CONTENT CREATION
Localize a published blog post into regional variants with a Coda glossary, then open PRs
When a post ships, it generates locale variants whose terminology is enforced against a Coda-managed glossary, then opens one GitHub pull request per locale for human review.
How it runs
The automated pipeline, trigger to output.
- TriggerNew blog post Markdown merged to repoGitHub
- ActionLoad approved term glossary from CodaCoda
- ActionTranslate post per locale with glossary constraintsOpenAI
- LogicFlag glossary terms mistranslated or wrongly translated
- ActionAuto-correct flagged terms to enforced wordingOpenAI
- OutputOpen one GitHub PR per locale for editor reviewGitHub
What it does
Turns one English blog post into review-ready translations for each target locale. Every translation is checked against an approved term list so brand and product names stay consistent, and each locale lands as its own GitHub pull request that an editor can approve and merge.
When to use it
Use this when your content lives as Markdown in a Git repo and you localize into a fixed set of regions (for example de-DE, fr-FR, ja-JP). It fits teams who want translations to follow the same review-and-merge flow as code, with a single source of approved terminology owned by marketing.
How it works
- 1A new Markdown post merged to the repo triggers the run.
- 2The flow pulls the approved glossary (source term, locale, required translation, do-not-translate flag) from a Coda table.
- 3For each target locale, OpenAI translates the post with the glossary injected as hard constraints.
- 4A logic step scans each draft and flags any glossary term that was mistranslated or translated when it should stay verbatim.
- 5Flagged drafts are auto-corrected with the enforced term before proceeding.
- 6The flow opens one GitHub pull request per locale with the localized Markdown for editor review.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect CodaDocs, packs, automations.
- 3Connect OpenAIModels, embeddings, files.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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