# Translation Quality > Comprehensive professional translation skill using Agent Teams. Orchestrates parallel terminology verification, translation with layout preservation, quality review, revision, and professional typesetting — all in one workflow. Use this skill whenever translating documents where accuracy and professional output matter — especially documents with specialized terminology, proper nouns, organization… - **Type:** Skill - **Install:** `agentstack add skill-senshinji-claude-translation-skill-claude-translation-skill` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [senshinji](https://agentstack.voostack.com/s/senshinji) - **Installs:** 0 - **Category:** [Communication](https://agentstack.voostack.com/c/communication) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [senshinji](https://github.com/senshinji) - **Source:** https://github.com/senshinji/claude-translation-skill ## Install ```sh agentstack add skill-senshinji-claude-translation-skill-claude-translation-skill ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Translation Quality — Multi-Agent Professional Translation This skill orchestrates a complete professional translation pipeline using Agent Teams. It combines: - **Translation with structure preservation** (absorbs translation-layout) - **Terminology verification via web search** (new capability) - **Independent quality review** (new capability) - **Revision based on review feedback** (new capability) - **Professional document typesetting** (absorbs formal-doc-layout) ## Architecture Overview ``` User: "翻译这个文档" │ Team Lead (this session) TeamCreate ─ estimate terms │ ┌─────────────────┼──────────────────┐ │ ≤50 terms │ >50 terms │ translator term- │ term-fast reviewer (Sonnet) researcher │ term-deep (Opus) │ (Sonnet) │ (both Sonnet) (waiting) Phase 1A: Phase 1B: │ Phase 1B: │ First-pass web search │ parallel │ translation verify │ search │ │ │ │ │ │ │ │ │ Phase 1.5: │ │ │ Lead merges → │ │ ← terminology-glossary.json → │ ├──────────────┼───────────────────────┤ │ Phase 2: reviewer │ │ ← review feedback → │ Phase 3: revision │ │ Phase 4: Typesetting → .docx/.pdf ``` ## Prerequisites Agent Teams must be enabled: - Add `"CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"` to `~/.claude/settings.json` under `"env"`, OR - Export `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1` in shell profile --- ## Phase 0: Team Lead Preparation ### 0.1 Read and Analyze Source Document Read the source document completely. Determine: - Source language and target language - Document type (conference agenda, contract, report, academic paper, etc.) - Estimated length (pages/sections) - Whether specialized terminology is present **Source format handling (critical for structure preservation):** - If source is .doc or .docx, convert with `textutil -convert html` (NOT txt). Plain text conversion STRIPS all table structure — the translator cannot preserve tables it cannot see. - Save HTML to /tmp/translation-workspace/source.html - Also copy the original file for reference **Chunking (if >10 pages):** 1. Estimate pages: count paragraphs in HTML body, ~35 paragraphs ≈ 1 page 2. If ≤10 pages: skip chunking, proceed as single-chunk mode 3. If >10 pages: identify chapter boundaries in HTML — look for: - ``, `` tags, OR - Bold paragraphs with larger font-size (Word's heading style), OR - Paragraphs matching "一、" "二、" "三、" etc. (Chinese section numbering) 4. Group sections into chunks of ≤10 pages each. Never split inside a `` block 5. Save chunks to /tmp/translation-workspace/chunks/chunk-{N}.html 6. Record boundaries in /tmp/translation-workspace/chunk-manifest.md **Structural manifest** — the lead MUST produce before spawning agents: Scan the source (use .html version) and record: - Number of tables, each table's column count and row count - Content that lives INSIDE table cells (especially multi-item cells like "10 presentations listed inside a single schedule row") Save to /tmp/translation-workspace/structure-manifest.md ### 0.2 Create Workspace ```bash # Create a workspace directory for this translation mkdir -p /tmp/translation-workspace ``` All agents will read from and write to this workspace. ### 0.3 Define Contracts **Before spawning any teammate**, define these contracts: **Contract 1 — Terminology Glossary (term-researcher → translator, reviewer):** See `references/glossary-schema.md` for full JSON schema and example. Confidence: high (2+ sources), medium (1 source), low (best judgment). Categories: organization, person, place, venue, title, technical, product, event **Contract 2 — Review Feedback (reviewer → translator):** See `references/review-feedback-schema.md` for format and example. Issue types by priority: FABRICATION > OMISSION > TERMINOLOGY > ACCURACY > STRUCTURE > REGISTER **Contract 3 — Workspace Paths:** ``` /tmp/translation-workspace/ ├── source.* / source.html # Source document + HTML conversion ├── structure-manifest.md # Lead's structural inventory ├── chunks/ # Only if >10 pages │ └── chunk-{N}.html ├── chunk-manifest.md # Chunk boundaries (if chunked) ├── first-pass.md # Merged translation (single or concatenated chunks) ├── first-pass-chunk-{N}.md # Per-chunk output (if chunked) ├── structure-report[-chunk-{N}].md # Structure audit (per chunk if chunked) ├── glossary-fast.json # Split mode only: names, places ├── glossary-deep.json # Split mode only: orgs, technical ├── terminology-glossary.json # Final merged glossary (always) ├── review-feedback.md # Reviewer feedback (on merged first-pass.md) └── final-translation.md # Final revised translation ``` ### 0.4 Create Team and Tasks ``` TeamCreate("translation-team") Estimate term count: scan source for org suffixes (协会/委员会/公司/集团/局/院/所), quoted proper nouns, person names with titles, and English acronyms. Heuristic: ~8-10 proper nouns per page. A 7-page doc ≈ 60-70 terms → split mode. If single chunk (≤10 pages): Task 1: "First-pass translation" → translator (Sonnet) If ≤50 terms (estimated): Task 2: "Verify terminology" → term-researcher (Sonnet) If >50 terms (estimated): Task 2a: "Verify terminology (fast: names, places)" → term-researcher-fast (Sonnet) Task 2b: "Verify terminology (deep: orgs, technical)" → term-researcher-deep (Sonnet) Both 2a+2b run in PARALLEL. Lead merges outputs before reviewer starts (Phase 1.5). Task 3: "Review translation" → reviewer (Opus), depends on Task 1 + Task 2 (or 2a+2b) Task 4: "Revise translation" → translator, depends on Task 3 If N chunks (>10 pages): Task 1..N: "Translate chunk {i}" → translator-{i} (one Sonnet agent per chunk) Term tasks: same ≤50/>50 split as above (Task N+1, or N+1a and N+1b) Task N+2: "Review translation" → reviewer (depends on all prior tasks) Task N+3: "Revise translation" → translator-1 (depends on Task N+2) All translators run in PARALLEL. term-researcher(s) also run in parallel with them. Reviewer waits for ALL to complete. Model assignments: translator / translator-{N}: Sonnet (model: sonnet) — speed for volume term-researcher: Sonnet (model: sonnet) — web search + extraction term-researcher-fast (split): Sonnet (model: sonnet) — names, places term-researcher-deep (split): Sonnet (model: sonnet) — orgs, technical terms reviewer: Opus (model: opus) — maximum accuracy for review ``` --- ## Phase 1: Parallel Execution ### Spawn Prompt — translator (Sonnet) ``` You are the translator for this team. Your model should be Sonnet for speed. OWNERSHIP: - You own: /tmp/translation-workspace/first-pass.md, /tmp/translation-workspace/final-translation.md - Do NOT modify any other workspace files TASK — PHASE 1 (First-pass translation): 1. Read the structure manifest at /tmp/translation-workspace/structure-manifest.md 2. Read the source document at [SOURCE_PATH] (use .html version if available) 3. Translate from [SOURCE_LANG] to [TARGET_LANG] 4. Save to /tmp/translation-workspace/first-pass.md STRUCTURE PRESERVATION RULES (critical): These rules ensure the translation mirrors the original document's structure exactly. Tables: - Same number of columns, same column order, same column purposes - Same number of rows with 1:1 row correspondence - If original merges cells, merge the same cells - NEVER convert tables to lists or lists to tables - NEVER add/remove/reorder columns - Content inside a table row in the original MUST stay inside a table row COMMON VIOLATIONS (from real failures — do NOT repeat): - Transportation table was converted to H2 headings + paragraphs because the .txt source lost table markup. ALWAYS use the .html source. - 10 keynote presentations inside a single table cell (09:00-12:00 row) were pulled out as free text below the table. If items are inside a cell, they STAY inside that cell, even if the cell content is long. - Dining info as free text (field: value) was converted to a table. Free text stays free text — do not create tables that don't exist in source. Headings: - Match heading hierarchy level for level (H1→H1, H2→H2) - Do not add or remove headings Lists: - Preserve numbering scheme and nesting depth - Ordered stays ordered, unordered stays unordered Paragraphs: - Maintain exact content sequence — paragraph order must not change - Do not merge or split paragraphs - Preserve alignment (centered stays centered) MANDATORY STRUCTURE REPORT: Before saving first-pass.md, write /tmp/translation-workspace/structure-report.md: For EACH table in the structure manifest: Table [N]: source=[R] rows x [C] cols → translation=[R] rows x [C] cols For content boundaries: List every multi-item cell from the manifest, confirm items stayed inside: Table [N] row [R]: "[content summary]" → inside cell / BOUNDARY VIOLATION Totals: [N] tables matched, [N] boundary violations If boundary violations > 0, FIX before saving first-pass.md. ANTI-FABRICATION RULES: - NEVER invent content not in the source - If unclear, mark as [unclear in original] - Mark uncertain terminology with [?原文?] for the term-researcher to verify - Person names: transliterate from pinyin, never replace with different names - Venue names that are proper nouns within a building: transliterate (e.g., 美泉宫 as a conference room → "Meiquangong Hall"), do not translate as the famous landmark - Numbers, dates, booth numbers: copy exactly from source COMMUNICATION: - After saving first-pass.md, mark Task 1 as completed - Wait for Task 4 to be assigned — you will receive review feedback and terminology glossary via SendMessage - For Phase 3 (revision): apply ALL critical and major fixes from review-feedback.md, apply glossary corrections, then save to /tmp/translation-workspace/final-translation.md and mark Task 4 complete REVISION STRUCTURE PROTECTION (critical): During revision, you MUST only change text — NEVER restructure the document. - Do NOT pull content out of table cells into free text - Do NOT convert table cells with line breaks into numbered lists outside the table - Do NOT convert free text sections into tables - Do NOT change the number of rows or columns in any table - Keep all multi-item cells intact: replace terms IN PLACE using find-and-replace - If in doubt, use exact string replacement rather than rewriting sections Violation of these rules was observed in real testing and caused 13 structure failures. ``` ### Spawn Prompt — translator-{N} (Sonnet) [chunked mode] Same rules as the standard translator prompt above, with these overrides: - SOURCE: Read /tmp/translation-workspace/chunks/chunk-{N}.html - OUTPUT: Save to /tmp/translation-workspace/first-pass-chunk-{N}.md - STRUCTURE REPORT: Save to /tmp/translation-workspace/structure-report-chunk-{N}.md - Read the GLOBAL structure-manifest.md (covers all tables in all chunks) - Read the GLOBAL terminology-glossary.json (when available) - Your chunk covers: [SECTIONS_FROM_CHUNK_MANIFEST] - Mark Task {N} complete when done ### Spawn Prompt — term-researcher (Sonnet) ``` You are the terminology researcher for this team. OWNERSHIP: - You own: /tmp/translation-workspace/terminology-glossary.json - Do NOT modify any other workspace files TASK: 1. Read the source document at [SOURCE_PATH] 2. Extract ALL: - Organization names (协会, 委员会, 公司, 集团, etc.) - Person names with titles - Place names and venue names - Event names and conference names - Technical/specialized terms - Product names and brand names - Any proper noun 3. For EACH term, verify via web search (try in order): "[原文]" official English name → site:official-website → org's own English page → bilingual government/industry databases → person's published English name If translating EN→ZH, reverse search direction. 4. Score confidence: high (2+ sources agree), medium (1 source), low (best judgment) 5. Record source URLs as evidence for every term 6. Save JSON to /tmp/translation-workspace/terminology-glossary.json following the schema in `references/glossary-schema.md` 7. SendMessage glossary summary to "translator" and "reviewer" 8. Mark your task as completed IMPORTANT: Search in batches of 5 terms. For low-confidence terms, clearly note this. WEB SEARCH FALLBACK: If a search fails or times out, retry once. After 2 consecutive failures for a term, mark it as confidence: "low" with notes: "web search unavailable" and MOVE ON. Do NOT block the entire glossary for a few unreachable terms. If ALL searches fail (service down), produce a glossary using best judgment for all terms, mark all as low confidence, and note "web search unavailable" in metadata. Record timing: note your start time and end time in metadata (ISO 8601) so Lead can measure researcher duration for future threshold calibration. ``` ### Spawn Prompt — term-researcher-fast (Sonnet) [split mode, >50 terms] Same rules as the standard term-researcher prompt, with these overrides: - SCOPE: Person names, place names, venue names, product names ONLY - Skip: organizations, technical terms, event names (handled by deep researcher) - Search strategy: prioritize Google Scholar, ResearchGate, university websites - For person names: check published English name, default to standard Pinyin - OUTPUT: Save to /tmp/translation-workspace/glossary-fast.json - Mark your task complete when done. Do NOT write to terminology-glossary.json. ### Spawn Prompt — term-researcher-deep (Sonnet) [split mode, >50 terms] Same rules as the standard term-researcher prompt, with these overrides: - SCOPE: Organization names, technical/specialized terms, event names ONLY - Skip: person names, place names, venues, products (handled by fast researcher) - Search strategy: prioritize official websites, government databases, industry associations - OUTPUT: Save to /tmp/translation-workspace/glossary-deep.json - Mark your task complete when done. Do NOT write to terminology-glossary.json. ### Spawn Prompt — reviewer (Opus) ``` You are the quality reviewer for this team. Use Opus model for maximum accuracy. OWNERSHIP: - You own: /tmp/translation-workspace/review-feedback.md - Do NOT modify any other workspace files TASK: Wait for Tasks 1 and 2 (or 2a+2b) to both complete, then: 1. Read the source document at [SOURCE_PATH] 2. Read the first-pass translation at /tmp/translation-workspace/first-pass.md 3. Read the terminology glossary at /tmp/translation-workspace/terminology-glossary.json PRE-CHECK: Verify glossary has total_terms > 0 and the count is plausible for the document size (~8-10 terms/page). If glossary is empty or missing, alert Lead via SendMessage before proceeding — do NOT review without it. 4. Perform a systematic review with these priorities (highest first): a. FABRICATION CHECK (critical): - Go paragraph by paragraph through the translation - For EACH paragraph, verify it corresponds to source content - Flag ANY content in the translation that does not exist in the source - Flag any numbers, names, or facts that differ from the source b. STRUCTURE CHECK (critical): Read /tmp/translation-workspace/structure-manifest.md (lead's counts) and /tmp/translation-workspace/structure-report.md (translator's counts). Independently verify: - For EACH table: count rows and columns in source vs translation - For EACH multi-item cell: verify items are still inside the cell - No free text → table or table → free text conversions Output a STRUCTURE COMPARISON TABLE in the review: | E … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [senshinji](https://github.com/senshinji) - **Source:** [senshinji/claude-translation-skill](https://github.com/senshinji/claude-translation-skill) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-senshinji-claude-translation-skill-claude-translation-skill - Seller: https://agentstack.voostack.com/s/senshinji - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.