prosemeter

A ruler for prose texture in Russian, English, Spanish and Italian. Pure stdlib, zero dependencies.

prosemeter measures the surface texture of a text — sentence rhythm, punctuation habits, lexical texture, voice — and hands you a flat dict of numbers. That's the whole product.

It is explicitly not an AI detector and not a quality metric: it describes, it does not pass judgment. Some of the features it reports (filler density, contrast frames, opener repetition) happen to be popular tells of generated or padded prose, but prosemeter attaches no verdict to any value. What counts as "too much" depends on your corpus, your genre and your taste — that's your call, on your data.

Install

pip install prosemeter

Usage

from prosemeter import profile

text = (
    "The kettle clicked off and the kitchen went quiet. I had been rewriting "
    "the same paragraph for an hour, and it still read like a brochure. So I "
    "did what editors do: cut the openers, moved the numbers up front, and "
    "read it aloud. Better. Not perfect, but better."
)

print(profile(text))          # lang="auto" detects English here
print(profile(text, lang="en"))  # or say it explicitly

Real output (English adds five extra keys on top of the common core):

{
  "words": 49, "sentences": 5,
  "mean_sentence_len": 9.8, "cv_sentence_len": 0.69,
  "pct_short": 0.4, "pct_long": 0.0, "staccato_share": 0.4,
  "anaphora_top_share": 0.2,
  "dash_per_100w": 0.0, "guillemet_per_100w": 0.0, "questions_per_sent": 0.0,
  "contrast_frames_per_100w": 0.0,
  "address_per_1k": 0.0, "first_person_per_1k": 40.8,
  "digit_density_per_1k": 0.0,
  "filler_per_100w": 0.0, "nominal_per_100w": 0.0,
  "mean_word_len": 4.14, "ttr": 0.8,
  "syllables_per_word": 1.33, "flesch_reading_ease": 84.7,
  "polysyllable_per_100w": 0.0, "passive_per_100sent": 0.0,
  "ly_adverbs_per_100w": 0.0
}

The public API is three names: profile(text, lang="auto"), SUPPORTED_LANGS (("ru", "en", "es", "it")) and __version__. lang="auto" uses a cheap script/function-word heuristic that is reliable on a paragraph of ordinary prose; pass the language explicitly for short fragments or mixed-language text.

Features

Counts are ints; every other value is a rate, so profiles of texts of different lengths are comparable. Rates are per word (per_100w / per_1k), per sentence (per_sent / per_100sent) or a share in 0..1.

Honest limits

Tokenization is regex-only: sentences split on runs of .!?, words on per-language character classes. Abbreviations, decimals and ellipses can over- or under-split. Over a paragraph or an article these errors average out; do not trust the numbers on a single sentence, and do not compare ttr across texts of very different lengths.

Scope is frozen

This package does one thing: it measures. Bug reports (wrong counts, crash on valid input, broken language detection) are very welcome. Feature requests — new languages, new metrics, verdicts of any kind — are out of scope for now.

Built to power Laspi

prosemeter is the measuring half of the writing engine behind Laspi — a marketing employee in a small-business owner's phone. Laspi keeps a living memory of the business (every fact confirmed by the owner), writes platform-native posts, images and short videos from it, and looks after the business's visibility in AI answers — in whatever language the business sells in. These metrics are part of how Laspi keeps that writing sounding human.

The measuring is open source. The writing is the product: laspi.pro.

License

MIT © 2026 GradeBuilder S.L.