DOCUMENT OPS

Snapshot a Figma file version to a Dropbox archive on publish

When a named version is published in Figma, this workflow archives a full snapshot of the file's frames and assets to a versioned Dropbox folder and logs the release in a Coda…

CategoryDocument Ops
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFigma named version publishedFigmaFigma
  • ActionExport frames and assets at published version from FigmaFigmaFigma
  • ActionWrite diff summary vs previous version with AIOpenAI
  • ActionUpload version-stamped snapshot to Dropbox archiveDropboxDropbox
  • ActionAppend release row to Coda changelogCodaCoda
  • OutputReturn changelog entry linkCodaCoda

What it does

Creates a durable, browsable archive of each released design. The instant a designer publishes a named version in Figma, it captures the frames and assets into a version-stamped Dropbox folder and records the release in a Coda changelog so anyone can trace what shipped and when.

When to use it

Use it when you need an auditable history of design releases outside Figma — for compliance, client deliverables, or simply so a rolled-back Figma file never erases the record of what was handed off. Best for teams that publish named versions deliberately.

How it works

  1. 1A Figma webhook fires when a named version is published.
  2. 2The workflow exports the file's frames and assets at the published version.
  3. 3OpenAI writes a short diff summary describing what changed since the previous archived version.
  4. 4It uploads the export into a version-stamped folder in the Dropbox archive.
  5. 5It appends a changelog row in Coda with the version label, date, Dropbox link, and diff summary.
  6. 6The changelog entry link is returned as the release record.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FigmaFiles, frames, comments, assets.
  2. 2
    Connect DropboxFiles and folders.
  3. 3
    Connect CodaDocs, packs, automations.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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