Translation - Context-Aware Localization

Scheduling

Goal

Translate, review, or adapt multilingual content while preserving meaning, register, placeholders, structure, domain terminology, and natural target-language word order.

Intent signature

• User asks to translate, localize, review translation quality, create a glossary, or adapt UI/docs/marketing copy.
• User needs context-aware translation rather than mechanical word substitution.

When to use

• Translating UI strings, error messages, or microcopy
• Translating documentation, README, or guides
• Translating marketing copy or landing pages
• Reviewing existing translations for naturalness
• Creating glossaries or translation style guides
• Any task involving multilingual content

When NOT to use

• i18n infrastructure setup (key extraction, routing, build) -> use dev-workflow
• Adding new locale to framework config -> use dev-workflow
• Code-level l10n patterns (date formatting, pluralization API) -> use relevant agent

Expected inputs

• Source text, target language, and optional locale or audience
• Existing locale files, glossary, code context, or style constraints
• Optional user/author writing sample for voice matching in prose, marketing, dialogue, or adaptation tasks
• Placeholder syntax, formatting constraints, and output mode

Expected outputs

• Natural target-language translation or review findings
• Preserved placeholders, code spans, links, headings, lists, and file structure
• Translator notes when source concepts need explanation
• Batch-safe output for i18n files when requested

Dependencies

• Existing translations and surrounding code for register and terminology
• resources/translation-rubric.md and resources/anti-ai-patterns.md (language-neutral)
• resources/lang/{code}.md for the target language (required when a profile exists)
• Project locale files when translating UI strings
• User-provided voice samples when the task asks to preserve or match a specific author's style

Control-flow features

• Branches by content type, target language, batch size, register uncertainty, and placeholder/structure requirements
• Branches by whether a language profile exists for the target, and by locale variant when the profile declares variants
• Branches by whether style-sample calibration is available and appropriate for the content type
• Reads locale files and source context; may write translated content only when explicitly editing files
• Blocks output until mechanical verification passes

Structural Flow

Entry

1. Confirm source text, target language, content type, and output mode.
2. Load resources/lang/{code}.md for the target language (see "Language Profile Loading").
3. Load existing translations, glossary, file context, or code context when available.
4. Identify placeholders, formatting constraints, and ambiguity.

Scenes

1. PREPARE: Load the target language profile, then determine register, domain, and structure constraints.
2. ACQUIRE: Read existing translations and surrounding context.
3. REASON: Analyze source meaning, connotations, figurative language, and terminology.
4. ACT: Reconstruct natural target-language output.
5. VERIFY: Run mechanical checks and translation rubric.
6. FINALIZE: Emit translation, review notes, or file changes.

Transitions

• If context is insufficient, ask one targeted question.
• If the target language has a profile and it was not loaded, stop and load it before drafting.
• If the profile declares locale variants and none was resolved, resolve the variant before translating any string.
• If batch size is greater than 10 strings, verification is mandatory before output.
• If the output violates a typography or sentence-completion rule in the profile, rewrite before final output.
• If placeholders or structure do not match, revise and rerun verification.

Failure and recovery

• If source meaning is ambiguous, flag ambiguity rather than guessing.
• If project conventions conflict with literal translation, follow project conventions and explain if needed.
• If file structure is risky to modify, preserve structure and limit edits to values.

Exit

• Success: target text is natural, faithful, structurally equivalent, and verified.
• Partial success: ambiguous source text or missing context is explicit.

Context Inference

No config file required. Instead, infer translation context from:

1. Existing translations in the project: scan sibling locale files to match register, terminology, and style already in use
2. File location: messages/, locales/, .arb files reveal the framework and format
3. Surrounding code: component names, comments, and variable names hint at domain and audience
4. Source text itself: register, formality, sentence structure reveal intent

If context is insufficient to make a confident decision, ask the user. Prefer one targeted question over a batch of questions.

Language Profile Loading

Translation quality rules split into two layers. Load both; neither is sufficient alone.

Layer	File	Holds
Shared	resources/anti-ai-patterns.md	AI writing pattern taxonomy, rules 1–25, source-side examples
Shared	resources/translation-rubric.md	5-criterion scoring
Per-language	resources/lang/{code}.md	Register system, language-only rules, localizations of shared rules, typography, self-check

Routing: resolve the target to a BCP 47 primary subtag and read resources/lang/{code}.md.

Target	Profile	Notes
Korean	lang/ko.md	rules KO-1–KO-12
Japanese	lang/ja.md	rules JA-1–JA-9
Chinese	lang/zh.md	rules ZH-1–ZH-9; variant resolution is mandatory before translating
English	lang/en.md	rules EN-1–EN-8; written for CJK → EN direction
Anything else	none yet	fall back to shared files only

Fallback rule: when no profile exists for the target, use the shared files, apply shared rules 19–24 by reasoning from the target's actual grammar, and state once in the output notes that no profile was available. Do not silently borrow another language's profile: ko.md rules are wrong for German, and applying them produces confident errors.

Adding a profile: copy resources/lang/_template.md to resources/lang/{code}.md and add the row to the routing table above. An empty profile beats an invented one.

Precedence: the profile wins over the shared file when they appear to conflict, because the shared file describes the pattern and the profile describes the target. A profile may declare that a shared rule does not apply to its language (en.md does this for the em-dash restructuring requirement, which exists only for CJK targets).

Translation Method

Stage 1: Analyze Source

Read the source text and identify:

• Register: Formal, casual, conversational, technical, literary
• Intent: Inform, persuade, instruct, entertain
• Domain terms: Words that need consistent translation (check existing translations first)
• Cultural references: Idioms, metaphors, humor that won't transfer directly
• Sentence rhythm: Short/punchy vs. long/flowing; note parallel structures, intentional repetition, and emphasis patterns
• Comprehension challenges: Terms or references target readers may struggle with, such as domain jargon lacking standard translations, cultural references (pop culture, history, social norms), implicit knowledge the author assumes, wordplay or puns, named concepts (e.g., "Dunning-Kruger effect"). For each, note: the original term, why it may confuse, and a concise plain-language explanation for a potential translator's note
• Figurative language mapping: For each metaphor, simile, idiom, or figurative expression, classify the handling approach:
  • Interpret: Discard source image entirely, express the intended meaning directly in natural target language
  • Substitute: Replace with a target-language idiom or image that conveys the same idea and emotional effect
  • Retain: Keep the original image if it works equally well in the target language
• Emotional connotations: Words carrying subjective feeling beyond dictionary meaning (e.g., "alarming" = urgency, "haunting" = lingering unease); note the emotional effect to preserve in translation

Stage 2: Extract Meaning

Strip away source language structure. Ask yourself:

• What is the author actually trying to say?
• What emotion or tone should the reader feel?
• What action should the reader take?

Do NOT start forming target sentences yet.

Stage 2.5: Persona Assignment

Persona resolution has two layers: content-type (what kind of text) and voice (how punchy or formal the rhythm). Both are needed.

Layer 1: Read translation_voice from .agents/oma-config.yaml

The translation_voice field controls global rhythm/formality. Three values:

Voice	Style override applied on top of content-type
formal	complete sentences only, no fragments, strict 합니다체/です・ます, no padding cuts
balanced (default)	content-type defaults; fragments allowed only in label/cell positions
interpreter	interpreter mindset across all content types: punchy, audience-first, spoken cadence, fragments allowed when natural in target, drops formal padding ("을 받았습니다" → "받음" / "을 모두" → drop)

If the field is missing, default to balanced. If oma-config.yaml is unreadable, also balanced.

Layer 2: Content-type persona table

Content type	Persona	Base style markers
UI strings / microcopy	UX copywriter	concise, imperative, user-friendly
Docs / README / API reference	technical writer	data + commentary, expanded explanations
Benchmark / report / changelog	technical reporter	data + commentary, objective tone
Marketing / landing / hero copy	brand copywriter	concise impact, audience-first, aggressive transcreation
Blog post / essay	essayist	preserve cadence and rhythm, retain author voice
Literary / prose	literary translator	preserve imagery, style consistency, narrative voice
Dialogue / subtitle / interview	interpreter	immediacy, audience-first, spoken register, cultural context inline

Classification heuristics:

• File location messages/, locales/, *.arb → UX copywriter
• Filename README*, docs/*, or .md with frequent code blocks → technical writer
• Score tables, benchmark stats, changelog rows → technical reporter
• Page/section hero copy → brand copywriter
• Quote marks, em-dashes, speaker labels in source → interpreter

When unclear, default to technical writer for code-adjacent content and essayist for prose. Never use a generic "translator" persona.

Combining layers

Voice is applied on top of the content-type persona. Examples:

• Content-type = technical reporter + voice = formal → fully expanded sentences, no fragments anywhere, strict 합니다체.
• Content-type = technical reporter + voice = balanced → complete sentences in body, fragments allowed in table cells (current default).
• Content-type = technical reporter + voice = interpreter → punchier rhythm, list-item fragments allowed (e.g., "39턴 / 8m 13s / $1.28 (파일당 $0.14)" instead of "39턴, 8m 13s, 총 $1.28을 썼습니다(파일당 약 $0.14)"), drops "을 모두 받았습니다" padding.

The persona is then localized to the target language at execution time. Translating into Korean as a "technical reporter" with interpreter voice means thinking as a Korean technical reporter who values rhythm and audience scan-speed over formal completeness.

Optional Layer 3: Voice sample calibration

If the user provides an author/user writing sample, analyze it before drafting. Use it as a style constraint, not as permission to alter meaning.

Extract:

• Sentence length pattern: short/punchy, long/flowing, or mixed
• Paragraph entry habit: immediate claim, context first, anecdote, question, or contrast
• Word choice level: casual, technical, academic, literary, blunt, or polished
• Punctuation habits: parentheses, colons, commas, semicolons, dashes, sparse punctuation
• Transition style: explicit connectors, abrupt turns, numbered logic, or minimal signposting
• Recurring phrases or verbal tics that are appropriate to preserve

Apply only where style matters:

• ON: blog posts, essays, speeches, interviews, marketing copy, narrative prose, adaptation requests, and user-authored documentation where preserving author voice is requested
• LIMITED: technical documentation and reports; match rhythm and terminology, but do not add personal stance
• OFF: UI strings, locale key batches, legal/official text, exact policy text, or any text where structure and fidelity outrank authorial style

Guardrail: Voice matching may adjust rhythm, diction, and sentence shape. It must not add new opinions, first-person perspective, humor, facts, examples, or emotional color that is absent from the source.

Stage 3: Reconstruct in Target Language

Rebuild from meaning as the assigned persona, following target language norms:

Word order: Follow the target language's natural structure. Quick orientation; the profile is authoritative.

• EN → KO: SVO → SOV, move verb to end, particles replace prepositions
• EN → JA: Similar SOV restructuring, honorific system alignment
• EN → ZH: Maintain SVO but restructure modifiers (pre-nominal in ZH)
• CJK → EN: topic-comment → subject-predicate, supply articles and number marking

Register matching:

• Infer from existing translations in the project, or from source text tone
• Adjust formality markers (honorifics, sentence endings, vocabulary level)

Sentence splitting/merging:

• English compound sentences often split into shorter Korean/Japanese sentences
• English bullet points may merge into flowing paragraphs in some languages

Omission of the obvious:

• Many languages (Korean, Japanese, Chinese, etc.) allow subject or pronoun omission when contextually clear
• Don't force subjects or pronouns that feel unnatural in the target language

Stage 4: Verification Gate (blocking; do not emit output until every item is confirmed)

Run the mechanical checks first, then the rubric.

A. Mechanical checks (run before rubric, must all pass):

• Profile self-check: Run the self-check list at the end of resources/lang/{code}.md in full. Every unchecked item blocks output. This is the first check, not the last, because it is the one that catches target-language failures the shared list cannot see.
• Em dash scan: Search the draft output for —. Handling is profile-defined. For targets whose profile forbids it (Korean, Japanese, Chinese), every occurrence must be structurally restructured, never simply substituted with : / ( / ,; zero em dashes AND zero mechanical-substitution survivors in the emitted output. For targets that permit it (English), enforce the shared ceiling of one per paragraph. (See anti-AI rules 14 and 14a.)
• Quote-mark scan: Search for “, ”, ‘, ’. Replace with straight quotes (", ') unless the profile's typography section requires otherwise (zh-CN uses “”; Japanese uses 「」/『』; French uses «»), the source explicitly uses curly quotes, or the file format mandates them. Check the profile before stripping anything.
• Placeholder integrity: Every {name}, {{count}}, %s, <tag>, and `code` from the source appears unchanged in the target.
• Structure parity: Headings, list bullets, table rows, code blocks, and links match the source count and nesting.
• Register consistency: One sentence-ending style throughout (don't mix -ㅂ니다 with -다, formal with casual).
• Sibling-pattern match (when applicable): If the target lives in a context that already contains target-language siblings (markdown table rows, locale file with sibling values, glossary entries, list items in a doc), read at least 3 siblings and identify (a) separator style: comma vs 및/와/과 vs em dash vs colon vs newline, (b) action-verb form: noun-phrase fragments vs full verb phrases vs imperative, (c) loanword density, (d) register and sentence-ending style. Your draft MUST match the dominant pattern. If the draft uses a separator/verb form/register absent from siblings, BLOCK and revise. Example failure: siblings use comma-separated noun phrases without colons; your draft uses X: Y and Z colon syntax. → revise to comma form.

If any mechanical check fails, revise and re-run. Do not proceed to the rubric until all pass.

B. Translation rubric (see resources/translation-rubric.md):

1. Does it read like it was originally written in the target language?
2. Are domain terms consistent with existing translations in the project?
3. Is the register consistent throughout?
4. Is the meaning preserved (not just words)?
5. Are cultural references adapted appropriately?
6. Are emotional connotations preserved (not flattened into neutral descriptions)?

C. Anti-AI patterns (see resources/anti-ai-patterns.md for the shared taxonomy and resources/lang/{code}.md for how each item manifests in the target): 7. No AI vocabulary clustering or inflated significance 8. No promotional tone upgrade beyond the source 9. No synonym cycling; use consistent terminology 10. No source-language word order leaking through 11. No unnecessary bold or formatting artifacts (em dashes already covered in mechanical check A) 12. No Europeanized patterns (unnecessary connectives, passive voice, noun pile-up, over-nominalization, forced pronouns, cleft calques) 13. No humanizer-pattern leftovers: generic positive conclusions, "let's dive in" signposting, persuasive-authority tropes, formulaic "challenges/future prospects" sections, title-restating warmups, emoji decoration, or vague media/notability padding

D. Figurative language handling: 14. Were all metaphors/idioms handled per the classify decision (interpret/substitute/retain)? 15. Do figurative expressions read naturally in the target language, not as literal calques?

Translator's Notes Guidelines

When adding explanatory notes for terms, cultural references, or concepts that target readers may struggle with:

Format: translated term (original term, plain-language gloss), or translated term (original term) for well-known terms that only need the original. Bracket style follows the target's typography section in resources/lang/{code}.md: halfwidth () for Korean and English, fullwidth （） for Japanese and Chinese around non-ASCII content

Calibration by audience:

• Technical readers: Skip annotation on common tech terms (API, deploy, refactor). Only annotate domain-specific or coined terms
• General readers: More generous annotation. Explain jargon, cultural references, and domain concepts in plain language
• Short texts (< 5 sentences): Minimize annotations; only annotate terms the target audience is unlikely to know

Rules:

• Annotate on first occurrence only; don't repeat the note
• Keep notes concise (aim for under 10 words)
• Explain what it means, not just provide the English original
• Don't annotate self-explanatory terms or widely recognized loanwords
• If a comprehension challenge was identified in Stage 1, use the pre-planned explanation

When to run Stage 5–7

Default ON for:

• Documentation (README, guides, API reference)
• Reports, benchmarks, changelogs, blog posts
• Marketing copy and landing pages
• Any prose longer than ~3 sentences
• Anything containing tables, bullet lists, or code blocks mixed with prose
• Translation review mode

Default OFF (Stage 4 verification only) for:

• Single short UI string (< 10 words) in a UI locale file (i18n keys, .arb, .json, messages/) with established glossary
• Batch UI key translations where each value is independent and < 1 sentence
• User explicitly requests "fast translation", "skip reflection", or "직역"

Tie-breaker rule: When a target qualifies for BOTH ON and OFF categories, default ON wins. Common conflict cases:

Situation	Why both	Resolution
README table cell (short AND documentation)	<10 words but lives in README*.md	ON: README is documentation
CHANGELOG line entry	<10 words but lives in changelog	ON: changelog is documentation
Skill description in registry	short noun phrase but commits to git-tracked source	ON: registry descriptions are documentation, not UI locale values
Tooltip in i18n file	<10 words AND in messages/	OFF: UI string in locale file

When in doubt, run reflection: roughly 1.5–2× tokens, against a post-merge revision that costs more. Skipping it on non-trivial content is the most common source of translationese complaints.

Extended workflow

After completing Stage 1–4, continue with:

Stage 5: Critical Review

Re-read the translation against the source with fresh eyes. Produce a diagnostic review (no rewriting yet).

Start the review by explicitly answering this question first: "What makes the draft below still feel obviously machine-translated or AI-generated?" Write 3–7 short bullets naming the remaining tells (e.g., "register suddenly shifts to formal in the final paragraph", "the same connective construction repeats three times", "noun-ending fragments survive in body text outside label/cell positions", "a metaphor was kept literal where the target language would interpret it"). Then continue with the structured checklist:

• Accuracy: Compare paragraph by paragraph. Any facts, numbers, or qualifiers altered?
• Europeanized language: Scan for unnecessary connectives, passive voice, noun pile-up, over-nominalization, forced pronouns (shared rules 19–24), using the worked examples in resources/lang/{code}.md
• Figurative language fidelity: Cross-check metaphor mapping from Stage 1. Were all handled per the classify decision? Any literal calques that sound unnatural?
• Emotional fidelity: Were subjective/emotional word choices flattened into neutral descriptions?
• Tone drift: Does the register stay consistent from start to finish, or does it shift mid-document (e.g., formal intro drifting into casual explanation)?
• Expression & flow: Flag sentences that still read like "translation-ese" (stiff phrasing, unnatural word order, awkward transitions)
• Humanization patterns: For prose, marketing, blog, report, and adaptation tasks, scan for sterile rhythm, evenly shaped paragraphs, signposting, generic conclusions, persuasive-authority tropes, formulaic challenge/future sections, emoji decoration, title-restating warmups, and filler phrases
• Voice sample fit: If a sample was provided, check whether sentence rhythm, paragraph openings, diction, punctuation, and transition style match the sample without adding unsupported meaning
• Translator's notes quality: Too many? Too few? Accurate and concise?

Stage 6: Revision

Apply all findings from Stage 5 to produce a revised translation:

• Fix accuracy issues
• Rewrite Europeanized expressions into native patterns
• Re-interpret literally translated metaphors per the mapping
• Restore flattened emotional connotations
• Restructure stiff sentences for fluency
• Adjust translator's notes per review recommendations

Stage 7: Polish

Final pass for publication quality:

• Read as a standalone piece: does it flow as native content?
• Smooth remaining rough transitions between paragraphs
• Ensure narrative voice is consistent throughout
• Final scan for surviving literal metaphors or translation-ese
• Verify formatting preservation (headings, bold, links, code blocks)

Batch Translation Rules

When translating multiple strings (e.g., UI keys):