DEVOPS

Dropbox Artifact Retention Review with Approval Gate

Monthly, an agent compiles a deletion candidate list of stale Dropbox artifacts, posts it to Slack for human approval, and only prunes the files an operator explicitly approves.

CategoryDevOps
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps7
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly review schedule fires
  • ActionList artifacts and release tagsDropboxDropbox
  • LogicBuild candidate list with justifications
  • ActionPost proposal to Slack for approvalSlack
  • LogicRoute only approved items forward
  • ActionDelete approved artifactsDropboxDropbox
  • OutputPost audit confirmation to SlackSlack

What it does

Once a month an agent reviews the Dropbox build-artifacts folder, cross-references GitHub release tags, and assembles a proposed deletion list with reasons and reclaimable size per file. Instead of acting on its own it posts the list to Slack with an approval prompt, then prunes only the items an operator confirms. The agent records the outcome for the audit trail.

When to use it

Use it when retention can't be fully automated — for example regulated builds or shared artifacts where a human must sign off before anything is deleted. It keeps a person in the loop while still doing all the tedious analysis.

How it works

  1. 1A monthly schedule starts the review.
  2. 2The agent lists Dropbox artifacts and gathers live GitHub release tags.
  3. 3It reasons over age and release linkage to build a candidate list with justifications.
  4. 4It posts the proposed deletions to Slack and waits for approval.
  5. 5A branch routes only the approved items forward.
  6. 6Delete the approved artifacts from Dropbox.
  7. 7Post a confirmation summary with the audit record to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DropboxFiles and folders.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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