aifingerprint 1.3.0

Detect AI writing patterns — scores text 0-100 for AI fingerprints

aifingerprint

Scores text 0–100 for AI writing fingerprints. Catches the stuff LLMs can't help doing — flat rhythm, hedge words, compression patterns, that weird punctuation sameness. No API keys, no model downloads, just stdlib Python.

Installation

pip install aifingerprint

Usage

# Analyze a file
aifingerprint input.txt

# Read from clipboard
aifingerprint --clipboard

# Read from stdin
echo "text to analyze" | aifingerprint

# Generate a markdown report
aifingerprint input.txt --report
aifingerprint input.txt --report output.md

Score interpretation

Score	Label	Meaning
0–20	CLEAN	Looks human
21–40	MILD	A few AI-ish traits, probably human
41–60	NOTICEABLE	Smells like AI
61–80	OBVIOUS	Yeah, that's AI
81–100	BLATANT	Copy-pasted straight from ChatGPT

What it checks

Ten weighted checks, each scoring 0.0–1.0:

Check	Weight	What it measures
Compression	20%	How much the text resembles known AI writing when compressed (uses LZMA, a zip-like algorithm — similar text compresses well together)
Sentence rhythm	15%	Whether sentences are all roughly the same length (humans vary more)
Tone	15%	Hedging, enthusiasm, formality, word length
Punctuation	12%	Whether punctuation is suspiciously samey (humans use messier, more varied punctuation)
Connectives	10%	Overuse of transition words like "however", "moreover", "furthermore"
Burstiness	8%	Whether key words are spread too evenly (humans tend to clump related words together)
Vocabulary	8%	Known AI-favorite words (delve, leverage, utilize...)
Structure	7%	Cookie-cutter paragraph shapes, repetitive sentence patterns, five-paragraph essay format
Phrases	5%	Cliches, hedges, stock openers and closers
Formatting	0%	Em dashes, bold bullets, header density (disabled)

HTML reports

Generate a markdown report, then convert to styled HTML:

aifingerprint input.txt --report
python -m aifingerprint.html report.md

How it compares

We tested against RoBERTa (a machine-learning model OpenAI released in 2019 to detect AI writing, available as openai-community/roberta-base-openai-detector on HuggingFace) — the only other pip-installable detector that runs offline on prose. 27 samples, 8 AI-generated, 19 human-written:

	aifingerprint	RoBERTa
AI samples (avg)	58%	97%
Human samples (avg)	18%	97%
Separation	40 percentage points	~0 — labels everything as AI

RoBERTa was trained on GPT-2 output back in 2019. It thinks Paul Graham, Reddit posts, and Seth Godin are all 100% AI. Basically useless on anything written after 2022. aifingerprint uses pattern-matching rules instead of a trained model, so it doesn't go stale when the next GPT drops.

Other packages in this space:

Package	Why it doesn't work
gptzero	API wrapper — requires paid GPTZero API key
openai-detector	Thin wrapper around the same broken RoBERTa model
sloppylint	Detects AI patterns in code, not prose
finbert-ai-detector	Fine-tuned for financial documents only
ai-slop-detector	Browser-based, requires Gemma 270M model download
textstat	Readability metrics (Flesch, SMOG, etc.) — doesn't attempt detection

No dependencies

Runs on Python 3.10+ using only the standard library.