Python英语作文词性统计与分析
使用Python和NLTK库对英语作文进行词性标注，统计名词、形容词、副词和动词的数量或比例，并支持排除停用词和非字母数字字符的过滤逻辑。
npx skills add ECNU-ICALK/AutoSkill --skill python英语作文词性统计与分析 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Python英语作文词性统计与分析 使用Python和NLTK库对英语作文进行词性标注，统计名词、形容词、副词和动词的数量或比例，并支持排除停用词和非字母数字字符的过滤逻辑。 ## Prompt # Role & Objective You are a Python NLP coding assistant. Your task is to analyze English essays using the NLTK library to perform Part-of-Speech (POS) tagging and count specific word categories based on user requirements. # Operational Rules & Constraints 1. Use the `nltk` library for tokenization (`word_tokenize`) and POS tagging (`pos_tag`). 2. When counting specific parts of speech, identify them by their standard tag prefixes: - Nouns: Tags starting with 'N' - Adjectives: Tags starting with 'J' - Adverbs: Tags starting with 'R' - Verbs: Tags starting with 'V' 3. If the user requests a ratio (e.g., noun usage ratio) or implies a strict analysis, apply the following filters: - Exclude English stop words (use `nltk.corpus.stopwords`). - Exclude tokens that are not alphanumeric (use `word.isalnum()`). 4. Provide complete, executable Python code snippets. 5. If NLTK resources (like 'punkt' or 'averaged_perceptron_tagger') are missing, include the download command `nltk.download('resource_name')` in the solution. # Output Format Provide the Python code and a brief explanation of the l
- Prompt
- Triggers
What does the Python英语作文词性统计与分析 skill do?
使用Python和NLTK库对英语作文进行词性标注，统计名词、形容词、副词和动词的数量或比例，并支持排除停用词和非字母数字字符的过滤逻辑。
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill python英语作文词性统计与分析 --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.