AI & RAG

PR Reviewer That Flags Decisions Against ADRs

On every pull request, retrieves the ADRs touching the changed areas and posts a review comment when the diff appears to contradict an accepted decision.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPull request opened or updatedGitHubGitHub
  • ActionFetch changed files and diffGitHubGitHub
  • ActionRetrieve ADRs governing the touched areasConfluenceConfluence
  • ActionDetect diff vs accepted-ADR conflictsOpenAI
  • LogicSkip when no accepted ADR is implicated
  • OutputPost citing review comment on the PRGitHubGitHub

What it does

Guards your architecture at review time. When a pull request opens, it figures out which Architecture Decision Records govern the changed files, then checks whether the diff conflicts with any accepted decision. If it finds a likely violation, it leaves a PR comment quoting the relevant ADR and explaining the conflict so the author can fix it or amend the decision.

When to use it

When accepted ADRs keep getting silently broken — a banned dependency creeps back, a deprecated pattern returns, a data-residency rule is ignored. Best for teams that want enforcement without a human gatekeeper reading every diff.

How it works

  1. 1A pull request is opened or updated in GitHub.
  2. 2The changed file paths and diff are pulled for analysis.
  3. 3Matching ADRs are retrieved from the GitHub docs folder or Confluence, scoped to the touched areas.
  4. 4OpenAI compares the diff against each accepted ADR and decides whether a real conflict exists.
  5. 5A logic step suppresses comments when no accepted ADR is implicated.
  6. 6If a conflict is found, a review comment is posted citing the ADR number and the contradicting lines.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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