SUMMARIZATION

Capture MR Architecture Decisions into a Confluence ADR

When an MR labeled architectural is merged, distills the design discussion in the thread into a structured Architecture Decision Record and publishes it to Confluence.

CategorySummarization
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab MR merged eventGitLabGitLab
  • LogicProceed only if MR has architecture label
  • ActionFetch full MR discussion threadGitLabGitLab
  • ActionStructure discussion into an ADROpenAI
  • ActionPublish ADR page to ConfluenceConfluenceConfluence
  • OutputPost ADR link as note on the MRGitLabGitLab

What it does

This workflow watches for merged MRs tagged as architecturally significant, reads the design debate inside the thread, and distills it into a proper Architecture Decision Record: context, options considered, the decision, and consequences. It publishes the ADR to your Confluence space and drops a link back on the MR, turning ephemeral review chatter into durable documentation.

When to use it

Use it when meaningful design decisions get made in MR comments and then lost. It gives architecture-track teams an automatic ADR trail without anyone hand-writing one after the fact.

How it works

  1. 1GitLab MR merged event triggers the run.
  2. 2A logic step proceeds only if the MR carries the architecture label.
  3. 3The workflow fetches the full discussion thread.
  4. 4OpenAI structures the conversation into an ADR with context, considered options, decision, and consequences.
  5. 5The ADR is published as a new Confluence page in the decisions space.
  6. 6A note linking the ADR is posted on the merge record.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  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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