linkedin-detector-tester

LinkedIn Detector Tester

Pipes any text through 5+ AI detectors at once and prints how badly they disagree. The point is not to find the "right" score. The point is to show there is no right score.

Why this exists

AI detectors get treated like medical tests. They are not. They are vibe checks with a percentage sign.

The receipts:

• Stanford 2023 (Liang et al., Patterns / Cell Press): 7 AI detectors flagged 61.3% of TOEFL essays from non-native English speakers as AI-generated. Same detectors flagged 5.1% of US-born 8th graders. The bias is against ESL writers, not against AI.
• OpenAI shut down its own AI Text Classifier in July 2023 because it hit only 26% accuracy on AI-written text. The company that builds the AI could not reliably detect the AI.
• Vanderbilt University disabled Turnitin's AI detection citing false-positive risk to students. Other R1 schools followed.
• Newby v. Adelphi University (October 2025): a federal court ordered the university to expunge an AI-cheating violation from a student's record after the only "evidence" was a detector score.
• team test: same article, three detectors, scores 82% / 100% / 50%. That is a 50-point spread on identical text.

If accusations are coming, this skill produces the screenshot.

When to use

• Someone accuses a post, essay, or proposal of being AI-written based on a single detector score
• Before defending a writer publicly, get the spread on record
• As a follow-up to Author's controversial detector post — paste any flagged text, run it, screenshot the divergence
• Internal QA on your product drafts before publishing to high-stakes audiences

Input

Any text. 200+ words gives the most stable spread; under 100 words and detectors get even more random.

Optional: a label (e.g. "ESL student essay", "GPT-4 output", "1995 Carl Sagan column") for the output header.

Output

Text: "<first 60 chars>..."
Length: 412 words

Detector scores (% AI probability):
  GPTZero         82
  Originality.ai  100
  ZeroGPT         50
  Sapling         34
  Copyleaks       91

Min: 34   Max: 100   Spread: 66

Verdict: USELESS — detectors disagree by more than 50 points.
Translation: nobody actually knows. The accusation is a coin flip.

The three verdicts

Spread (max - min)	Verdict	What it means
≤ 15 points	CONSENSUS	Detectors agree. Still not proof, but at least they're not contradicting each other.
16-30 points	MIXED	Some signal, but enough disagreement that no single score is defensible.
31-50 points	DIVERGENT	The detectors are flipping a coin.
> 50 points	USELESS	The spread is bigger than half the scale. Whatever you decide, the opposite detector also "proves" it.

How to run

cd ${HOME}/p/linkedin-skills/skills/linkedin-detector-tester
python3 scripts/test_detectors.py --text "$(cat draft.txt)"

Or pipe in:

cat draft.txt | python3 scripts/test_detectors.py --stdin

Most detectors gate their API behind paid plans. The script supports two modes:

1. API mode — set keys in .env (GPTZERO_API_KEY, ORIGINALITY_API_KEY, ZEROGPT_API_KEY, SAPLING_API_KEY, COPYLEAKS_API_KEY, COPYLEAKS_EMAIL). Detectors with valid keys run automatically.
2. Manual paste mode — --manual flag opens each detector's web UI, prompts the user to paste the score back. Slower but free, and captures detectors with no API.

Files

• SKILL.md — this file
• references/detector-list.md — supported detectors, API endpoints, known accuracy issues, citations
• scripts/test_detectors.py — runs the parallel test, computes spread, prints verdict

Related skills

• linkedin-humanizer — rewrites text after a high score (or before, defensively)
• linkedin-post-audit — pre-publish check that catches AI tells without relying on detectors

What this skill is not

It is not a detector. It does not claim a piece of text is or is not AI-written. It only documents how much the existing detectors disagree, so that a single score can never again be used as a trump card.