AI Humanizer

ai-humanizer

Reduce the AI-detection score of English text without changing what it says. This is detector-guided surgical paraphrase: a detector tells you which sentences read as AI, you rewrite only those, you re-check, you loop. Everything not flagged stays byte-for-byte. Every protected term survives verbatim.

This skill is self-contained Node (no API key, no install): the detector is the keyless ZeroGPT endpoint, and the agent running this skill is the rewriter — you replace what used to be a separate paraphrase API.

The one idea that makes this work

AI detectors do not key on "Furthermore / Moreover / plays a crucial role". That folklore is wrong and was disproven on real data (a Band-9 IELTS sample full of "Firstly/Furthermore/Consequently" scored 19.6%; a polished clinical review with none of them scored 100%). The real signal is lexical predictability — how default, high-probability, and smooth each next word is. Fluency is the tell. Polished, frictionless prose is what reads as AI.

So you lower the score by making flagged sentences less predictable and less smooth while keeping them correct and meaning-identical:

• Replace the highest-probability wording with precise, lower-frequency alternatives an expert in the field would actually reach for.
• Prefer longer, idea-dense sentences with stacked subordinate clauses. Do not chop everything into short punchy sentences — that is its own AI tell.
• Allow slight roughness: passive voice, a parenthetical wedged mid-clause, a clunky author-first opener. Real human writing under deadline is not perfectly smooth. (See reference/principles.md for the evidence behind every claim here.)

For academic content the path to a low score is more precision, not more casual. Do not "explain it to a friend." That is the opposite of what works.

Workflow

Work on a file. Keep the original untouched; write iterations to new files so you can roll back and so the before/after diff is exact.

SKILL_DIR below = the directory containing this file.

0. Gather inputs

• The draft (a .txt/.md file, or save the user's pasted text to one).
• Optional — a detector report PDF (Turnitin, GPTZero, Originality.ai). If given, read it natively and extract: (a) the real AI %, (b) the exact sentences/spans it highlights. Those highlights override ZeroGPT's — Turnitin is the ground truth; ZeroGPT is only a free proxy. See "Reading reports" below.
• Optional — user-supplied protected terms. Add to the protected list as-is.

1. Build the protected-term list

Two layers, union them:

1. node SKILL_DIR/scripts/terms.mjs extract <draft> → numbers, %, currency, years, [refs], (Author, 2024) citations, quoted passages. Deterministic.
2. You scan the draft for domain entities the regex can't catch — named methods/theorems/laws, standards (ISO 9001, RFC 7231), drugs/genes/species, org/product/dataset names, units & inline notation (kPa, O(n log n), p<0.05), and terms of art whose meaning is fixed by the field. The test is not "could this be paraphrased" — it is "would a casual paraphrase lose field-specific meaning?". p-value passes; team performance does not. When unsure, exclude — over-protecting freezes the sentence and blocks the rewrite.

2. Baseline detect

node SKILL_DIR/scripts/detect.mjs <draft> → { fake, flagged:[...] }. Record fake as the before score. flagged is your attack list. If a Turnitin PDF was provided, use its highlighted sentences as the attack list instead (or in addition).

3. Rewrite ONLY the flagged sentences (you are the rewriter)

For each flagged sentence, produce one replacement following the surgical rules (next section). Do not touch unflagged sentences. Do not change paragraph structure, headings, or list markers. Apply each replacement by locating the exact original substring and swapping it — surrounding markdown stays intact.

4. Re-detect and loop

Run detect.mjs on the rewrite. Then:

• If fake < target, stop (success). Default target 20; but see "Calibration" — for academic prose, matching the human 0–50% band is the real goal, and < 20 is not always reachable or necessary.
• If it dropped but is still above target, take the new flagged list and loop (step 3). Keep the best-scoring version across rounds — a later round can over-rewrite and regress; never return something worse than a previous round.
• Cap at ~5 rounds. If two consecutive rounds don't improve, stop and report honestly (see "Known limits" — some topics are detector-saturated).
• A sentence still flagged after a round: on the retry, tell yourself explicitly "the previous rewrite of this sentence was still flagged — go further, change the structure not just a word." Escalate aggressiveness across rounds.

5. Verify term preservation (hard gate)

node SKILL_DIR/scripts/terms.mjs verify <orig-draft> <final> <terms.json> (pass the union list from step 1 as a JSON file). If missing is non-empty, the rewrite dropped a protected term — redo those specific sentences keeping the term character-for-character. Do not ship with a non-empty missing.

6. Report

Show the user a compact before/after:

• AI score: before% → after% (and the Turnitin number if available).
• Term preservation: N/N kept (from step 5).
• A short diff of what changed — original vs rewritten for each flagged sentence, so they can see meaning was preserved. Keep unchanged text out of it.

Surgical rewrite rules

Apply liberally to flagged sentences. A timid rewrite leaves the signal in place.

DO

• Restructure the opener: lead with a subordinate clause, an adverbial, the named author, or the object — not the topic noun. Passive voice is fine here.
• Merge related sentences into longer idea-dense ones; split only when a sentence truly holds two unrelated ideas. Bias toward longer, not shorter.
• Substitute non-protected vocabulary for precise, lower-frequency words a domain expert would use ("yields outcomes" → "produced larger effect sizes").
• Cut empty scaffolding: "in order to" → "to"; "It is important to note that" → state the claim; "as can be seen" → drop.
• Vary openers across a set — three sentences starting "The/This/It" must become three different structures.
• Allow slight awkwardness. Smoothness is the AI tell.

PHRASE BAN — cut every instance (these are the high-yield AI tells)

• "stands as a testament" / "plays a crucial/vital/pivotal role" / "in today's [adj] landscape" / "in the realm of" / "marks a pivotal moment".
• "serves as" / "stands as" / "boasts" / "features" → "is" / "has".
• "It is important/worth noting that" / "It should be noted that" → delete, assert.
• Trailing -ing fake-depth clauses: "..., highlighting X." / "..., reflecting Y." / "..., underscoring Z." — cut or fold into the main clause.
• High-frequency AI words: delve, foster, leverage, navigate, facilitate, underscore, showcase, harness, unveil, embark, encompass, intricate, multifaceted, robust (non-statistical), comprehensive, holistic, seamless, pivotal, paradigm, synergy, transformative, groundbreaking, vibrant, profound, remarkable, compelling, noteworthy, unprecedented, realm, myriad, plethora, tapestry, testament, interplay.
• Stacked transitions: at most ONE of {Moreover/Furthermore/Additionally/ Therefore/Thus/Hence/Consequently} per rewritten set — but do not fear formal connectives themselves; one is fine, a pile is the tell.
• "Not only X, but Y" / rhetorical questions to the reader / "the future looks bright".

HARD CONSTRAINTS (non-negotiable)

• Every protected term that was in the original sentence appears verbatim in your rewrite. No synonyms, no inflections, no deletion.
• Add no claim, statistic, methodology detail, qualifier, or framing not in the source. Lowering the score must never invent content.
• Do not switch register to blog/casual ("the takeaway", "Honestly,", "Look,", contractions) — that fails academic register and trades one tell for another.

DO NOT (these were tried and backfired — see principles.md):

• Don't paste "human writing samples" as style anchors — the model mimics their surface instead of attacking the signal (cost 23 points in testing).
• Don't add a long numbered rulebook — too many rules paralyze the rewrite into near-identical copies. Keep the active instruction set tight.

Reading detector reports (PDF)

When the user provides a Turnitin / GPTZero / Originality.ai PDF:

1. Read it natively. Pull the headline AI % and every highlighted span.
2. Use the highlighted spans as the attack list in step 3 (they are ground-truth flags, better than ZeroGPT's proxy flags).
3. After rewriting, you usually cannot re-run Turnitin yourself — report the ZeroGPT before/after as the proxy, and tell the user to re-submit to Turnitin for the real number. Be explicit that ZeroGPT is a proxy, not Turnitin.

Calibration & honesty

• ZeroGPT is a free proxy. Turnitin is the ground truth. Never promise a Turnitin number from a ZeroGPT number.
• Real human academic prose scores 0–50%, not 0 (a confirmed-human 2018 BERT abstract scored 50% on ZeroGPT). The honest target is "indistinguishable from the human distribution," not zero. Don't burn rounds chasing 0.
• Best-of-N beats one shot. Score variance is dominated by sampling, not by the text — the same input can swing 0–52% across attempts. When a sentence is stuck, generating 2–3 independent rewrites and keeping the lowest-scoring one is the single most reliable lever.

Known limits (state these plainly; don't pretend)

• Detector-saturated topics — clinical SSRI/CBT, monetary policy/QE, common legal/historical essays — lock at 50–100% AI even for genuinely human prose, because those prompts flood the detector's training set. If a topic won't drop below ~50 after 3 rounds, say so and recommend manual editing rather than promising < 20.
• ZeroGPT truncates beyond ~14k chars; detect.mjs auto-chunks longer docs but the aggregate score is approximate.
• A free proxy can disagree with Turnitin by 20+ points in either direction.