SUMMARIZATION

Audience-split release notes: route customer-facing vs internal changes

On each release, this classifies merged PRs as customer-facing or internal, then writes two tailored summaries.

CategorySummarization
EngineSim + Paperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitHub release publishedGitHubGitHub
  • ActionFetch merged PRs with labelsGitHubGitHub
  • ActionClassify each PR: customer-facing vs internalOpenAI
  • LogicBranch into customer and internal buckets
  • ActionPublish customer changelog note to IntercomIntercomIntercom
  • OutputPost technical recap as Linear updateLinearLinear

What it does

One release, two audiences. The workflow reads the PRs in a release, decides which changes customers will notice versus which are internal-only, and produces two separately-toned summaries: a friendly, jargon-free note for customers and a precise technical recap for the engineering org.

When to use it

When the same release needs both a public-friendly announcement and an internal record, and you don't want one team's noise leaking into the other's view. Great for product-led teams shipping continuously.

How it works

  1. 1A GitHub release-published event triggers the flow.
  2. 2It pulls the merged PRs for the release with their labels and descriptions.
  3. 3An OpenAI step classifies each PR as customer-facing or internal based on labels and impact.
  4. 4A logic branch splits the set into two buckets.
  5. 5The customer-facing bucket is rewritten as a warm changelog note and posted to Intercom.
  6. 6The internal bucket is summarized technically and written as a Linear project update.

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 IntercomConversations, contacts, articles.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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