AI Translation
Translate app content while preserving structure, placeholders, and product terminology.
What it adds
A translation pipeline over the app's translatable content that keeps source and target linked, protects non-translatable tokens, and routes uncertain output to review.
What your agent is told to do
5
What your agent is told to do
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Identify what is genuinely translatable and separate it from what is not. Extract placeholders, markup, URLs, identifiers, code, and proper names into protected tokens before generation and restore them afterwards.
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Store every translation with a reference to the exact source version it came from. When the source changes, mark the translation stale and queue it for retranslation rather than leaving a silently outdated string in place.
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Hold a per-language glossary of product terms and their approved renderings, and apply it to every request. A term that translates three ways across the app is worse than leaving it in the source language.
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Run translation through the app's existing background-job system in batches, with a token ceiling per batch and backoff on provider errors. Do not translate on page render or on a user's request path.
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Require review before a translation becomes visible to end users, and let a reviewer approve, edit, or reject per string. Machine output published unreviewed will be found by a customer before it is found by the team.
Edge cases it handles
8
Edge cases it handles
8- Placeholders, markup, URLs, and code fragments come back reordered, translated, or dropped. Verify every protected token is present and unaltered after generation, and reject the string when one is missing rather than shipping a broken interpolation.
- Glossary terms must be enforced after generation as well as requested before it, because a model will happily ignore a term list mid-sentence.
- Word-for-word output loses register. Formality, address form, and politeness level must be specified per language, since the correct choice differs by locale and cannot be inferred from the English source.
- Ambiguous source text — a bare noun that is also a verb, a string with no context, a fragment reused in several places — must be flagged for a human rather than resolved by guessing. Give translators the source context and where the string appears.
- Source and target must stay linked in both directions, so an edit to the source can find every affected translation and a reviewer can always see what a translation was made from.
- Text expansion breaks layouts. Translated strings will run longer than the source in several languages, and the review surface should show where a string is used so the reviewer can catch overflow.
- Right-to-left languages need direction and alignment handled at the layout level; a correct translation rendered in the wrong direction is still broken.
- A refusal, a timeout, or truncated output must leave the previous approved translation in place and mark the string as failed, never fall back to showing the untranslated source where a translation already existed.
Definition of done
9
Definition of done
9- Placeholders, markup, URLs, and code are protected before generation and verified intact after it.
- Each translation records the source version it derives from and is marked stale when the source changes.
- A per-language glossary is applied and enforced on the output.
- Translation runs in background batches with token ceilings and backoff, never on a request path.
- No translation reaches end users without a reviewer approving it.
- Ambiguous source strings are flagged with their usage context for human resolution.
- A failed translation leaves the last approved version visible.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Multimodal Image Analysis
Multimodal Image Analysis
Let users ask questions about screenshots, photos, charts, and interface images.
What it does
Image input on AI conversations, with safe downscaling, grounded answers, and defined retention of derived copies.
How it works
- 1 Prepare images before they are sent: correct orientation, strip location and camera metadata, and downscale to the smallest size that still keeps small text and fine detail legible.
- 2 Require the answer to distinguish what is visible in the image from what is inferred, and to say when the image is too low in quality to support a conclusion.
- 3 Label and reference images explicitly when more than one is attached, so a question about the second chart is not answered from the first.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/multimodal-image-analysis
AI Image Captioning
AI Image Captioning
Describe images so they can carry a visible caption and be found by search.
What it does
A generated description stored per image, offered as a draft caption and indexed for search.
How it works
- 1 Store the generated description in its own field and decide explicitly whether each surface shows it, indexes it, or both. Do not write it into the alt attribute, which belongs to AI Image Alt Text and answers a different question.
- 2 Offer the description as a draft caption the user can accept or rewrite, and leave any caption a human already wrote untouched unless they explicitly ask for a replacement.
- 3 Index the description alongside the image's existing metadata so images become findable by what is in them, and reuse the app's existing search infrastructure rather than adding a parallel one.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-image-captioning
AI Changelog Drafting
AI Changelog Drafting
Turn merged work into a changelog draft written for customers, not for engineers.
What it does
A grouped, customer-facing changelog draft generated from merged work items or release notes for a nominated release, held for review before publishing.
How it works
- 1 Take the set of work items belonging to one nominated release as input, and require each item to carry a shipped marker before it is eligible. Unreleased and reverted work must be excluded at the input stage, not filtered out of the prose afterwards.
- 2 Rewrite each item as the outcome a customer notices rather than the change that was made, and keep a link from every drafted line back to the underlying item so a reviewer can check it.
- 3 Group related items under headings the reader would recognise, and keep fixes as their own visible section rather than absorbing them into a feature summary.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-changelog-drafting
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.