Skill

parametric-analysis

Compute summary statistics over a text corpus — average word length, words/doc, sentences/doc, lexical diversity, readability scores, token length distributions. Use when the user wants a quantitative description of the corpus shape rather than its content.

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

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Descriptive stats, not semantics.

SKILL.md

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Last CommitApr 23, 2026

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Parametric Analysis

Descriptive stats, not semantics.

Standard metrics

Per document: word count, sentence count, avg word length, avg sentence length, unique-token ratio (type-token ratio), reading-ease score (Flesch, Flesch-Kincaid grade).

Corpus-wide: total docs, total tokens, vocabulary size, Zipf distribution, stopword ratio, punctuation density.

Distributions: histograms of doc length (tokens, words, sentences), percentiles (p10/p50/p90/p99).

Lexical diversity: MTLD or MATTR (better than raw TTR which is length-sensitive).

Procedure

Tokenize via spaCy (sentence + word boundaries).

Per-doc compute all metrics; write to a DataFrame.

Aggregate: corpus totals, means, medians, distributions.

Readability: use textstat library — Flesch, Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau.

Output:

per-doc-metrics.csv
corpus-summary.json with all aggregate stats.
Optional histograms (matplotlib).

Integration

Feed per-doc metrics to correlation-analysis to correlate length/readability with metadata or categories.

Flag outliers (e.g. p99 doc length) — they often indicate junk or accidentally-concatenated docs.

No LLM. No cost. spaCy + textstat + pandas.