{"id":"d86f0ac5-74fe-4bfe-bbdd-be1dbf121ae8","shortId":"jVPCUn","kind":"skill","title":"lovstudio:translation-review","tagline":"Review Chinese-to-English translations for accuracy, grammar, terminology, and consistency. Produces a structured review report with prioritized issues. Trigger when: user provides a Chinese document and its English translation for review/checking/proofreading, or mentions \"翻译检查\"","description":"# translation-review — Chinese-English Translation Review\n\nSystematically compare a Chinese source document against its English translation,\nidentify issues across 6 dimensions, and produce a prioritized review report.\n\n## When to Use\n\n- User provides a Chinese original + English translation pair for review\n- User asks to check/proofread an English translation from Chinese\n- User mentions \"翻译检查\", \"审校\", \"translation review\"\n\n## Workflow (MANDATORY)\n\n**You MUST follow these steps in order:**\n\n### Step 1: Collect Input\n\n**Use `AskUserQuestion` to confirm before starting:**\n\nAsk the user:\n1. **Document type** — academic paper (SCI/EI), technical docs, business docs, general\n2. **Domain** — medicine, CS, law, finance, general (affects terminology checking)\n3. **Focus areas** — terminology accuracy, grammar, style, references, all (multi-select)\n\nIf the user has already provided context (e.g., \"SCI 4区医学论文\"), skip the obvious questions.\n\n### Step 2: Read Both Documents\n\nRead the Chinese source and English translation. For `.docx` files:\n\n```bash\npandoc \"<chinese_file>.docx\" -t plain --wrap=none\npandoc \"<english_file>.docx\" -t plain --wrap=none\n```\n\nFor `.md` or `.txt` files, read directly.\n\nIf files are large, read in sections (offset/limit) to stay within context.\n\n### Step 3: Systematic Review\n\nCompare the two documents across **6 dimensions**, in this order:\n\n#### D1: Mistranslation (meaning changed)\n- Words/phrases translated to a different meaning\n- Omissions (content in Chinese missing from English)\n- Additions (content in English not in Chinese)\n- Pay special attention to domain-specific terms that have been incorrectly generalized or specialized\n\n#### D2: Terminology Accuracy\n- Domain-specific terms: check against standard English terminology\n- Abbreviation consistency: first occurrence should have full form + abbreviation\n- Proper nouns: names, institutions, gene/protein names, drug names\n- Units and measurements\n\n#### D3: Grammar & Syntax\n- Subject-verb agreement\n- Tense consistency (especially in Methods vs Results)\n- Article usage (a/an/the) — common error in Chinese-to-English\n- Dangling modifiers, run-on sentences\n- Passive voice overuse\n\n#### D4: Consistency\n- Same Chinese term translated differently in different places\n- Formatting inconsistencies (capitalization, hyphenation, number style)\n- Heading style consistency\n\n#### D5: References & Citations (academic papers only)\n- Citation numbers match between Chinese and English versions\n- Reference format consistency (author names, journal abbreviations)\n- Missing author information in reference entries\n- In-text citation correctness\n\n#### D6: Style & Register\n- Academic register appropriate for target journal\n- Overly verbose or literary phrasing\n- Chinese-influenced sentence structure (e.g., \"through...method\" patterns)\n- Paragraph length and structure\n\n### Step 4: Source Document Issues\n\nWhile reviewing, also note issues in the **Chinese source** that the English faithfully translates but are themselves incorrect. Flag these separately.\n\n### Step 5: Generate Report\n\nOutput a structured review report with the following format:\n\n```markdown\n# 翻译审校报告 / Translation Review Report\n\n**Document**: [title]\n**Reviewer**: 手工川 (AI-assisted)\n**Date**: [YYYY-MM-DD]\n**Domain**: [domain]\n**Document type**: [type]\n\n---\n\n## Summary\n\n[2-3 sentence overall assessment]\n\n---\n\n## A-Level Issues (Must Fix)\n\n### A1. [Issue title]\n**Location**: [section/paragraph]\n**Original (CN)**: \"...\"\n**Translation (EN)**: \"...\"\n**Problem**: [explanation]\n**Suggested fix**: → \"...\"\n\n---\n\n## B-Level Issues (Recommended)\n\n### B1. [Issue title]\n...\n\n---\n\n## Source Document Issues\n\n### C1. [Issue title]\n...\n\n---\n\n## Priority Summary\n\n| Priority | # | Issue | Impact |\n|----------|---|-------|--------|\n| Must fix | A1 | ... | ... |\n| ... | | | |\n\n---\n\n## Conclusion\n\n[Overall quality assessment + recommended next steps]\n```\n\n### Step 6: Save Report\n\nSave the report to the same directory as the input files:\n\n**Filename**: `手工川-translation-review-{YYYY-MM-DD}-v0.1.md`\n\nIf a file with this name exists, increment the version.\n\n## Issue Severity Classification\n\n| Level | Criteria | Examples |\n|-------|----------|---------|\n| **A (Must fix)** | Meaning changed, factual error, or would cause rejection | Mistranslation, wrong terminology, reference number mismatch |\n| **B (Recommended)** | Awkward but understandable, or non-standard usage | Verbose phrasing, minor grammar, style inconsistency |\n| **C (Source issue)** | Error in Chinese original, faithfully translated | Wrong terminology in Chinese, factual inaccuracy |\n\n## Domain-Specific Checklists\n\n### Academic / SCI Papers\n- [ ] Abstract: structured, matches body content\n- [ ] Keywords: standard MeSH or domain terms\n- [ ] Reference numbers: sequential, match between CN/EN versions\n- [ ] Figure/table captions: translated and consistent\n- [ ] Statistical reporting: p-values, CI format\n\n### Medical / Biomedical\n- [ ] Disease names: ICD/MeSH standard\n- [ ] Gene/protein names: HUGO/UniProt standard (italics for genes)\n- [ ] Drug names: INN (International Nonproprietary Names)\n- [ ] Anatomical terms: Terminologia Anatomica standard\n\n### Technical Documentation\n- [ ] UI strings: consistent with product localization\n- [ ] Code snippets: untranslated, syntax preserved\n- [ ] API terms: match official 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'mean':230,237,569 'measur':298 'medic':651 'medicin':133 'mention':39,93 'mesh':628 'method':310,404 'minor':595 'mismatch':582 'miss':242,372 'mistransl':229,577 'mm':464,547 'modifi':324 'multi':151 'multi-select':150 'must':101,481,515,567 'name':290,293,295,369,555,654,658,665,669 'next':523 'non':590 'non-standard':589 'none':188,194 'nonproprietari':668 'note':418 'noun':289 'number':346,358,581,633 'obvious':165 'occurr':282 'offici':691 'offset/limit':209 'omiss':238 'order':106,227 'origin':77,488,605 'output':440 'over':392 'overal':475,519 'overus':331 'p':647 'p-valu':646 'pair':80 'pandoc':183,189 'paper':124,355,620 'paragraph':406 'passiv':329 'pattern':405 'pay':252 'phrase':396,594 'place':341 'plain':186,192 'preserv':687 'priorit':23,67 'prioriti':510,512 'problem':492 'produc':17,65 'product':681 'proper':288 'provid':28,74,158 'qualiti':520 'question':166 'read':169,172,200,206 'recommend':500,522,584 'refer':148,352,365,376,580,632 'regist':385,387 'reject':576 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