AI & RAG

Flag breaking changes in inbound SDK PRs against the changelog corpus

When a pull request bumps the SDK dependency version, this reads the changelog corpus between the old and new versions and posts a review comment listing exactly which breaking…

CategoryAI & RAG
EngineSim + Paperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSDK-bump pull request opened or updatedGitHubGitHub
  • LogicExtract old/new SDK version from dependency diff; exit if unchanged
  • ActionRetrieve changelog entries in the version range from corpusPostgreSQLPostgres
  • LogicMatch breaking entries to symbols used in changed files
  • OutputPost review comment with impacted changes and migration stepsGitHubGitHub

What it does

Indexes your published API changelog and migration notes, then watches for pull requests that bump your pinned SDK version. For each bump it retrieves every changelog entry between the old and new version, cross-references the breaking ones against the files the PR touches, and leaves a single review comment naming the risky changes and the lines to check.

When to use it

Use it when your team pins an SDK version and upgrades happen through dependency PRs (Dependabot, Renovate, or manual). It turns "bump and pray" into a reviewed decision before merge.

How it works

  1. 1A pull request opens or updates on GitHub.
  2. 2The flow parses the dependency diff to extract the old and new SDK versions; if no version changed it exits.
  3. 3It queries the embedded changelog corpus in Postgres for all entries in that version range.
  4. 4The agent ranks each breaking entry by whether the PR's changed files use the affected symbols.
  5. 5It posts a GitHub review comment summarizing impacted changes, severity, and migration steps, blocking auto-merge when severity is high.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  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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