5. 5PagerDuty receives an incident with the at-risk epic, the blocked-issue list, and the computed severity routed to the engineering lead.

Set it up

What you configure once, before turning it on.

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

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