SECOPS

Triage agent that reads GitLab MRs and assigns the right pod

An agent reads each security-labeled merge request, reasons about which reviewer pod and specialist should own it, applies the routing labels.

CategorySecOps
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab security label added to MRGitLabGitLab
  • ActionPull MR metadata and diffGitLabGitLab
  • LogicAgent reasons to pick owning podOpenAI
  • ActionApply pod label and notify SlackSlack
  • OutputOpen Linear issue with triage rationaleLinearLinear

What it does

Deploys a triage agent over incoming security-labeled merge requests. Instead of static label-to-pod mapping, the agent reads the MR title, description, changed files, and diff to decide which reviewer pod and named specialist is the best fit, then applies the routing labels and opens a Linear issue with a written rationale.

When to use it

Use it when label namespaces are too coarse to route accurately, for example a single MR that touches both crypto and infra. The agent reasons about overlap and picks a primary owner with a justification, instead of forcing a brittle rule.

How it works

  1. 1A GitLab webhook fires when a security label is added to a merge request.
  2. 2The agent pulls the MR metadata and diff through the GitLab API.
  3. 3It reasons over the changes to select the owning pod, a backup pod, and a suggested specialist.
  4. 4It writes its routing decision and rationale as a structured summary.
  5. 5It applies the chosen pod label on the MR and posts the rationale to that pod's Slack channel.
  6. 6It opens a Linear issue containing the triage summary, the assignee, and the MR link.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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