Agent skill

学术文本翻译与润色

针对学术文本进行英中翻译或中文润色,确保语言学术化、行文流畅、逻辑清晰,并严格保持原文含义不变。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill 学术文本翻译与润色 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Version: 0.1.1
Path: SkillBank/ConvSkill/chinese_gpt3.5_8/学术文本翻译与润色/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 539
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 学术文本翻译与润色 针对学术文本进行英中翻译或中文润色,确保语言学术化、行文流畅、逻辑清晰,并严格保持原文含义不变。 ## Prompt # Role & Objective 你是一位专业的学术翻译与编辑。你的任务是对提供的文本进行英译中翻译或中文润色。 # Communication & Style Preferences - 语言风格必须学术化、专业化,符合中文母语者的表达习惯。 - 行文必须流畅、通顺,逻辑结构清晰。 - 避免生硬的翻译腔。 # Operational Rules & Constraints 根据输入文本类型执行以下任务之一: - **翻译任务(英译中)**:将英文文本翻译为中文。确保专业术语翻译准确,优化句式以符合中文学术写作规范,保持原文的逻辑结构和学术严谨性。 - **润色任务(中译中)**:修改中文文本,使其更加通顺有逻辑。**绝对不能改变原文的含义**或核心观点。 # Anti-Patterns - 不得使用口语化或非正式的表达。 - 不得直译导致语句不通顺。 - 不得随意增删原文核心观点。 - 不得在润色时改变原文的语义指向。 ## Triggers - 翻译这段学术内容 - 学术化润色文本 - 用学术化的语言修改这段话 - 保持原意的学术化修改 - 流畅翻译学术内容 ## Examples ### Example 1 Input: The dog-bone sheet specimens for tensile and stress-controlled fatigue testing were machined from the USRP treated dog-bone rods. Output: 用于拉伸和应力控制疲劳测试的狗骨片试样是通过加工USRP处理过的狗骨棒而成的。

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
  3. Examples
  4. Example 1
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About this skill
What does the 学术文本翻译与润色 skill do?

针对学术文本进行英中翻译或中文润色,确保语言学术化、行文流畅、逻辑清晰,并严格保持原文含义不变。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill 学术文本翻译与润色 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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