CONTENT CREATION
Auto-File Linear Tickets for High-Severity Localization Drift
When new localized strings merge to main, back-translates them, and for any string whose meaning drifted past the threshold.
How it runs
The automated pipeline, trigger to output.
- TriggerMerge to default branch touching locale filesGitHub
- ActionCollect newly shipped target stringsGitHub
- ActionBack-translate and score drift severityOpenAI
- LogicKeep high-severity, dedupe against open issues
- ActionCreate assigned Linear issue with full contextLinear
- OutputPost issue link as commit commentGitHub
What it does
After a merge to the default branch, it back-translates the newly shipped target strings, identifies high-severity meaning drift, and turns each flagged string into an actionable Linear issue, complete with the source text, the translation, the back-translation, and the detected problem type, routed to the owner for that locale.
When to use it
Use it when catching drift is not enough and you need it tracked and owned. This closes the loop from detection to remediation so flagged translations become assigned work instead of a report someone has to triage by hand.
How it works
- 1A push merges to the default branch touching locale files, triggering the flow.
- 2The flow collects the newly added or changed target strings from GitHub.
- 3Each string is back-translated to the source language by the LLM and scored for drift severity and issue type.
- 4A logic step keeps only high-severity entries and deduplicates against issues already open for the same string key.
- 5For each remaining entry it creates a Linear issue with full context and assigns it to the configured locale owner, then posts the issue link back as a commit comment on GitHub.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 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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