CONTENT CREATION

Open a GitHub PR when Figma tokens drift from the committed Tailwind theme

On a schedule, compares Figma variable values against the tokens checked into your repo's Tailwind config and, when they diverge, opens a GitHub pull request with the regenerated…

CategoryContent Creation
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule
  • ActionFetch and flatten Figma variable mapFigmaFigma
  • ActionRead committed theme file from repoGitHubGitHub
  • LogicDiff Figma tokens vs committed tokens; stop if no drift
  • ActionOpen PR with regenerated theme + changelogGitHubGitHub
  • OutputNotify reviewer with PR link in SlackSlack

What it does

Keeps your codebase's token source (a Tailwind/CSS theme file in Git) in sync with Figma. It detects when a designer changed a value in Figma that the code has not picked up, then opens a PR so an engineer can review and merge.

When to use it

Use it when design tokens are authored in Figma but consumed in code, and you want a controlled, reviewable path for changes rather than auto-merging design edits straight to main.

How it works

  1. 1A daily schedule triggers the check.
  2. 2The flow pulls current Figma variable collections and resolves them to a flat token map.
  3. 3It reads the committed theme file from GitHub and parses its existing token values.
  4. 4A logic step diffs the two maps; if there is no drift, the run ends silently.
  5. 5On drift, it regenerates the theme file with the new values and a comment block listing each changed token.
  6. 6A GitHub step opens a pull request on a fresh branch with the updated file and a summarized changelog in the PR body.
  7. 7The PR link is posted to Slack for the on-duty front-end reviewer.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FigmaFiles, frames, comments, assets.
  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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