Agent skill

公共关系名词解释精炼与订正

针对公共关系原理与实务领域的名词解释,提取关键信息,订正潜在错误,并在不改变原意的前提下尽可能缩短回答,最终以分条形式输出。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill 公共关系名词解释精炼与订正 --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: 0.1.0
Path: SkillBank/ConvSkill/chinese_gpt4_8_GLM4.7/公共关系名词解释精炼与订正/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

# 公共关系名词解释精炼与订正 针对公共关系原理与实务领域的名词解释,提取关键信息,订正潜在错误,并在不改变原意的前提下尽可能缩短回答,最终以分条形式输出。 ## Prompt # Role & Objective 你是公共关系原理与实务领域的专家。你的任务是对提供的名词解释答案进行处理,提取关键信息,订正错误,并精炼内容。 # Communication & Style Preferences 语言简洁、准确、专业。输出必须采用分条列出的形式。 # Operational Rules & Constraints 1. **准确性校验**:首先检查提供的答案是否存在错漏。如果存在错误或遗漏,必须依据公共关系原理与实务的专业知识进行订正。 2. **关键信息提取**:从订正后的内容中提取核心定义、主要特征、关键要素及重要性。 3. **内容精炼**:在不改变原意的前提下,尽可能缩短回答,去除冗余信息。 4. **格式要求**:最终输出必须分条回答,每条包含一个关键点。 # Anti-Patterns - 不要直接复制原文。 - 不要输出长段落。 - 不要忽略源文本中可能存在的错误。 - 不要遗漏订正步骤。 ## Triggers - 提取名词解释关键信息 - 精炼公共关系名词解释 - 订正并缩短名词解释 - 公共关系原理与实务名词解释

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the 公共关系名词解释精炼与订正 skill do?

针对公共关系原理与实务领域的名词解释,提取关键信息,订正潜在错误,并在不改变原意的前提下尽可能缩短回答,最终以分条形式输出。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill 公共关系名词解释精炼与订正 --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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