Three-mode translation with terminology consistency and publication-quality refinement workflows.
Works with
Supports quick (direct), normal (analyze then translate), and refined (analyze, translate, review, polish) modes; auto-detects mode from user intent keywords
Handles long content via intelligent markdown-aware chunking with parallel subagent translation and shared glossary enforcement across chunks
Customizable via EXTEND.md for target language, mode, audience, style presets (storytellin
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbaoyu-translateExecute the skills CLI command in your project's root directory to begin installation:
Fetches baoyu-translate from jimliu/baoyu-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate baoyu-translate. Access via /baoyu-translate in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Three-mode translation skill: quick for direct translation, normal for analysis-informed translation, refined for full publication-quality workflow with review and polish.
Scripts in scripts/ subdirectory. {baseDir} = this SKILL.md's directory path. Resolve ${BUN_X} runtime: if bun installed → bun; if npx available → npx -y bun; else suggest installing bun. Replace {baseDir} and ${BUN_X} with actual values.
| Script | Purpose |
|---|---|
scripts/main.ts |
CLI entry point. Default action splits markdown into chunks; also supports explicit chunk subcommand |
scripts/chunk.ts |
Markdown chunking implementation used by main.ts and kept compatible for direct invocation |
Check EXTEND.md existence (priority order):
# macOS, Linux, WSL, Git Bash
test -f .baoyu-skills/baoyu-translate/EXTEND.md && echo "project"
test -f "${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-translate/EXTEND.md" && echo "xdg"
test -f "$HOME/.baoyu-skills/baoyu-translate/EXTEND.md" && echo "user"
# PowerShell (Windows)
if (Test-Path .baoyu-skills/baoyu-translate/EXTEND.md) { "project" }
$xdg = if ($env:XDG_CONFIG_HOME) { $env:XDG_CONFIG_HOME } else { "$HOME/.config" }
if (Test-Path "$xdg/baoyu-skills/baoyu-translate/EXTEND.md") { "xdg" }
if (Test-Path "$HOME/.baoyu-skills/baoyu-translate/EXTEND.md") { "user" }
| Path | Location |
|---|---|
.baoyu-skills/baoyu-translate/EXTEND.md |
Project directory |
$HOME/.baoyu-skills/baoyu-translate/EXTEND.md |
User home |
| Result | Action |
|---|---|
| Found | Read, parse, apply settings. On first use in session, briefly remind: "Using preferences from [path]. You can edit EXTEND.md to customize glossary, audience, etc." |
| Not found | MUST run first-time setup (see below) — do NOT silently use defaults |
EXTEND.md Supports: Default target language | Default mode | Target audience | Custom glossaries (inline or file path) | Translation style | Chunk settings
Schema: references/config/extend-schema.md
CRITICAL: When EXTEND.md is not found, you MUST run the first-time setup before ANY translation. This is a BLOCKING operation.
Full reference: references/config/first-time-setup.md
Use AskUserQuestion with all questions (target language, mode, audience, style, save location) in ONE call. After user answers, create EXTEND.md at the chosen location, confirm "Preferences saved to [path]", then continue.
All configurable values in one place. EXTEND.md overrides these; CLI flags override EXTEND.md.
| Setting | Default | EXTEND.md key | CLI flag | Description |
|---|---|---|---|---|
| Target language | zh-CN |
target_language |
--to |
Translation target language |
| Mode | normal |
default_mode |
--mode |
Translation mode |
| Audience | general |
audience |
--audience |
Target reader profile |
| Style | storytelling |
style |
--style |
Translation style preference |
| Chunk threshold | 4000 |
chunk_threshold |
— | Word count to trigger chunked translation |
| Chunk max words | 5000 |
chunk_max_words |
— | Max words per chunk |
| Mode | Flag | Steps | When to Use |
|---|---|---|---|
| Quick | --mode quick |
Translate | Short texts, informal content, quick tasks |
| Normal | --mode normal (default) |
Analyze → Translate | Articles, blog posts, general content |
| Refined | --mode refined |
Analyze → Translate → Review → Polish | Publication-quality, important documents |
Default mode: Normal (can be overridden in EXTEND.md default_mode setting).
Style presets — control the voice and tone of the translation (independent of audience):
| Value | Description | Effect |
|---|---|---|
storytelling |
Engaging narrative flow (default) | Draws readers in, smooth transitions, vivid phrasing |
formal |
Professional, structured | Neutral tone, clear organization, no colloquialisms |
technical |
Precise, documentation-style | Concise, terminology-heavy, minimal embellishment |
literal |
Close to original structure | Minimal restructuring, preserves source sentence patterns |
academic |
Scholarly, rigorous | Formal register, complex clauses OK, citation-aware |
business |
Concise, results-focused | Action-oriented, executive-friendly, bullet-point mindset |
humorous |
Preserves and adapts humor | Witty, playful, recreates comedic effect in target language |
conversational |
Casual, spoken-like | Friendly, approachable, as if explaining to a friend |
elegant |
Literary, polished prose | Aesthetically refined, rhythmic, carefully crafted word choices |
Custom style descriptions are also accepted, e.g., --style "poetic and lyrical".
Auto-detection:
Upgrade prompt: After normal mode completes, display:
Translation saved. To further review and polish, reply "继续润色" or "refine".
If user responds, continue with review → polish steps (same as refined mode Steps 4-6 in refined-workflow.md) on the existing output.
/translate [--mode quick|normal|refined] [--from <lang>] [--to <lang>] [--audience <audience>] [--style <style>] [--glossary <file>] <source>
<source>: File path, URL, or inline text--from: Source language (auto-detect if omitted)--to: Target language (from EXTEND.md or default zh-CN)--audience: Target reader profile (from EXTEND.md or default general)--style: Translation style (from EXTEND.md or default storytelling)--glossary: Additional glossary file to merge with EXTEND.md glossaryAudience presets:
| Value | Description | Effect |
|---|---|---|
general |
General readers (default) | Plain language, more translator's notes for jargon |
technical |
Developers / engineers | Less annotation on common tech terms |
academic |
Researchers / scholars | Formal register, precise terminology |
business |
Business professionals | Business-friendly tone, explain tech concepts |
Custom audience descriptions are also accepted, e.g., --audience "AI感兴趣的普通读者".
1.1 Check EXTEND.md (see Preferences section above)
1.2 Load built-in glossary for the language pair if available:
1.3 Merge glossaries: EXTEND.md glossary (inline) + EXTEND.md glossary_files (external files, paths relative to EXTEND.md location) + built-in glossary + --glossary file (CLI overrides all)
Materialize source (file as-is, inline text/URL → save to translate/{slug}.md), then create output directory: {source-dir}/{source-basename}-{target-lang}/. Detect source language if --from not specified.
Full details: references/workflow-mechanics.md
Output directory contents (all intermediate and final files go here):
| File | Mode | Description |
|---|---|---|
translation.md |
All | Final translation (always this name) |
01-analysis.md |
Normal, Refined | Content analysis (domain, tone, terminology) |
02-prompt.md |
Normal, Refined | Assembled translation prompt |
03-draft.md |
Refined | Initial draft before review |
04-critique.md |
Refined | Critical review findings (diagnosis only) |
05-revision.md |
Refined | Revised translation based on critique |
chunks/ |
Chunked | Source chunks + translated chunks |
Quick mode does not chunk — translate directly regardless of length. Before translating, estimate word count. If content exceeds chunk threshold (default 4000 words), proactively warn: "This article is ~{N} words. Quick mode translates in one pass without chunking — for long content, --mode normal produces better results with terminology consistency." Then proceed if user doesn't switch.
For normal and refined modes:
| Content | Action |
|---|---|
| < chunk threshold | Translate as single unit |
| >= chunk threshold | Chunk translation (see Step 3.1) |
3.1 Long Content Preparation (normal/refined modes, >= chunk threshold only)
Before translating chunks:
${BUN_X} {baseDir}/scripts/main.ts <file> [--max-words <chunk_max_words>] [--output-dir <output-dir>]
01-analysis.md (if exists) and assembles shared context using Part 1 of references/subagent-prompt-template.md — inlining: target style, content background, merged glossary, and translation challenges02-prompt.md in the output directory (shared context only, no task instructions)02-prompt.md for shared context, receives chunk position info (chunk N of M + brief context of where it sits in the argument), translates its chunk, saves to chunks/chunk-NN-draft.md02-prompt.md (glossary, figurative language mapping, comprehension challenges, source voice, and translation challenges from analysis)02-prompt.mdchunks/frontmatter.md exists, prepend it. Save as 03-draft.md (refined) or translation.md (normal)chunks/After chunked draft is merged, return control to main agent for critical review, revision, and polish (Step 4).
Translation principles (apply to all modes):
(**解释**). Keep annotations few — only where genuinely needed for comprehensionsource prefix (camelCase: url→sourceUrl, title→sourceTitle, etc.), add translated values as new top-level fields (skip title if body has H1), keep other fields as-isTranslate directly → save to translation.md. Apply all translation principles above.
01-analysis.md (domain, tone, terminology, translation challenges)02-prompt.md (translation instructions with context, glossary, challenges)02-prompt.md) → translation.mdAfter completion, prompt user: "Translation saved. To further review and polish, reply 继续润色 or refine."
If user continues, proceed with critical review → revision → polish (same as refined mode Steps 4-6 below), saving 03-draft.md (rename current translation.md), 04-critique.md, 05-revision.md, and updated translation.md.
Full workflow for publication quality. See references/refined-workflow.md for detailed guidelines per step.
The subagent (if used in Step 3.1) only handles the initial draft. All subsequent steps (critical review, revision, polish) are handled by the main agent, which may delegate to subagents at its discretion.
Steps and saved files (all in output directory):
01-analysis.md (domain, tone, terminology, translation challenges)02-prompt.md (translation instructions with inlined context)03-draft.md (initial translation with translator's notes; from subagent if chunked)04-critique.md (diagnosis only: accuracy, Europeanized language, strategy execution, expression issues)05-revision.md (apply all critique findings to produce revised translation)translation.md (final publication-quality translation)Each step reads the previous step's file and builds on it.
Final translation is always at translation.md in the output directory.
After the final translation is written, do a lightweight image-language pass:
Reminder format (use whatever image syntax the article already uses — standard markdown or wikilink):
Possible image localization needed:
- : likely still contains source-language text while the article is now in target language
- : likely text-heavy framework graphic, check whether labels need translation
Display summary:
**Translation complete** ({mode} mode)
Source: {source-path}
Languages: {from} → {to}
Output dir: {output-dir}/
Final: {output-dir}/translation.md
Glossary terms applied: {count}
If mismatched image-language candidates were found, append a short note after the summary telling the user that some embedded images may still need image-text localization, followed by the candidate list.
Custom configurations via EXTEND.md. See Preferences section for paths and supported options.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: baoyu-translate is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: baoyu-translate is focused, and the summary matches what you get after install.
baoyu-translate fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added baoyu-translate from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
baoyu-translate has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: baoyu-translate is focused, and the summary matches what you get after install.
We added baoyu-translate from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added baoyu-translate from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: baoyu-translate is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: baoyu-translate is focused, and the summary matches what you get after install.
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