Stylometric Fingerprint: what it measures and what it cannot prove.
Measure sentence rhythm, lexical diversity, function-word density, punctuation profile, repetition pressure, and readability complexity as one descriptive writing fingerprint.
Plain-English method
Stylometry describes how a sample is written. It is useful as a layered signal and as a before/after comparison tool, but it is not courtroom authorship attribution.
Mechanism and scoring
The free fingerprint uses normalized feature extraction over tokens and sentences: coefficient of variation, unique-word ratio, function-word ratio, punctuation density, sentence-start diversity, repetition density, and complexity.
What this catches
- Sentence-length rhythm
- Lexical diversity
- Function-word density
- Punctuation profile
- Repetition pressure
What this misses
- Proof of authorship
- AI-generation verdicts
- Truth, sourcing, or hallucination
This is one signal in a layered stack.
Single-method detectors are too easy to overtrust. Stylometric Fingerprint is useful when it changes routing: allow, revise, human_review, or reject. It should be layered with specificity, provenance, pattern pressure, Unicode sanitation, media provenance, and paid Deep Scan when the decision matters.
score_stylometric_fingerprint is available through local MCP with no LLM cost, and through the remote MCP endpoint with free unauthenticated rate limits. See all detection methodologies and the dedicated methodology page for the deepest treatment of this signal.