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Translator Api

Community
webmaxru
translator-api

Implements and debugs browser Translator API integrations in JavaScript or TypeScript web apps. Use when adding Translator support checks, language-pair availability flows, model download UX, session creation, translate() or translateStreaming() calls, input-usage measurement, or permissions-policy handling for on-device translation. Don't use for server-side translation SDKs, cloud translation services, or generic multilingual content pipelines.

Overview

Publisherwebmaxru
Repositoryweb-ai-agent-skills
Skill nametranslator-api
Stars
53
Forks
5
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by webmaxru on GitHub. Read the source before you install it.

Installation

Install the Translator Api AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/webmaxru/web-ai-agent-skills/tree/main/skills/translator-api
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/webmaxru/web-ai-agent-skills.git /tmp/web-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/web-ai-agent-skills/skills/translator-api .claude/skills/translator-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Translator Api in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Translator Api on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Translator Api is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Translator API

Procedures

Step 1: Identify the browser integration surface

  1. Inspect the workspace for browser entry points, text-input flows, localization UI, and any existing AI abstraction layer.
  2. Execute node scripts/find-translator-targets.mjs . to inventory likely frontend files and existing Translator API markers when a Node runtime is available.
  3. If a Node runtime is unavailable, inspect the nearest package.json, HTML entry point, and framework bootstrap files manually to identify the browser app boundary.
  4. If the workspace contains multiple frontend apps, prefer the app that contains the active route, translation UI, or user-requested feature surface.
  5. If the inventory still leaves multiple plausible frontend targets, stop and ask which app should receive the Translator API integration.
  6. If the project is not a browser web app, stop and explain that this skill does not apply.

Step 2: Confirm API viability and choose the integration shape

  1. Read references/translator-reference.md before writing code.
  2. Read references/examples.md when the feature needs a session wrapper, download-progress UI, streaming output, cancellation, or cleanup shape.
  3. Read references/compatibility.md when preview flags, browser channels, iframe rules, or environment constraints matter.
  4. Read references/troubleshooting.md when support checks, session creation, translation, or cleanup fail.
  5. Verify that the feature runs in a secure Window context.
  6. Verify that the current frame is allowed to use the translator permissions-policy feature.
  7. Choose the narrowest session shape that matches the task:
    • bare Translator.create() for a fixed language pair and full-result translation
    • monitored create() when the UI must surface model download progress
    • translateStreaming() when the UI should reveal translated output incrementally
    • measureInputUsage() when quota or input-size budgeting affects the flow
  8. If the feature must run in a worker, on the server, or through a cloud translation contract, stop and explain the platform mismatch.
  9. If the project uses TypeScript, add or preserve narrow typings for the Translator API surface used by the feature.

Step 3: Implement a guarded translator session

  1. Read assets/translator-session.template.ts and adapt it to the framework, state model, and file layout in the workspace.
  2. Centralize support checks around globalThis.isSecureContext, Translator, and the same sourceLanguage and targetLanguage shape the feature will use at runtime.
  3. Gate session creation behind Translator.availability() using the same language pair that will be passed to Translator.create().
  4. Treat availability() as a capability check, not a guarantee that creation will succeed without download time, policy approval, or user activation.
  5. Create sessions only after user activation when creation may trigger a model download.
  6. Use the monitor option during create() when the product needs download progress.
  7. Use AbortController for cancelable create(), translate(), translateStreaming(), or measureInputUsage() calls, and call destroy() when the session is no longer needed.
  8. Recreate the session instead of mutating sourceLanguage or targetLanguage after creation; session options are fixed per instance.
  9. If the feature lives in a cross-origin iframe, require explicit delegation through allow="translator".

Step 4: Wire UX and fallback behavior

  1. Surface distinct states for missing APIs, insecure contexts, blocked frames, unsupported language pairs, downloadable or downloading models, ready sessions, in-flight translation, and aborted work.
  2. Keep a non-AI fallback for unsupported browsers, blocked frames, or environments that do not meet current preview requirements.
  3. Preserve the source and target language pair explicitly in product state instead of inferring them from translated output.
  4. Use translate() when downstream logic needs the full result before continuing, and use translateStreaming() when the UI should reveal translated text incrementally.
  5. Use measureInputUsage() when large inputs can exceed session quota.
  6. Treat same-language or best-fit identity translations as valid outcomes instead of forcing a fallback when the translated result matches the input.
  7. Chunk or queue long-running product workflows deliberately; translation requests are not a substitute for general summarization, writing, or chat tasks.
  8. Do not route generic cloud translation, document localization pipelines, or server-side batch translation through this API; switch to the approved service or runtime when the task is not browser on-device translation.

Step 5: Validate behavior

  1. Execute node scripts/find-translator-targets.mjs . to confirm that the intended app boundary and Translator API markers still resolve to the edited integration surface.
  2. Verify secure-context checks, Translator feature detection, and availability() behavior before debugging deeper runtime failures.
  3. Test at least one create() plus translate() flow with representative user text for the target language pair.
  4. If the feature exposes streaming, test translateStreaming() separately and confirm partial output stops cleanly on abort.
  5. If the feature depends on quota budgeting, verify measureInputUsage() behavior before sending large input.
  6. Confirm that cancellation rejects with the expected abort reason and that destroyed sessions are not reused.
  7. If the target environment depends on preview browser flags or channel-specific behavior, confirm the required browser state from references/compatibility.md before treating failures as application bugs.
  8. Run the workspace build, typecheck, or tests after editing.

Error Handling

  • If Translator is missing, keep a non-AI fallback and confirm secure-context, browser, channel, and flag requirements before changing product logic.
  • If availability() returns downloadable or downloading, require user-driven session creation before promising that translation is ready.
  • If create() throws NotAllowedError, check permissions-policy constraints, missing user activation for downloads, browser policy restrictions, or user rejection.
  • If create() throws OperationError, treat model initialization as failed and retry only after checking browser state, required flags, and download readiness.
  • If a translation call throws QuotaExceededError, reduce the input size or measure usage before retrying.
  • If a translation call throws NotReadableError or UnknownError, surface a visible fallback instead of retrying blindly with the same input.
  • If the feature must run in a worker or server context, stop and explain that the Translator API is a window-only browser API.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Translator Api AI skill do?

Implements and debugs browser Translator API integrations in JavaScript or TypeScript web apps. Use when adding Translator support checks, language-pair availability flows, model download UX, session creation, translate() or translateStreaming() calls, input-usage measurement, or permissions-policy handling for on-device translation. Don't use for server-side translation SDKs, cloud translation services, or generic multilingual content pipelines.

Why use Translator Api on TypingMind?

Because you install it once and use it with any model. Translator Api is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Translator Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/webmaxru/web-ai-agent-skills/tree/main/skills/translator-api. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Translator Api?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Translator Api?

As many as you like. As long as a model supports skills, you can use Translator Api with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Translator Api AI skill free?

Yes. It is published on GitHub by webmaxru under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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