AI & RAG

Scheduled Confluence migration-runbook generator from new releases

On a schedule, finds API versions released since the last run, retrieves their changelog breaking changes.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled run (e.g. nightly)
  • ActionFetch recent release tags and changelog from GitHubGitHubGitHub
  • LogicFilter to versions released since last runbook revision
  • ActionRetrieve breaking-change passages for new versionsOpenAI
  • ActionWrite per-version migration sections grounded in diffsOpenAI
  • OutputCreate or update the Confluence migration runbookConfluenceConfluence

What it does

This workflow runs on a schedule, detects any API versions published since its last run, retrieves the breaking changes and deprecations from each release's changelog, and writes a structured migration runbook into Confluence, keeping a living upgrade guide that always cites the exact version and diff.

When to use it

Use it when your team maintains a Confluence migration guide by hand and it constantly drifts behind actual releases. This keeps the runbook current automatically and traceable to source.

How it works

  1. 1A schedule triggers the workflow (for example nightly).
  2. 2The workflow fetches recent release tags and CHANGELOG entries from GitHub.
  3. 3A logic step filters to versions released after the last published runbook revision.
  4. 4It retrieves the breaking-change and deprecation passages for those versions.
  5. 5An OpenAI model writes per-version migration sections grounded in the retrieved diffs, citing each.
  6. 6The workflow creates or updates the Confluence runbook page with the new cited sections.

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 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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