ENGINEERING

Cluster Dependabot PRs Sharing the Same Dependency Across Repos

Reads open Dependabot PRs from a Postgres mirror of your org's repos, clusters them by the exact package-and-version being bumped.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled run fires
  • ActionQuery open-PR inventory from PostgresPostgreSQLPostgres
  • LogicCluster PRs by identical package + version
  • LogicRank clusters and flag partial rollouts
  • ActionLabel every PR in each clusterGitHubGitHub
  • OutputPost wave-coordination digest to SlackSlack

What it does

When one package (say, a logging library) bumps across twenty microservices, you get twenty near-identical Dependabot PRs. This workflow groups them by the precise package and target version, so reviewers approve a coordinated wave once rather than re-reviewing the same diff twenty times. It surfaces which repos are upgrading together and which are lagging behind.

When to use it

Ideal for orgs with many repos sharing common internal or third-party libraries, where the same dependency upgrade fans out into dozens of duplicate PRs that should move in lockstep.

How it works

  1. 1A schedule triggers the run.
  2. 2A Postgres query reads the org's current open-PR inventory (synced from GitHub) including package name and version per Dependabot PR.
  3. 3A clustering step groups PRs by identical package + target version across repos.
  4. 4A logic step ranks clusters by repo count and flags partial rollouts where some services lag.
  5. 5GitHub applies a shared coordination label to every PR in each cluster.
  6. 6A wave-coordination digest posts to Slack listing each cluster, its repos, and merge order.

Set it up

What you configure once, before turning it on.

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