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Oma Translation

OrganizationPopular
first-fluke
oma-translation

Translate or revise localized UI strings and prose while preserving meaning, terminology, placeholders, and structure.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-translation
Stars
1.3K
Forks
149
Bundled files
7
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.

  • 7 bundled files

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

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Translation 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.
  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/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-translation .claude/skills/oma-translation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Translation 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 Oma Translation 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 Oma Translation 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.

Translation - Context-Aware Localization

Scheduling

Goal

Translate, review, or adapt multilingual content faithfully and naturally while preserving the parts that must remain exact: placeholders, code, links, formatting, terminology, and file structure.

Intent signature

  • Translate, localize, review a translation, adapt copy, or create a glossary/style guide.
  • Update target-language files after the source changed.

When to use

  • UI strings, error messages, locale files, documentation, reports, marketing copy, and prose.
  • Review of existing translations for accuracy, register, terminology, or naturalness.

When NOT to use

  • i18n infrastructure, locale registration, or key extraction -> use the relevant development skill.
  • Date/pluralization APIs or other code-level localization -> use the owning code-domain skill.

Expected inputs

  • Source text, target language or locale, content type, and requested output mode.
  • Existing locale files, glossary, surrounding context, and optional author sample.
  • Placeholder and formatting constraints when the source is structured.

Expected outputs

  • Natural target-language text or evidence-backed review findings.
  • Exact preservation of placeholders, code spans, links, headings, list structure, and locale keys.
  • Translator notes only for meaningful ambiguity or necessary cultural explanation.

Dependencies

  • Target profile: resources/lang/{code}.md, if one exists.
  • resources/translation-rubric.md for substantive content or review.
  • ../_shared/core/anti-ai-prose.md and resources/anti-ai-patterns.md together when prose needs style review.
  • Existing siblings and glossary when translating into a project.

Control-flow features

  • Branches by UI batch versus prose, profile availability, locale variant, ambiguity, and review mode.
  • Loads only the target profile and references required for the content type.
  • Edits files only when the user asked for file changes.

Structural Flow

Entry

  1. Identify source, target locale, content type, and whether this is translate, review, or diff-sync.
  2. Preserve exact tokens first: placeholders, code, URLs, keys, and structural markers.
  3. Load the target profile if available; read sibling translations when the target is in a project.

Scenes

  1. PREPARE: Resolve locale, output mode, target audience, and protected syntax.
  2. ACQUIRE: Read the target profile, relevant siblings, glossary, and source context. For prose or review, load the rubric; for style review, load the shared prose diagnostics and translation exceptions together.
  3. REASON: Determine meaning, register, terminology, cultural references, and any ambiguity. Decide whether an idiom should be interpreted, substituted, or retained.
  4. ACT: Write natural target-language text. Match established project patterns without adding meaning, opinion, or personality.
  5. VERIFY: Check protected syntax and structure. For substantive text, check meaning, terminology, register, and target-language naturalness against the rubric.
  6. FINALIZE: Return the requested text, patch, or review with only supported findings.

Transitions

  • A profile exists -> load it; its target-language rules take precedence over shared guidance.
  • A locale variant is required -> resolve it before drafting.
  • No profile exists -> use shared guidance and note that limit once; do not borrow another language's rules.
  • A short independent UI string with established siblings -> use protected-syntax and sibling checks; the full prose review is optional.
  • Long prose, documentation, marketing copy, mixed tables/code, or review mode -> use the rubric.
  • Ambiguous meaning that changes the result -> ask one targeted question or flag the choice.
  • Diff-sync -> modify only source-touched equivalents, located by headings and surrounding context, not line number.

Failure and recovery

FailureRecovery
Missing target contextUse the source register and state the assumption, unless the ambiguity changes meaning
Conflicting literal wording and project conventionFollow the established target convention and explain a material difference
Placeholder or structure mismatchRevise before emitting or applying a patch
Missing profileUse shared guidance and identify the coverage limit once
Unclear cultural referenceAdd a concise note only when the target audience needs it

Exit

  • Success: the text is faithful, natural, structurally safe, and checked in proportion to content risk.
  • Partial: unresolved ambiguity or missing context is stated without inventing an answer.

Language profile loading

Resolve the BCP 47 primary subtag and load one matching profile when present:

TargetProfile
Koreanresources/lang/ko.md
Japaneseresources/lang/ja.md
Chineseresources/lang/zh.md; resolve the declared variant
Englishresources/lang/en.md
Otherno profile; use shared guidance

Profiles define typography, register, and target-language checks. Shared guidance explains common risks. A profile wins if they conflict. Add a new profile from resources/lang/_template.md only when the task is to extend this skill.

Translation method

  1. Read for meaning, intended effect, domain terms, and protected syntax. Identify figurative wording as interpret, substitute, or retain. Do not add a new metaphor or remove an intentional one without a reason.
  2. Choose register from the source, target audience, and sibling translations. Apply translation_voice from .agents/oma-config.yaml only as a rhythm/formality preference: formal, balanced (default), or interpreter. It never overrides meaning or target norms.
  3. Reconstruct in the target language. Change word order, split or merge sentences, and omit implied subjects only when the target language calls for it.
  4. Preserve author style only where it belongs: prose, dialogue, adaptation, or explicitly user-authored documentation. Match observable rhythm and diction, never add facts, stance, jokes, first person, or stronger emotion.

Verification

Always check:

  • Every placeholder, code span, identifier, URL, and locale key is unchanged.
  • Headings, links, table rows, list nesting, and code blocks retain their structure.
  • The target follows its profile's typography and register rules.
  • Existing siblings are matched when they establish terminology or UI style.

For substantive content and review mode, also check the rubric: meaning, naturalness, terminology, register, cultural fit, and emotional force. Use anti-AI patterns as a diagnostic, not a demand to make prose decorative or to remove intentional source style.

Review mode

Review from evidence, not from an expectation that every draft is machine-translated. Record zero findings when no material defect is confirmed. Separate:

  • Accuracy or safety defects: changed meaning, broken placeholder, incorrect term, or wrong register.
  • Supported style findings: a concrete target-language issue with a reason and proposed fix.
  • Preferences: optional alternatives that do not make the existing translation wrong.

Do not rewrite a sound translation merely to produce findings.

Batch and diff-sync work

For batches, read the complete set first, translate values only, and maintain one terminology and register decision. Verify placeholders and key structure across the set.

For diff-sync, map each source hunk by heading and surrounding context. Touch only the affected target section; leave unrelated text byte-identical. State updated sections, skipped cosmetic hunks, and material terminology decisions.

Output formats

For a single text, present the translation and brief notes for ambiguities or adaptations. For files, preserve the input format and modify only requested values. For review, show the original, the suggested revision only when warranted, and the evidence for each finding.

Logical Operations

Actions

ActionSSL primitiveEvidence
Resolve locale and modeSELECTUser request and file context
Read profile and siblings conditionallyREADTarget profile, glossary, nearby translations
Protect syntax and structureVALIDATESource tokens and structural markers
Infer meaning and registerINFERSource, audience, project convention
Translate or reviseWRITETarget text or requested file patch
Review substantive contentVALIDATERubric and concrete findings
Report result and limitsNOTIFYTranslation, patch, or review

Tools and instruments

  • Native search/read for local source and sibling translations.
  • Configured code_intelligence capability when available for code-context navigation; native search/read when it is unavailable or times out.
  • Target profile, rubric, anti-AI patterns, and language-template resources as selected above.

Canonical workflow path

  1. Resolve target locale and protect exact syntax.
  2. Load the one applicable language profile and relevant siblings; load the rubric for substantive content or review. For prose style review, load ../_shared/core/anti-ai-prose.md with resources/anti-ai-patterns.md; apply its fidelity exceptions before any shared style fix.
  3. Infer meaning, register, terminology, and figurative-language handling.
  4. Draft in natural target order and project style.
  5. Verify exact syntax and structure, then review substantive content against the rubric.
  6. Emit the requested translation, patch, or evidence-backed review. State a missing-profile or context limitation when material.

Resource scope

ScopeResource target
LOCAL_FSSource/target files, glossaries, language profiles
CODEBASESibling translations and code context
PROCESSNative search and syntax checks
MEMORYLocale, terminology, register, protected syntax, unresolved ambiguity

Effects and side effects

  • Produces translated text or review findings.
  • Changes locale/docs files only when explicitly requested.
  • Retains source structure, identifiers, and placeholders.

Guardrails

  1. Preserve meaning, protected syntax, and file structure before stylistic preference.
  2. Do not invent facts, opinions, emotional emphasis, examples, citations, or personality.
  3. Do not use a different language's profile as a substitute.
  4. Do not change untouched sections during diff-sync.
  5. Do not claim completion of a check that the available source cannot support.

References

  • Translation rubric: resources/translation-rubric.md (load for substantive content or review)
  • Common prose diagnostics: ../_shared/core/anti-ai-prose.md (load with translation exceptions when prose style needs review)
  • Translation exceptions and grammar diagnostics: resources/anti-ai-patterns.md (load with common diagnostics when prose style needs review)
  • Target profiles: resources/lang/{ko,ja,zh,en}.md (load one matching profile)
  • Profile template: resources/lang/_template.md (only when adding a profile)
  • Shared context loading: ../_shared/core/context-loading.md
  • Shared quality principles: ../_shared/core/quality-principles.md

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 Oma Translation AI skill do?

Translate or revise localized UI strings and prose while preserving meaning, terminology, placeholders, and structure.

Why use Oma Translation on TypingMind?

Because you install it once and use it with any model. Oma Translation 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 Oma Translation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-translation. 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 Oma Translation?

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 Oma Translation?

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

Is the Oma Translation AI skill free?

Yes. It is published on GitHub by first-fluke 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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