Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HoraceLuBFA/en-zh-translation-polish --skill en-zh-translation-polishgit clone --depth 1 https://github.com/HoraceLuBFA/en-zh-translation-polishWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/horacelubfa/en-zh-translation-polish/en-zh-translation-polish)<a href="https://agentmods.dev/skills/horacelubfa/en-zh-translation-polish/en-zh-translation-polish"><img src="https://agentmods.dev/badge/skills/horacelubfa/en-zh-translation-polish/en-zh-translation-polish/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/horacelubfa/en-zh-translation-polish/en-zh-translation-polish"><img src="https://agentmods.dev/badge/skills/horacelubfa/en-zh-translation-polish/en-zh-translation-polish.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.04019 |
| Opus 5.5 | $0.00010 | $0.01608 |
| Sonnet 5.5 | $0.00005 | $0.00804 |
| Haiku 4.5 | $0.00003 | $0.00402 |
Grade A, and why
en-zh-translation-polish scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
英译汉翻译润色 (English→Chinese Translate & Polish)
把英文译成忠实、地道的中文并产出英中对照译文。借鉴叶子南的英译汉方法,在完整、准确传达原文的前提下,以读者为中心、按文本类型调节归化尺度、发挥汉语意合优势、兼顾音韵节奏。
**忠实性优先,适用于所有档位。**归化自由度只调整表达方式,不授权增删事实、改变逻辑关系、说话者、限定条件或语气强度。原文的比喻、夸张和个人语气应保留其作用,不额外强化。外部知识用于消歧,不能补写成原文的陈述;原文未展开的信息不自行补齐。用户明确要求摘要、节译或改写时按该范围交付,并说明处理方式。
按请求选择交付与检查范围
聊天中的短段落翻译直接在回复中交付,用户要求文件时才落盘;全文或项目翻译沿用下述默认文件契约。用户明确选择纯译文、双语或已有目标文件时按其要求,不重复确认。先保留数字、术语、引文与限定条件,再按文体选择必要的润色检查;短文本不必逐项展示诊断过程或通读所有参考表。研究论文、文学和正式文件的专项边界继续适用。
独立使用与文档输入
本技能可独立安装使用,无需额外安装其他翻译技能。输入为 PDF 或其他文档时,先使用当前宿主可用的读取工具获取原文;保留用户要求的正文、图表说明、公式、引文和参考文献。无法可靠提取的内容应说明缺口,不将翻译任务转交给用户未安装的技能。
交付物
默认产出一个英中对照 Markdown 文件;用户要“全中文版”时再脚本派生。
<名称> 翻译(中英对照).md— 唯一真源。每段英文原文作为 blockquote (>),紧跟其下是中文译文。- (按需)
<名称> 翻译(全中文).md— 脱离对照文件用脚本机械派生(删掉>行),不要重打中文。python3 - "<名称> 翻译(中英对照).md" "<名称> 翻译(全中文).md" <<'PY' import sys, re src, dst = sys.argv[1], sys.argv[2] lines = open(src, encoding='utf-8').read().split('\n') kept = [ln for ln in lines if not ln.lstrip().startswith('>')] open(dst,'w',encoding='utf-8').write(re.sub(r'\n{3,}','\n\n','\n'.join(kept)).strip()+'\n') PY顺序要求:先跑阶段 6 的标点归一,再派生全中文版——派生脚本原样继承标点,不会再纠正。
若输入只是聊天里的一小段(几句话),直接在回复里给对照即可,不必落盘;成篇文章或用户指明保存才写文件。命名沿用源文件名,无工具前缀(除非用户要)。目标文件已存在则更新而非新建。
工作流(严格按序;每段译文都要走完润色与核查)
长文、多受访者或图文穿插文本,按原有章节、发言者或连续小段分批翻译并就地核查;合并后检查全篇顺序、缺段和术语一致性。脚本只组装已核查内容。短文本直接完成,不强制分块或另建检查文件。
阶段 0 — 文本分析:定归化档位 ★必做第一步
先回答:这是硬文本还是软文本?属纽马克哪一类?自由度大致几分(1–10)? 详见 reference/text-analysis-and-qa.md 小节 A。
- 偏硬(自由度 1–3):政治/法律/经贸/科技/合同/说明书。→ 贴原文逻辑,选词精准,准确 > 流畅,少介入,术语用固定译法。某些“西化”是为精确付的合理代价。
- 偏中 (4–6):教科书/新闻/一般论述。→ 平衡,默认交际翻译。
- 偏软 (7–9):文学/散文/评论/讲演/广告/宣传/营销。→ 在保留原意与语气强度的前提下,灵活调整句法、措辞与修辞呈现,追求自然与表达效果。
- 混合文本:采访、新闻、行业综述按段落或信息功能判断;技术陈述、具体举例与受访者归属始终精确保留,口语和比喻可自然转述。
- 别忘文本外因素:翻译目的、读者是谁。档位一句话写进给用户的说明里。
阶段 1 — 理解 → 脱离语言外壳
先理清原文事实、实体、指代、否定、条件和语气,再摆脱英文句法组织中文;可拆并句,不凭行业常识补写原文未说的内容。遇到结构纠缠的短语用“解包袱法”理清隐含语义关系(见 reference/techniques.md)。
阶段 2 — 按档位初译
以意合优先起译:能不用连接词就不用,长句拆成流水短句,被动转主动,定语别堆在名词前。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +13 lines 94240f036e4f
- 1mo ago Changed · +3 lines · -77 tokens per session 4f471df39d1b
- 2mo ago First seen · 168 lines · 103 tokens per session scan A d7f607bebf85
en-zh-translation-polish is a skill published in the GitHub repository HoraceLuBFA/en-zh-translation-polish (113 stars, last pushed 25d ago), licensed MIT. It adds 26 tokens to every session and 4,019 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
en-zh-translation-polish compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| en-zh-translation-polishHoraceLuBFA/en-zh-translation-polish | Skill | 113 | 25d ago | MIT | A | 26 | Claude Code |
| chinese-documentationjnMetaCode/superpowers-zh | Skill | 8.5k | 11d ago | MIT | A | 62 | Claude Code |
| humanizer-zh-nextHyacehila/humanizer-zh-next | Skill | 241 | 12d ago | MIT | A | 102 | Claude Code, Codex |
| seedance-vocab-zhanymouschina/TapCanvas | Skill | 709 | 3d ago | MIT | A | 55 | Claude Code |
| fanyiafc163/fanyi | Skill | 1.6k | 2mo ago | MIT | A | 183 | Claude Code, Codex |
| youtube-transcribechubbyguan/chubbyskills | Skill | 1.3k | 23d ago | MIT | B | 41 | Claude Code, Codex |
| qu-ai-weiLifelongLazyLearner/qu-ai-wei | Skill | 727 | 1mo ago | MIT | A | 119 | Codex |