CONTENT CREATION

Reconcile completed translations back into the master doc tracker

When a locale's Linear translation task is marked done, this validates the delivered translation against the glossary, updates the master doc's locale status in Coda.

CategoryContent Creation
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerLinear translation task marked DoneLinearLinear
  • ActionPull delivered translation and locale glossaryCodaCoda
  • ActionCheck glossary term usage and consistencyOpenAI
  • LogicRoute by clean vs glossary mismatch
  • ActionOpen review subtask for mismatchesLinearLinear
  • OutputUpdate per-locale status in master trackerCodaCoda

What it does

Closes the localization loop. As each per-locale translation task is completed in Linear, the flow checks the delivered text against that locale's glossary, marks the locale complete in the master tracker, and raises a review note if required terms were not used correctly.

When to use it

Use this as the return half of a handoff pipeline: once tasks are out with translators, this keeps the master doc's status board accurate and catches glossary violations before content ships.

How it works

  1. 1A Linear task moving to Done in the Localization project triggers the run.
  2. 2The flow pulls the delivered translation and the matching locale glossary from Coda.
  3. 3OpenAI checks whether required glossary terms appear and are used consistently.
  4. 4A branch routes the result: clean translations mark the locale Complete in the Coda tracker.
  5. 5Translations with glossary mismatches instead get a Linear review subtask listing the offending terms and the task is reopened.
  6. 6Either way, the master doc's per-locale status field is updated so the board reflects reality.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LinearIssues, projects, cycles, triage.
  2. 2
    Connect CodaDocs, packs, automations.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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