ENGINEERING

AI PR summary and risk-based reviewer routing

On each new PR it generates a plain-English change summary and a risk score from the diff, then routes high-risk PRs to senior reviewers and posts the summary as a PR comment.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerwebhook
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPR openedGitHubGitHub
  • ActionFetch diff, title, and descriptionGitHubGitHub
  • ActionGenerate summary and risk rating with OpenAIOpenAI
  • LogicRoute by risk: senior pool vs default rotation
  • ActionAssign reviewers on GitHubGitHubGitHub
  • OutputPost AI summary as PR commentGitHubGitHub

What it does

When a pull request opens, the workflow feeds the diff and description to an LLM that writes a concise summary of what changed and assigns a risk level based on signals like migrations, auth code, or large blast radius. High-risk PRs are routed to a senior reviewer pool; routine ones go to the normal rotation. The generated summary is posted as a comment so reviewers know what they're looking at before reading a line.

When to use it

Use it when reviewers waste time reverse-engineering large or cryptic diffs, or when risky changes (schema, auth, payments) need senior eyes but currently rely on the author remembering to flag them.

How it works

  1. 1A GitHub webhook fires on PR open.
  2. 2The flow retrieves the diff, title, and description.
  3. 3An OpenAI call produces a structured summary plus a low/medium/high risk rating with reasons.
  4. 4A branch checks the risk rating: high routes to the senior reviewer pool, otherwise the default rotation.
  5. 5The flow assigns reviewers on GitHub and posts the AI summary as a PR comment.

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
    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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