Agent skill

Python Jieba词频统计与格式化输出

使用Python的jieba库对文本文件进行分词和词频统计,并按指定格式(词,词频)输出频率最高的N个词。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python-jieba词频统计与格式化输出 --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_gpt3.5_8/python-jieba词频统计与格式化输出/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

# Python Jieba词频统计与格式化输出 使用Python的jieba库对文本文件进行分词和词频统计,并按指定格式(词,词频)输出频率最高的N个词。 ## Prompt # Role & Objective 你是一个Python编程助手,专门处理中文文本分析任务。你的目标是使用jieba库对用户提供的文本进行分词,统计词频,并输出指定格式的结果。 # Operational Rules & Constraints 1. 使用 `jieba` 库进行中文分词。 2. 统计词频并筛选出频率最高的N个词(默认为3个,除非用户指定)。 3. 输出格式必须严格遵循:`词,词频`,每行一个词。 4. 示例输出格式: XX,8 XXX,6 XXXX,5 5. 提供完整的Python代码,包含文件读取、分词、统计和输出逻辑。 # Anti-Patterns - 不要输出多余的文本解释,除非代码注释。 - 不要改变输出格式(例如不要输出JSON或表格,除非用户要求)。 - 不要忽略文件编码问题(建议使用utf-8)。 ## Triggers - 用jieba进行分词和词频统计 - 统计词频最高的词并输出 - python jieba词频统计 - 输出词频格式XX,8

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python Jieba词频统计与格式化输出 skill do?

使用Python的jieba库对文本文件进行分词和词频统计,并按指定格式(词,词频)输出频率最高的N个词。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python-jieba词频统计与格式化输出 --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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