ENGINEERING

Route GitLab MRs to reviewer pools by changed-path risk

When a merge request opens, score its changed files against risk path rules, apply the matching security label, and assign reviewers from the correct pool.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab merge request opened or updatedGitLabGitLab
  • ActionFetch changed file paths for the MRGitLabGitLab
  • LogicMatch paths to risk tier (critical / elevated / routine)
  • ActionApply security label and assign matching reviewer poolGitLabGitLab
  • OutputNotify the chosen reviewer pool in SlackSlack

What it does

Classifies every new merge request by which files it touches and routes it to the right reviewers automatically. Touch `auth/`, `crypto/`, `payments/`, or CI config and it gets a `security-review` label plus the security reviewer pool. Everything else lands with the default team. No more security-sensitive diffs slipping through on a generic round-robin.

When to use it

Use it when your repo has clearly higher-stakes directories and you want a guaranteed second set of eyes on them without relying on authors to self-flag. Ideal for teams with a small security pool who only want to be pulled in when it actually matters.

How it works

  1. 1A GitLab webhook fires on merge request `open` and `update`.
  2. 2The flow pulls the MR's changed file paths from the GitLab API.
  3. 3A logic step matches paths against tiered risk rules (critical / elevated / routine).
  4. 4It applies the matching label and assigns reviewers from the corresponding pool via the GitLab API.
  5. 5A Slack message notifies the chosen pool's channel with the MR link and matched risk reason.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  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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