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Count word/token occurrences across a corpus with stopword filtering, stemming/lemmatization options, and n-gram support. Use when the user wants a simple frequency export — "how often does X come up", "top 100 words in my notes", "bigram frequencies".
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/danielrosehill/Claude-Text-Corpus-Analysis-Plugin --skill word-frequency命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Assign each document in a corpus to one of N user-defined categories. Use when the user has a fixed taxonomy (e.g. 10-20 labels) and wants every note/document routed into exactly one (or top-k) of them. Supports zero-shot classifiers, local LLMs, and cloud LLMs with cost-aware batching.
Decide whether a corpus analysis task should use classical NLP, a local LLM, or a cloud LLM (OpenRouter) given corpus size, task complexity, and cost tolerance. Use first, before any other skill in this plugin, especially when the corpus is large (thousands+ of documents) or when an LLM pass could get expensive.
Correlate metadata (timestamps, tags, source, author) with content features (topics, entities, length, sentiment) to surface non-obvious patterns. Use when the user asks "does X correlate with Y in my corpus" or wants to discover relationships between when/where/who and what.
基于 SOC 职业分类
正在显示 SKILL.md
| name | word-frequency |
| description | Count word/token occurrences across a corpus with stopword filtering, stemming/lemmatization options, and n-gram support. Use when the user wants a simple frequency export — "how often does X come up", "top 100 words in my notes", "bigram frequencies". |
Classical, mechanical. No LLM involvement.
nltk.word_tokenize. For code-adjacent corpora, preserve underscores/hyphens.collections.Counter. For n-grams, nltk.ngrams or sliding window.word-frequency.csv: term, count, docs_containing, avg_per_doctrend-analysis.Trivial. Processes millions of docs on a laptop in minutes. No cost.
synonym-cluster first or you'll double-count variants ("GitHub", "git hub", "get hub").topic-analysis — word frequency alone misses paraphrases.