ENGINEERING

Auto-triage dependency-bump PRs by license risk

When an automated dependency-bump PR opens, an agent assesses the license of the changed package.

CategoryEngineering
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDependency-bump pull request openedGitHubGitHub
  • ActionResolve previous and new SPDX licenses for changed packageHTTP webhook
  • LogicAgent reasons over license delta and assigns risk tierOpenAI
  • LogicRoute low-risk vs. escalation
  • OutputAuto-approve and label, or request human review with rationaleGitHubGitHub

What it does

Reduces noise from automated dependency-update PRs by triaging them on license risk. When a bump PR opens, an agent reads the dependency change, resolves the old and new licenses, and reasons about the delta. If the license is unchanged and on the allowlist, it labels the PR low-risk and approves it; if the license changed, became copyleft, or is unrecognized, it labels it for human review and explains why in a comment.

When to use it

Use it on repositories flooded with bot-generated dependency PRs where most are safe rubber-stamps but a few carry hidden license risk. It lets reviewers focus only on the bumps that actually changed legal exposure.

How it works

  1. 1An automated dependency-bump pull request opens.
  2. 2The agent fetches the changed dependency and resolves both the previous and new SPDX licenses.
  3. 3It reasons over the license delta against policy to assign a risk tier.
  4. 4A branch routes low-risk bumps to auto-approve-and-label and risky ones to escalation.
  5. 5It either approves the PR with a low-risk label or posts a review-requested comment explaining the license concern.

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 HTTP webhookTrigger any URL on agent actions.
  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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