DOCUMENT OPS

Intake review for newly published Figma components

When a new component is published to a Figma library, drafts a catalog entry in Coda, asks an LLM to propose a description and category.

CategoryDocument Ops
EngineSim + Paperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew component published to Figma libraryFigmaFigma
  • ActionFetch component name, note, and screenshotFigmaFigma
  • ActionDraft description and category with LLMOpenAI
  • ActionCreate pending_review draft row in CodaCodaCoda
  • OutputRoute draft to Slack for one-click approvalSlack

What it does

Gives every new component a clean front door into the catalog. The moment a component is published to a watched Figma library, the flow creates a draft row in Coda, uses an LLM to propose a plain-English description, suggested category, and naming-convention check, then sends the draft to Slack for an owner to approve or edit before it goes live in the catalog.

When to use it

Use it when components land in the library with cryptic names and no description, and you want a consistent intake step instead of retroactive cleanup. Best for teams that want human review on every addition but don't want to write the first draft from scratch.

How it works

  1. 1A Figma webhook fires when a new component is published to a watched library file.
  2. 2The flow pulls the new component's name, screenshot reference, and any author note.
  3. 3An LLM step drafts a description, proposes a category, and flags naming-convention violations.
  4. 4A draft row with status `pending_review` is created in Coda.
  5. 5The draft is posted to Slack with Accept and Edit actions for the owner.
  6. 6On accept, the Coda row flips to `published`.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FigmaFiles, frames, comments, assets.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect CodaDocs, packs, automations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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