Skill

word-frequency

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".

npx claudepluginhub danielrosehill/claude-code-plugins --plugin text-corpus-analysis

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Classical, mechanical. No LLM involvement.

SKILL.md

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Last CommitApr 23, 2026

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Word Frequency

Classical, mechanical. No LLM involvement.

Procedure

Tokenize: spaCy tokenizer (handles punctuation, contractions) or nltk.word_tokenize. For code-adjacent corpora, preserve underscores/hyphens.

Normalize: lowercase. Optionally lemmatize (spaCy) or stem (Porter/Snowball). Lemmatization is usually better for reporting.

Filter:

Remove stopwords (spaCy / NLTK lists). Allow custom additions (user-domain boilerplate).
Remove pure-numeric tokens unless the user wants dates/ids.
Length filter (drop tokens <3 chars).

Count: collections.Counter. For n-grams, nltk.ngrams or sliding window.

Export:

word-frequency.csv: term, count, docs_containing, avg_per_doc
Top-N summary with percentile cutoffs.

Optional enrichments:

TF-IDF instead of raw counts (surfaces distinctive words, not just common ones).
Per-metadata-slice frequencies (e.g. per month, per category) — chain with trend-analysis.

Scale

Trivial. Processes millions of docs on a laptop in minutes. No cost.

Pitfalls

Voice-note corpora: run synonym-cluster first or you'll double-count variants ("GitHub", "git hub", "get hub").

If the user wants "how often am I talking about X" semantically (not literally), escalate to topic-analysis — word frequency alone misses paraphrases.

Word Frequency

Classical, mechanical. No LLM involvement.

Procedure

Tokenize: spaCy tokenizer (handles punctuation, contractions) or nltk.word_tokenize. For code-adjacent corpora, preserve underscores/hyphens.
Normalize: lowercase. Optionally lemmatize (spaCy) or stem (Porter/Snowball). Lemmatization is usually better for reporting.
Filter:
- Remove stopwords (spaCy / NLTK lists). Allow custom additions (user-domain boilerplate).
- Remove pure-numeric tokens unless the user wants dates/ids.
- Length filter (drop tokens <3 chars).
Count: collections.Counter. For n-grams, nltk.ngrams or sliding window.
Export:
- word-frequency.csv: term, count, docs_containing, avg_per_doc
- Top-N summary with percentile cutoffs.
Optional enrichments:
- TF-IDF instead of raw counts (surfaces distinctive words, not just common ones).
- Per-metadata-slice frequencies (e.g. per month, per category) — chain with trend-analysis.

Scale

Trivial. Processes millions of docs on a laptop in minutes. No cost.

Pitfalls

Voice-note corpora: run synonym-cluster first or you'll double-count variants ("GitHub", "git hub", "get hub").
If the user wants "how often am I talking about X" semantically (not literally), escalate to topic-analysis — word frequency alone misses paraphrases.