ENGINEERING

Breaking-Change to Linear Ticket Splitter

On a major version bump PR, extracts each breaking change from the changelog and opens one Linear ticket per affected internal module so the migration can be split across the team.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMajor version bump PR openedGitHubGitHub
  • ActionExtract breaking changes from the changelogOpenAI
  • LogicMatch breaking APIs to affected internal modules
  • ActionDraft a per-module migration descriptionOpenAI
  • OutputOpen one Linear ticket per affected moduleLinearLinear

What it does

This workflow takes a major-version upgrade and breaks it into trackable work. It reads the changelog for breaking changes, finds which internal modules call the affected APIs, and creates a discrete Linear ticket for each module that needs migration work, linked back to the upgrade PR.

When to use it

Use it when a major bump (say React 18 to 19, or a framework jump) is too large for one PR review and needs to be parceled out as real migration tasks. Ideal for teams that plan upgrades as sprint work rather than merging blindly.

How it works

A GitHub trigger fires on a PR tagged as a major version bump. The flow pulls the upstream changelog and isolates the breaking-changes section. A logic step matches each breaking API against repo usage to determine which modules are touched, dropping changes that affect nothing you use. The model writes a focused migration description per affected module: what broke, the call sites, and the suggested fix. For each one, a Linear issue is created with the module as title context, the PR linked, and an effort estimate, so the upgrade becomes a planned, distributable backlog.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect OpenAIModels, embeddings, files.
  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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