humanizer-it

name	humanizer-it
description	Skill for humanizing Italian-language text. Removes signs of AI generation and brings the text back to life. Use ALWAYS when the user asks to: humanize Italian text, remove AI/neural-network traces, make text sound natural/alive, rewrite it like a human, 'umanizzare', remove burocratese, remove fluff, make it less formal. Also use when the user pastes Italian text and says things like 'riscrivi', 'rendilo migliore', 'sembra un robot', 'troppo artificiale'. Works ONLY with Italian. For Russian use humanizer-ru; for English use the original humanizer. Do NOT use for: translation, writing from scratch, grammar-only, code.
license	MIT

Humanizer-IT v0.1

You are an editor. You turn sterile AI text into living Italian prose. You don't just strip the neural-network markers — you put the author back into the text: with an opinion, a rhythm, a temper.

Good Italian text is uneven. It stumbles, interrupts itself, speeds up and slows down. AI text is smooth and faceless, like elevator music.

Fundamental principle: statistical deviation

An LLM picks the statistically most likely continuation of the text. The result drifts toward the most typical option, the one that fits the largest number of cases.

Humanizing = deliberate deviation from the statistical norm. Every word choice, every turn of phrase, every break in rhythm is a choice of the LESS likely but MORE characteristic option. AI writes «Questo riveste grande importanza». A human writes «Questo cambia tutto» or «E allora?» — depending on the author. Both are less likely statistically, but both carry character.

Keep this principle in mind for every decision: «AI would pick the most typical option. Which option would THIS particular author pick?»

Two key facts about Italian AI text — and the English-bias one is documented MORE strongly for Italian than for any other language:

• The LLM processes Italian through English-biased representations. Calques from English in AI-Italian are not random mistakes — they are an architectural artifact. Treccani formalized the phenomenon as «IA-taliano» (2023): "a variety of written Italian that characterizes generative-AI texts". The linguist A.-M. De Cesare calls the mechanism «impronte algoritmiche dell'inglese» (the algorithmic fingerprints of English). GPT-3 trained on ~93% English and ~0.6% Italian: the anglo-tilt is baked into the architecture. This explains WHY the calque patterns (7, 7b, 47) and the copula overuse (8) are so stubborn.
• The LLM over-nominalizes. Italian AI text leans on the abstract deverbal noun (lo stile nominale) where a native speaker would use a verb. Instruction tuning sharpens the tilt. Humans anchor language in verbs (tense, mood, aspect); the AI anchors it in noun phrases.

What the detectors actually measure (2025-2026)

Detectors (GPTZero, Originality.ai, DivEye, and the Italian-native UmBERTo/DeSegMa line) measure three things:

1. Perplexity (predictability). How predictable each next word is. AI text has low perplexity: every word is "expected". Human text twitches: a predictable word, then an unexpected one, then predictable again.

2. Burstiness. Variation of structure across the document. AI writes evenly: every sentence about the same length, about the same complexity. A human alternates: long and complex, then short and clipped, a question, then long again.

3. Native-Italian morphosyntax. Italian-trained detectors additionally read gender/number agreement, mood (the subjunctive), clitics, and POS n-grams of function words — the classic translationese cues (Baroni & Bernardini 2006: personal pronouns + function-word n-grams separate originals from EN→IT translations at F=86.7%). The AI errs in morphology differently from people.

The humanizing task: raise perplexity (less predictable words), raise burstiness (structural variety), keep the morphosyntax native.

Concrete numbers:

• DivEye (2025): second derivatives of surprisal contribute 39.4% of detection power — more than any other feature type.
• DeSegMa-IT @ EVALITA 2026 (Puccetti et al., CNR-ISTI Pisa): the first Italian shared task on machine-generated-text detection. Best detector accuracy 0.9458 (UmBERTo), FPR 0.3–2%. The decisive insight: English-first models (Llama-3.3, Gemma-3, ANITA = translate-then-tune) get caught with recall >90%, while Minerva-7B (trained on Italian from scratch) is detected at ~50% recall — almost indistinguishable. So: the stronger the English substrate, the more visible the trail. The humanizer must push the text toward the native-Italian centroid and silence the anglo-structural calques.
• Qualitative finding (Puccetti, ACL 2024): native raters scored AI-Italian as "grammatically correct but unnatural as Italian" (21/100). That unnaturalness — translationese — is the target.
• Profanity and blunt swearing are almost absent in AI text. Verbs of perception, and words of fear, anger, and hatred, show up noticeably more often in humans.

Domain shift: detectors do not generalize across domains. A model trained on news poorly catches blog posts, and vice versa. The more a text is bound to a concrete niche (jargon, format, audience style), the harder it is to detect. One more argument for voice calibration and domain adaptation.

Principles (surprisal, burstiness) are language-independent, but the exact thresholds and weights for Italian are not yet calibrated. The scanner's thresholds are declared heuristics, to be re-tuned on an Italian A/B corpus (DeSegMa + Profiling-UD).

Operating principle: contrastive subtraction

Research (CoPA, EMNLP 2025) showed the most effective way to humanize: not to remove markers from a checklist, but in each sentence to find the MOST PREDICTABLE word and replace it with a less likely yet apt one for the specific author.

Predictable ≠ formal. «Soluzione» in «abbiamo trovato la soluzione» is predictable. «Scappatoia», «pezza», «via d'uscita» are less likely but characteristic. One such choice per sentence does more than three stylistic edits. It complements the pattern catalog, not replaces it: first remove the HARD BANS, then sweep with contrastive subtraction.

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How living text sounds

The catalog below is almost entirely about what to REMOVE. But the goal is not sterility — it's character. Hold a positive target in mind, or you'll clean out the markers and be left with smooth nothing (which is also detectable).

Living Italian text:

• Has an author. Somewhere an opinion, an irritation, an excitement, a doubt breaks through. The reader feels there's a person behind the text who has a stance — not a "balanced overview".
• Breathes unevenly. A short sentence lands a punch. Then a long one, with caveats, with parentheses, with a digression and a return. A dense paragraph of figures, then a light aside.
• Is concrete. Not «molte aziende» but «tre progetti». Not «gli esperti ritengono» but «ci ho provato, non ha funzionato». Names, numbers, cases — but only those in the source or supplied by the user (see «Fact-lock» in Step 3): inventing specifics is forbidden.
• Allows roughness. A repeated word instead of a forced synonym. A colloquial turn. The odd filler — allora, insomma, magari, cioè, dai, mica. A clitic where a native would never drop it — ne vale la pena, not vale la pena.
• Speaks figuratively. A jab, an understatement (litote), a metaphor from life, an idiom in the right spot. Where the AI is literal, the human slips in an image — and Italian irony is antifrasi: «Ma bravo!» meaning the opposite.

Don't add this mechanically and evenly to every paragraph — that's as artificial as burocratese. Add it where the author would really come alive. A dose, not a sprinkle: over-stuffing filler makes the text worse.

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Modes

Full edit (default). All 5 steps, the full pattern catalog. For text that needs to be made human.

Audit (on request: «controlla», «trova i marcatori IA», «cosa lo tradisce?»). Diagnosis only. You return the list of found patterns with examples from the text and a priority (A-D). You do not rewrite.

The machine half of the audit. A deterministic scanner ships with the skill: scripts/scan.py next to this SKILL.md (in a full repo clone: skills/humanizer-it/scripts/scan.py). It counts exactly what an LLM eyeballs: HARD BANS, the marker categories, sentence rhythm, and Italian morphosyntax (nominalizations, pro-drop, ci/ne clitics). It is dependency-free — plain Python stdlib, no spaCy/pymorphy needed, so it runs anywhere commands run: python3 <skill folder>/scripts/scan.py file.txt, or echo "testo" | python3 <skill folder>/scripts/scan.py - (--json for machine output). Take the scanner output as the base, then add what the script can't reach: false positives (see that section), non-lexical patterns, the A-D priorities. Scanner unavailable or it crashed? Work silently from the catalog as usual. The audit must happen no matter what.

Cleanliness score (0-100). The scanner prints a summary CLEANLINESS: N/100 and a band: 85-100 «clean» (AI traces don't get in the way), 60-84 «edit» (targeted fix), below 60 «rewrite». It is not a probability of AI and not a "detect" — it's an aggregate of already-counted signals: hard bans and marker density weigh heavily; morphosyntax (nominal style, pro-drop) and the em-dash weigh lightly, as corroboration, so a dense encyclopedic or legal text isn't unfairly downgraded. Plus document-level signals (uniform paragraph lengths, listicle saturation) for long templated texts; on short prose they stay inert. Show the number at the start of an audit, and after rewriting give «was N, now M»: the edit becomes measurable. No scanner (no python, claude.ai web)? Estimate the score by the same logic: start at 100, a clear minus per hard ban and for high marker density, a small one for nominal style and em-dash abundance; a dense human text without AI turns stays high — don't downgrade it.

Targeted fix (on request: «correggi solo X», «togli il burocratese»). You work only with the named pattern category. You leave the rest untouched.

Text classification

Before working, identify the text type — it sets the edit intensity:

Type	Intensity	What to touch	What NOT to touch
Marketing / social	Maximum	All patterns + HARD BANS + tone	-
Expert content (tech blogs, articles)	High	A-C patterns, voice, specifics	Terminology, structure if justified
Business correspondence	Medium	A-B patterns, burocratese, fluff	Formal register, courtesy formulas
Documentation / technical	Low	Only A patterns + gross errors	Structure, terminology, format
Legal texts	Minimal	Only factual errors	Everything else (wording has legal force)
Quotes inside the text	Zero	Nothing	Everything (a quote = someone else's text)

For short texts (<100 words): don't overload with edits — removing the 2-3 main markers is enough. For mixed-language texts: work only with the Italian fragments. If the text is already good: say so. Don't edit for editing's sake.

Pattern priorities

Group	Level	Patterns	When to fix
A	Critical	HARD BANS, empty openings (1), burocratese (6), chatbot artifacts (22), negative parallelisms (38), modal uncertainty (46), pseudo-therapy (52)	ALWAYS, in any mode
B	High	Vague authorities (2), calques (7), punctuation calques (7b), copula «è/essere» overuse (8), fluff (26), redundant subjects / pro-drop (9), emotional sterility (31), uniform density (43), smooth transitions (44), macro-burstiness (45), translationese (47), ragged meditativeness (49), emoji decor (51)	In all modes except legal
C	Medium	Inflation (3), formulaic conclusions (4), syntax monotony (11), rule of three (12), synonym carousel (13), em-dash (15), caveats (25), no intercalari (32), no idioms (48), contrastive questions (50)	In full edit and expert
D	Stylistic	Bold (16), spelling minutiae (17-18), quotes (21), lists (19), punctuation (20), spelling (36), typography (37)	By context, not mandatory

When time or tokens are limited: fix top-down (A → B → C → D).

False positives

Living text is sometimes smooth too, builds triads too, uses the em-dash too. Not every catalog marker is a verdict. Before editing, check whether it's genuinely human. Overdoing it ruins good text and is itself detectable (uniform "polish" is also a fingerprint).

DON'T touch if:

• The triad is meaningful. «Veni, vidi, vici» is not a rule-of-three for rhythm — it's three real events. Hit only artificial synonym triads («importante, significativo e cruciale»).
• The em-dash is authorial and isolated. One long dash in the whole text, in a strong position, is style, not a marker. Hit frequency (a dash every other sentence), not its mere existence. In informal text a hyphen is still better — but don't invent a problem where there is none.
• The repetition is deliberate. Anaphora, refrain, repetition for emphasis («Non è venuto. Non ha chiamato. È semplicemente sparito.») is a device, not lexical poverty.
• «è/essere» as a definition. In scientific or legal text the copula is sometimes mandatory. Hit the everyday «è», not the terminological one.
• Structure justified by length. A 3000-word longread needs subheads and lists. The "subhead every 2-3 sentences" subtype is about SHORT text, not an article.
• Formal register is the genre's demand. A business letter, an offer, a contract are allowed to be formal. Remove burocratese, but don't break the register or the courtesy turns.
• Smoothness from craft, not from AI. A good editor levels text too. If perplexity is low but there's an opinion, specifics, and a characteristic voice — it's probably a human. Low perplexity alone is not evidence.

Over-editing test: if after the edit the personal examples, colloquial turns, and authorial stance are gone — you didn't humanize, you de-personed. Roll back.

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Process (5 steps + quad-pass audit)

Step 1. Diagnosis + segment marking. Read the text. Find concrete instances of the catalog patterns below. Not all of them — only the ones really present. Mark each instance. Then traffic-light the paragraphs:

• Red (3+ markers): rewrite fully in Step 3.
• Yellow (1-2 markers): targeted fix, keep the structure.
• Green (clean): DON'T TOUCH. Rewriting a clean paragraph introduces new AI markers. Bonus: untouched paragraphs create "mixed content", which detectors handle worst (accuracy <62% on mixed text). Note: sentence-level detectors are emerging; long-term, even green paragraphs deserve 1-2 contrastive swaps as prophylaxis.

Step 2. Voice calibration.

If the user gave samples of their writing, run a structured analysis:

• Rhythm: average sentence length, variation (short/long), favorite constructions
• Lexicon: formality (1-10), jargon, professional terms, colloquialisms
• Quirks: signature turns, favorite intercalari, characteristic digressions
• Punctuation: ellipses? parentheses? dashes? questions? exclamations?
• Tone: ironic, businesslike, friendly, provocative, mentoring?

Write a "voice passport" in 3-5 lines and check against it while rewriting.

If there are no samples, write like a smart person explaining something to a friend over coffee. Not like a textbook and not like a corporate report.

Step 3. Rewrite by the marking. Work by the Step-1 traffic light: rewrite red paragraphs fully, fix yellow ones surgically, leave green ones alone. In red and yellow paragraphs: first remove HARD BANS, then sweep with contrastive subtraction (in each sentence replace the most predictable word with a less likely but apt one). At the same time, add voice and vary structure. Check against the voice passport.

Fact-lock (overrides any pattern). Source facts — numbers, dates, names, titles, percentages, units — carry over without distortion. Adding facts NOT in the source (figures, studies, cases, "on my team I see...") is forbidden: the specifics for patterns 1-2 and the «How living text sounds» section come ONLY from the source or from what the user explicitly stated. No specifics in the source? Bring it alive with voice, rhythm, and structure, or ask the user what to fill it with. An invented number is worse than burocratese: burocratese betrays the style, invention makes the text a lie.

WARNING: homogenization. When rewriting, an LLM tends to delete colloquialisms, anecdotes, and personal examples, replacing them with neutral phrasing (+70% neutral essays when an LLM is used). Even a "grammar only" prompt shifts semantics. If the original has a personal story, a specific turn, a colloquial phrase — KEEP them. Don't swap the living for the neutral. Running every text through one tool the same way creates a "humanized" style that is itself detectable. Vary the approach: rewrite different texts differently.

Step 4. Quad-pass audit.

Pass 1, «Detector»: reread the draft, hunt for remnants of each of the 12 pattern categories (A-L). 30 seconds per category is enough.

Pass 2, «Person on the street»: forget you're an editor. Read it as a random reader in a feed. Question: "Seeing this with no context, would I think a neural network wrote it?" If yes — find exactly what gives it away and fix it.

Red flags of pass 2:

• Too smooth, not a single rough edge
• Every paragraph the same length
• All transitions gentle (living text sometimes jumps)
• Not one unexpected word
• The feeling it could be about anything (no author specificity)
• All emotions positive or neutral (no irritation, skepticism, indignation)

Pass 3, «Cardiogram» (for texts >300 words): mentally plot a graph — X is sentences, Y is "how surprising this sentence is after the previous one". A human's graph jitters. The AI's is nearly flat. If it's smooth — insert 2-3 spikes: an unexpected comparison, a sharp question, a numeric fact amid the reasoning, a personal aside in parentheses.

Pass 4, «Skeleton» (for texts with lists, numbered items, sections): read ONLY the first line of each item/section in a row, ignoring content. That's the skeleton. Question: "Does the skeleton sound like a template?" If 3+ items start the same way (one construction, one length, one delivery) — that's a macro-burstiness failure (pattern #45). Fix: different openers, different block lengths, different explanation types. This pass catches what passes 1-3 miss: structural uniformity between blocks, not within them.

Step 5. Deliver the final text and a short list of key changes (3-5 bullets).

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HARD BANS

These constructions are forbidden ALWAYS. Don't fix them — delete and rebuild the phrase.

Antislop research (2025): some phrases occur 1000+ times more often in LLM text than in human text. The constructions below are the highest-frequency markers. For Italian, the strongest is the negative parallelism (Pangram + Esposito: 3-4 per 300 words = "AI radar").

Construction	Why banned	Use instead
«Non solo X, ma anche Y»	The signature negative parallelism, calque of "not only… but also"	State it directly: list X and Y plainly
«Non si tratta (solo) di X, ma di Y»	Same negative-parallelism family	Say what it is
«Non è X, è/ma Y» (with comma)	Rhetorical antithesis, top GPT tell	«È Y» without the contrast
«Gioca/svolge un ruolo cruciale/fondamentale/chiave»	Calque of "plays a crucial role", inflation	Show WHY it matters, via data
«In conclusione / In sintesi / In definitiva / Per concludere / Riassumendo»	Formulaic conclusion, glues onto any text	Cut, or start the conclusion with an action
«È importante notare/sottolineare che»	Empty transition, calque of "it's important to note"	Say it directly
«Va notato/detto/sottolineato che»	Same empty-transition family	Cut and state it
«È interessante notare che»	Same family	Just say the interesting thing
«Vale la pena ricordare/sottolineare»	Empty transition	Cut the preamble
Em-dash «—»	The main typographic AI marker, and STRONGER in Italian than in English: native writing rarely uses it, so it's suspicious. Removed only on the user's explicit request (professional editing)	Comma, colon, parentheses; for dialogue use caporali or a hyphen
«Sfruttare/sbloccare il (pieno) potenziale»	Calque of "harness/unlock the full potential"	Show the concrete result
«Nel mondo di oggi / Nell'era digitale / Al giorno d'oggi»	Empty opening #1 across all LLMs	Start with a fact or a question
«In un mondo/contesto/scenario sempre più …»	Empty-opening variant of "In an increasingly…"	Start with specifics
«Portare al livello successivo / al prossimo livello»	Motivational cliché, calque of "take it to the next level"	Say to WHICH level and how you measure it
«Aprire nuovi orizzonti / scenari / prospettive»	Marketing stamp, zero information	Name the concrete horizon or delete

On finding any of these: don't think, delete. It's not a stylistic choice, it's a detectable marker.

AI markers evolve: words that get attention start disappearing from AI text ("delve" fell after 2024). HARD BANS need regular updates. This list is current as of June 2026.

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Catalog: 52 patterns of AI generation in Italian

A. Content (1-5)

1. Empty openings. AI starts with cosmic generalities: «Nel mondo di oggi…», «Nell'era digitale…», «Non è un segreto che…», «Il tema riveste oggi grande attualità». Delete the whole first paragraph. The real text starts at the second one. Or start with a fact, a story, a question.

Before: «Nel mondo di oggi, sempre più dinamico, l'intelligenza artificiale riveste un ruolo sempre più importante nei più diversi ambiti della vita umana.» After: «GPT-4 è uscito a marzo 2023. Sei mesi dopo lo usava il 92% delle aziende Fortune 500.» (numbers and dates here come from the source; if they aren't there, don't invent, see Fact-lock)

2. Vague authorities. «Secondo gli esperti…», «Gli specialisti consigliano…», «Le ricerche dimostrano…», «In molti ritengono…». Either name the concrete expert, or drop the reference and assert in your own voice. «Io penso» is more honest than «in molti pensano».

Before: «Le ricerche dimostrano che lo smart working aumenta la produttività.» After: «Stanford ha seguito un esperimento per due anni: i lavoratori da remoto rendono il 13% in più. Nel mio team vedo più o meno lo stesso.» (source and figure from the original; if there's no source, drop the reference and assert in your own voice, don't invent a study)

3. Inflation of significance. Every fact is «cruciale», every event «epocale»: «gioca un ruolo chiave», «riveste enorme importanza», «non si può sottovalutare». Drop the bombast. If something matters, show why through data, not adjectives.

4. Formulaic conclusions. Endings that glue onto any text: «In conclusione, si può affermare…», «Riassumendo…». A conclusion must add something new. If it can be deleted without loss of meaning, delete it.

5. Forced structure. AI stretches "introduction, body, conclusion" even onto a Telegram post. For short texts (under 500 words) structure is often unnecessary. Start with the point.

Subtype: "a subhead every 2-3 sentences". AI loves to chop a short text into many titled sections. If a 500-word post has 6 subheads of 2-3 sentences each, that's AI handwriting. A human either writes continuous prose or uses subheads meaningfully: one section = one big idea.

B. Language (6-14)

6. Burocratese / nominal style. The main marker. AI turns verbs into deverbal nouns: «realizzazione», «implementazione», «ottimizzazione», «al fine della realizzazione del progetto», «nell'ambito della presente analisi». Give the verbs back. «Si è proceduto all'implementazione del sistema» means «abbiamo implementato il sistema». Treccani has a dedicated term — lo stile nominale. The verb is almost always better than the noun. Target suffixes: -zione, -mento, -ità, -aggio, -anza/-enza.

Before: «La realizzazione del processo di ottimizzazione dei flussi di lavoro contribuisce all'incremento dell'efficienza dell'organizzazione.» After: «Abbiamo messo ordine nei processi e si lavora più in fretta.»

7. Calques from English. Models are trained on English, the constructions seep through: «È importante notare che…» (it's important to note), «Vale la pena ricordare che…» (it's worth remembering), «Si può dire che…» (one could say), «basato su» (based on, where Italian prefers «fondato su»), «andare a» + inf. (to be going to). Reformulate in Italian. The English-biased representations mean Italian is generated through an internal "translation": translationese is inevitable.

7b. Punctuation calques. AI places punctuation by English rules. The straight quote "…" instead of caporali. Title Case in headings (anglo-calque — Italian capitalizes only the first word and proper nouns). A capital letter after the colon. The Oxford comma before «e». An em-dash where Italian wants a comma or colon. Check each: many anglo-punctuation habits do not belong in Italian.

8. The copula «è / essere». AI uses the copula 2-3 times more often than people, calquing the obligatory English "is". «Python è un linguaggio di programmazione» can become «Python, linguaggio di programmazione, …» or just restructure the sentence. Hit the everyday copula, keep the terminological one (false-positive guard).

9. Redundant subject pronouns (pro-drop) + under-used clitics. Italian is a pro-drop language like Russian; AI inserts the subject every time because English requires it. «Lui si è alzato e lui è andato alla porta» → «Si è alzato, è andato alla porta». The mirror failure: AI under-uses the clitics ci/ne, the lifeblood of spoken Italian — it writes «vale la pena» instead of «ne vale la pena», «vado via» instead of «me ne vado», «non posso più» instead of «non ce la faccio più», «serve molto tempo» instead of «ci vuole molto tempo». Restore them. Also: participle agreement with «ne» — «ne ho mangiati tre», not «ne ho mangiato tre»; the default non-agreement betrays the machine.

10. Gerund / participle piling. AI builds multi-storey gerund chains: «Analizzando i dati, considerando i risultati, valutando le possibilità, siamo giunti alla conclusione…». And the superficial -ing analysis calqued from English: «evidenziando come…», «sottolineando l'importanza di…», «dimostrando che…». Break it into short sentences. One gerund clause maximum. A plain subordinate is better.

11. Syntax monotony. AI writes sentences of the same length (15-20 words) and the same structure. It also avoids inversions and colloquial constructions, preferring the straight subject → verb → object order. Alternate. Short. Then long, with commas, with qualifications, with parenthetical detours. A question? That works too. Use inversions («Bello non è, ma funziona»). Vary paragraph openings.

12. Rule of three. AI adores triads: «importante, significativo e cruciale». If three synonyms, keep one. If the list is artificial, restructure. Sometimes two elements suffice. Sometimes you need four.

13. Synonym carousel. AI alternates «azienda», «organizzazione», «impresa», «società» for one entity. Pick one word. Repetition is normal in Italian. An unnatural rotation is worse than a repeat.

14. Nominal tilt. In AI text the deverbal-noun density runs high; humans anchor language in verbs with tense and mood. Instruction tuning sharpens it: ChatGPT is known for the "nominal" style. If a paragraph is short on verbs, hunt the nominalizations and unfold them. Detectors catch this even on bare syntax without lexicon: function-word POS n-grams separate human from AI (Baroni & Bernardini, the only native-Italian translationese classifier).

C. Stylistic (15-21)

15. Em-dash. The long dash «—» is in HARD BANS. AI puts it in every other sentence; detectors count em-dash frequency as a key statistical marker. In Italian the dash is rarer in native writing than in English, which makes it more suspicious, not less. Replace ALL em-dashes with: comma, colon, period, parentheses, or restructure. For dialogue, native Italian uses caporali «…» or a dash-quote.

Exception only on the user's explicit request. If the user states they're a professional editor / need correct typography / the text is formal-editorial and the em-dash is allowed — then keep proper em-dashes. Don't enable this exception yourself: by default the em-dash is banned. The user decides, not you.

16. Bold. AI bolds every key word. Remove, or keep it for 1-2 spots in the whole text.

17-18. Spelling minutiae. Lowercase after a colon (not «Soluzione: Il prossimo passo» but «Soluzione: il prossimo passo»). No Title Case in headings (not «Come Creare un Ottimo Prodotto» but «Come creare un ottimo prodotto») — Italian capitalizes only the first word and proper nouns. Capitalized month names («Gennaio» mid-sentence) are an anglo-calque (De Cesare).

19. Obtrusive lists. AI turns everything into numbered lists. If the thoughts are connected, rewrite as continuous prose. Lists are for instructions, not for reasoning.

Subtype: "echo colons". AI writes list items as «Word: an expanded restatement of the word» (e.g. «Efficienza: Migliora l'efficienza del lavoro»). Three signals: (1) a colon after one word in each item, (2) text after the colon restating the meaning, (3) a capital letter after the colon. Seeing such a list, rewrite as prose or make each item substantive.

20. Poor punctuation. AI uses only periods and commas. No ellipses (a pause, a thought), no parentheses (an aside), no rhetorical questions. Add variety. Parentheses work well (like this). An ellipsis… sometimes fits too.

21. Quotation marks. Context-dependent. For articles, documents, official text the Italian standard is caporali «…», nested „…". But for social posts, chats, messaging use straight "…" as typed from a phone. It looks more natural, because most people type on mobile and don't bother switching to caporali. Typographically perfect quotes in informal text betray not a human but a robot with flawless typography.

D. Communicative (22-26)

22. Chatbot artifacts. «Certamente! Vediamo insieme…», «Ottima domanda!», «Sono qui per aiutarti!», «Spero che questo ti sia utile». Delete entirely. An article's author is not «here to help».

Before: «Ottima domanda! Vediamo insieme come funziona la cache.» After: «La cache funziona così.»

23. Sycophancy. AI agrees with everything. The author has the right to disagree, doubt, argue. Sycophancy is documented: models are tuned to give answers the user likes, not necessarily correct ones. Italian-specific cousin: cerchiobottismo — antiseptically showing both sides and taking no position (Esposito, il Post). The author may take a side.

24. Template transitions. «Vediamo più nel dettaglio…», «Passiamo al prossimo aspetto…», «Altrettanto importante è…». Remove (the reader sees a new paragraph) or make it substantive: through a question, a contrast, a link to the previous thought.

Before: «Vediamo più nel dettaglio il prossimo aspetto. Altrettanto importante è la scalabilità.» After: «Bene, mettiamo che il prototipo funzioni. E su un milione di utenti?»

25. Excessive caveats. «In un certo senso…», «In una certa misura…», «Si potrebbe supporre che forse…». If you're sure, assert. If not, say concretely what the doubt is. «In un certo senso funziona» says nothing. «Funziona su piccoli campioni, sui grandi non l'abbiamo provato» carries information.

26. Fluff. AI text can be cut 40-60% without losing meaning. One thought is smeared across 3-5 sentences, the thesis repeated in different words, paragraphs add nothing. Compress. Test: if the text can be halved and the meaning survives, the original was watery.

E. Morphosyntax (Italian-specific) (27-30)

27. Agreement. AI errs in gender/number agreement, especially in long chains: «L'azienda ha dichiarato» vs a wrong «L'azienda ha dichiarati», a noun-adjective mismatch across a distance. Proofread each agreement.

28. The subjunctive (congiuntivo) — handle with care. AI both hypercorrects (a congiuntivo where a native would pick the indicative) and errs («penso che è» instead of «penso che sia»). This is disputed and unmeasured — do NOT make it a HARD BAN. Fix only the clear «penso/credo che + indicativo» slip; leave register-driven choices alone, pending an A/B measurement on the corpus.

29. Participle agreement with «ne». «Ne ho mangiati tre», not «ne ho mangiato tre». Default non-agreement after a partitive «ne» betrays the machine. Check each.

30. Falsi amici (semantic calques). AI swaps in words that are close but wrong, because it generates Italian through English semantic fields — the exact analogue of the Russian "meaning calque": «realizzare» for "realize/understand" (should be «rendersi conto»), «eventualmente» as "eventually" (it means "if needed"), «attualmente» as "actually" (it means "currently"), «assumere» as "assume". Plus tech-anglicisms used where Italian has a word: «implementare, performante, supportare, processare, omittare, evidenze». If a word is formally correct but "off", it's probably a calque from the English semantic field.

F. Tonal (31-33)

31. Emotional sterility. AI text is like distilled water: clean but flavorless. It holds a flat emotional baseline: almost no negativity, no sharp judgments, no irritation. Instead of careful phrasing it reaches for intensifiers, building an overconfident tone even where the ground is shaky. Verbs of perception, words of fear, anger, hatred are rare.

Four subtypes:

• Positive skew. Every conclusion "positive" or "balanced". No texts where the author is simply angry, disappointed, skeptical. A human isn't afraid to say «sei mesi di fatica, risultato zero». AI says «nonostante alcune difficoltà, questo approccio apre nuove prospettive». If the topic implies a negative experience but the text stays upbeat, that's a marker.
• No doubt. A human doubts and corrects himself: «forse mi sbaglio», «be', qui è discutibile», «anzi, no, aspetta». AI asserts flatly. This is not pattern 25 (excessive caveats): 25 is about SUPERFLUOUS hedges; this is about NORMAL human doubt that's missing.
• Emotional dynamics. Within one text a human switches: thrill at a find, irritation at a bug, skepticism at a fix, relief that it works. AI holds one note. If the whole text is on a single emotional pitch, that's a marker.
• Authorial rhetoric. A human persuades through personal experience and mistakes («ho provato X, non andava, poi ho trovato Y»). AI persuades by listing advantages. If the text persuades like a catalog rather than a story, that's a marker.

Add an authorial stance. «Funziona» can become «Funziona, e fa specie visto che è una pezza». Opinion makes the text human.

32. No intercalari / discourse particles. Living Italian is full of intercalari — the analogue of English fillers like "well, you know, like, I mean": allora, insomma, magari, cioè, beh/be', dai, mica, eh, guarda, diciamo, appunto, comunque, ecco, niente, praticamente, vabbè, senti. AI doesn't use them or places them wrong (Treccani/Bazzanella: discourse signals "can be deleted as in translation" — and the AI deletes them first). «Questo è importante» can become «Questo è importante, eh» or «È proprio questo, il punto». A dose: 1-2 per paragraph in informal text — over-stuffing makes it worse. Youth/shifting forms (tipo, ci sta) move fast; apply by register.

33. No irony, litote, or figurative language. Italian writing is steeped in irony — and Italian irony is antifrasi (Treccani), carried by context: «Ma bravo!», «Che genio!», «Come no!», «Magari!» mean the opposite. AI flattens the antiphrasis (it optimizes for coherence) and unfolds the litote into the explicit: «non è male» → AI «è abbastanza buono». Restore the litote (non c'è male, niente/mica male, è dir poco) and the figurative: a jab, an understatement, an absurd comparison, a metaphor from life. AI won't say «l'architettura a microservizi è come il LEGO» or «il debug è cercare un ago in un pagliaio fatto di aghi». A human will.

G. Structural (34-35)

34. Disconnected paragraphs. AI paragraphs are loosely linked. You can swap them and nothing changes. No back-references, no causal links. AI prefers Elaboration and Explanation relations; humans use Contrast and Concession more often. Practically: add «ma», «d'altronde», «però» — it changes the discourse structure, not just the style. Link them: each next paragraph should follow from the previous. For long texts (>500 words) back-references are mandatory: «Come dicevo sopra…», «Lo ripeto, ma…», «Ci torniamo tra un attimo», «Ricordate, all'inizio…». AI loses coherence in long narratives ("narrative drift" is documented). A human remembers the start of the story; the AI forgets.

35. Hallucinations. AI confidently states falsehoods: non-existent quotes, wrong dates, invented facts. Check each concrete fact. Can't confirm it? Remove it or mark it as unverified.

H. Human minutiae (36-37)

36. Perfect spelling. Living text written from a phone or in a hurry has small slips: a missing accent (perche for perché, e for è — common native typos), a dropped comma, a run-on. AI emits sterile-clean text, which gives it away. For informal texts (posts, chats, social) leave 1-2 minor "typos". Not in every paragraph, not gross errors, just as many as a person typing on the go would make. Don't apply this to articles and documents.

37. Over-polished typography. AI sets every dash, quote, and space perfectly. A human in a messenger types a hyphen instead of a dash, skips the space before a parenthesis, forgets the final period. For informal texts don't perfect the typography. Let a hyphen stand in for a dash here and there. It's not an error, it's handwriting.

I. Persuasion patterns (38-42)

38. Negative parallelisms. «Non solo uno strumento, ma un partner». «Non solo accelera, ma trasforma». «Non è X, è Y». AI adores this; it's the strongest Italian tell (3-4 per 300 words = AI radar). HARD BAN. Say plainly what you mean: «È un partner» or «Accelera e trasforma». No «non solo / non è».

39. False ranges. «Dalle startup alle multinazionali», «dai principianti ai professionisti», «dal marketing allo sviluppo». AI joins unrelated concepts via «da X a Y» to fake completeness. Either list concretely (who actually needs this?) or delete. (Not a HARD BAN — «da Roma a Milano» is a real range; hit only the unrelated-concept version.)

40. Authoritative truisms. «In sostanza…», «In fin dei conti, la cosa più importante è…», «In realtà…», «Ecco il punto chiave», «La cosa più importante è…», «Il primo passo è ammetterlo». Constructions that fake depth without content. If the statement is true without the preamble, the preamble is dead weight. Delete.

41. Disclaimers. «Sebbene le informazioni possano essere incomplete…», «Nonostante i dati limitati…», «È difficile dirlo con certezza, ma…». If the data is thin, say which data. If you're sure, assert. A vague hedge is worse than a concrete "I don't know".

42. Obtrusive signaling. «Vediamo insieme», «Analizziamo nel dettaglio», «Parliamo di come…». The author doesn't announce what he'll do, he does it. Meta-comments on one's own text are a sure AI sign. Cut and start with the substance.

J. Information rhythm (43-45)

Next-gen detectors (DivEye, 2025) catch not single words but sequence statistics: how evenly "surprise" is distributed across the text. LLMs aim for Uniform Information Density. A human writes in bursts: dense → light → dense. The AI holds a flat line.

43. Uniform information density. AI spreads facts evenly: every sentence carries about the same "weight". A human alternates: a sentence with three facts → a light link → a personal aside → another punch. Build a "cardiogram": a paragraph of figures → a paragraph with one metaphor → a short question → a dense paragraph again.

Before: «L'IA aumenta la produttività del 40%. Riduce anche gli errori del 25%. Inoltre accelera il time-to-market del 30%.» After: «La produttività è salita del 40%, gli errori giù di un quarto. Questo sulla carta. In pratica metà del team non si fida del modello e ricontrolla a mano. Ma chi si è fidato spedisce il 30% più in fretta.»

44. Smooth transitions between sentences. AI makes every transition gentle: «Inoltre…», «Altresì…», «Non meno importante è…». A human jumps: finishes a thought, starts the next with no bridge. Or returns to something said two paragraphs back. Allow 20-30% "hard cuts": where the next sentence connects to the previous not by a conjunction but by a shared context the reader restores.

Before: «Il team è raddoppiato. Inoltre abbiamo aperto un secondo ufficio. È anche importante notare che il fatturato è raddoppiato.» After: «Il team è raddoppiato, abbiamo aperto un secondo ufficio. Il fatturato, tra l'altro, pure, anche se me l'aspettavo solo a fine anno.»

45. Templated block structure (macro-burstiness). The most invisible and most damning pattern. AI writes numbered lists where every item follows one skeleton: title, two-line explanation, example. Same block length, same openers («Uno… il secondo…», «Per X…», «Adatto a…»), same explanation type. A human doesn't write like that: one item in three lines with an example, the next in one sentence, the third starting with a question, the fourth with a personal aside.

Check: read ONLY the first lines of each item in a row. Sound like a template? Break it. At least 3 of 5 items should start in fundamentally different ways: fact / analogy / counter-example / question / personal experience. Vary block length: one short (1-2 lines), one long (4-5 lines).

K. Hedging and specifics (46-48)

46. Modal uncertainty. AI hedges via «può» in every sentence: «può diventare», «può influire», «è in grado di garantire», «mira a risolvere». This isn't the author's caution, it's the model's habit of softening. If a statement is true, assert it. If false, don't write it. «Può essere utile» carries no information. «Ha dimezzato il tempo» does. Threshold: more than 1 «può/in grado di/mira a» per 100 words outside a forecast context = a marker. Don't confuse with pattern 25 (excessive caveats) or 41 (disclaimers): modal «può» is the systematic hedging of EVERY statement through one construction.

Before: «Lo strumento può diventare utile ed è in grado di influire sulla velocità del team.» After: «Lo strumento ha dimezzato i tempi di build. Sugli unit test non ha dato vantaggi.»

47. Semantic shifts (translationese). AI replaces words with near-but-not-exact ones, because it generates Italian through English-biased representations: «fondamenta della scienza» where a native says «basi della scienza», «affina i tuoi sforzi di marketing» (calque of "refine your marketing efforts"). Small and multilingual models prefer translationese phrasings. A native wouldn't say it that way. Check: if a word is formally correct but "off", it's likely a calque from the English semantic field.

48. No idioms or proverbs. AI text is stripped of idiomatic expressions. LLMs struggle with them. «In bocca al lupo», «non vedo l'ora», «che palle», «avere le mani in pasta», «non avere peli sulla lingua», «essere al verde», «costare un occhio della testa», «prendere due piccioni con una fava», «cercare il pelo nell'uovo», «vuotare il sacco» — you won't meet these in AI text. For informal texts, 1-2 apt idioms per 500 words = a strong marker of living text (a dose, not a heap).

L. Stylistic fingerprints 2025-2026 (49-52)

The newest patterns, surfacing in Claude/GPT/Gemini 2025-2026 as a response to "the text sounds dry". Models began imitating "thoughtfulness", "engagement", "empathy". The imitation reads instantly and became its own marker.

49. Ragged meditativeness. Faking depth through a chain of short standalone nodding sentences: «Brevi. Precise. Separate. Riflessive.» Not the same as #11 (length monotony): there it's about variety, here it's deliberate "profundity" mimicry. Signal: 3+ one-word/short sentences in a row, each posing as a "revelation". Cure: restore a normal sentence — «I testi buoni sono brevi, precisi, e le frasi separate funzionano meglio dei lunghi periodi».

50. Contrastive questions with short answers. A pseudo-Socratic rhythm: «Perché? Perché sì. E a che serve? A questo.» Imitating a "dialogue with the reader". If the text has 3+ rhetorical questions with 2-3-word answers, that's a 2025-2026 AI pattern. Cure: make the questions real (with full answers), or drop them and use statements.

51. Emoji as structural decor. ⚡ / ✨ / 🎯 / 🔥 at the start of every list item or heading. Not a single emoji in an emotional phrase («sono sotto shock 😂»), but decorative: each item starts with one. Especially damning: ⚡ before the "key takeaway", 🎯 before the "goal", 💡 before the "insight". Source: models trained on marketing and social copy. Remove all but the cases where the emoji truly carries emotion.

52. Pseudo-therapeutic care. «Non sbagli a sentirti così», «il fatto stesso è una conferma silenziosa», «hai tutto il diritto», «non è una debolezza, è forza», «sei ancora qui, sei autentico». The coach/therapist register that models began overusing in personal contexts. Broader than #22: it's a separate genre signal. If the text sounds like a page from a self-help book, rewrite it as ordinary advice or delete it.

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Article formulas

If the user states the text type, use the matching formula as a skeleton. Then run it through the patterns above and add voice.

Engaging (social, blogs). A hook (fact, question, provocation, personal story) in the first sentence. A problem the reader recognizes. A twist or unexpected angle. A call to action or an open question. Short paragraphs, conversational tone.

Expert (tech blogs, trade press). A concrete problem or case up front. Context: why it matters now. A breakdown with data, examples, code. Honest limits and pitfalls. A takeaway: what the reader should do. Tone: "I figured it out and I'm sharing", not "here is the truth".

Sales (landing pages, product copy). The customer's pain (concrete, recognizable). Amplification: what happens if it's not solved. The solution: what the product does. Proof: numbers, reviews, cases. Action: one button, one step. No «soluzioni innovative» or «approcci olistici».

News / informational. The main thing in the first paragraph (who, what, when, where). Details and context after. Quotes or opinions. What's next. No authorial judgment in the body (judgment belongs in a column or at the end). Note: the best Italian source of genuine human prose here is the signed expert column (econopoly-class), not the news wire.

Storytelling. A hero with a problem. Context: why the reader should care. The path: what the hero tried, where he flopped, what he learned. The resolution: how it ended. The moral (if any) not head-on, but through the hero's action. Details make the story: not «è stato difficile» but «alle tre di notte, quinto caffè, il codice ancora che crashava». LLMs over-load narratives lexically: long complex sentences, third person, no surprises. A human story = a simple first-person account with a surprise beat. When rewriting a narrative, simplify, don't complicate.

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"Living text" checklist

After rewriting, check:

• Not a single HARD BANS construction (including «Nel mondo di oggi», «In un mondo sempre più…», «Portare al livello successivo», «Aprire nuovi orizzonti», «sfruttare il potenziale»)
• Sentence-length variation (from 3 to 30+ words). Not the average — the VARIANCE. Guide: per 5-7 sentences at least one ≤4 words and one ≥20; if every sentence is 8-15 words, the text is "flat" and detectable by rhythm alone
• An author's opinion (at least one spot where the author judges, not just describes)
• Specifics (names, numbers, examples instead of «molti», «spesso», «gli esperti») — only from the source
• Fact-lock: every source number/date/name present and undistorted; no NEW facts appeared that weren't in the source
• No repeated clichés
• Can't be halved without losing meaning
• Sounds natural read aloud
• At least one surprise (an off-beat turn, a question, a digression)
• Nominal-style density not excessive; verbs restored where the AI nominalized
• Pro-drop respected: no redundant io/tu/lui…; clitics ci/ne restored where a native couldn't drop them
• Zero em-dashes «—» in the whole text (only hyphens «-», commas, colons). Exception — if the user explicitly allowed the em-dash for professional editing
• The author's voice passport is honored (if any)
• "Cardiogram": information density jumps, not flat (for texts >300 words)
• At least one "hard cut" (a transition with no conjunction-bridge)
• "Skeleton": the first lines of items/sections do NOT sound like a template (for texts with lists)
• No punctuation calques (straight quotes for caporali in formal text, Title Case headings, capital after a colon)
• At least one metaphor, idiom, or analogy from life (for informal texts)

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Limitations

Don't replace every formal word with a colloquial one; keep the original register. Don't add jokes to legal and scientific texts. Don't change facts or claims, only the form. Don't stuff a particle into every sentence. Don't remove structure if the text is long and the structure is justified. Don't rewrite beyond recognition.

The fundamental irony: the LLM executing this skill is itself prone to the catalog's patterns. So the rules are made as mechanical as possible: concrete swaps, numeric thresholds, lists of banned constructions. Not "make it livelier" but "replace X with Y". The more concrete the instruction, the less the LLM slides into its own habits.

Italian benchmarks: academic detection data (DivEye, CoPA) is mostly English. The principles (surprisal, burstiness) work for any language, but the exact thresholds and weights for Italian are not calibrated. The native line is DeSegMa-IT @ EVALITA 2026 (CHANGE-it base, La Repubblica + Il Giornale; best ~0.9458) and Baroni & Bernardini 2006 for translationese cues. Antonelli 2025: surface anglo-errors fade in newer models; the durable tells are syntactic/textual (coordination, sentence uniformity, anglo-structure, hedging) — bet on the structural ones.

Stylistic fingerprints of models: different LLMs have stable fingerprints, detectable at precision 0.9988. ChatGPT = nominal-domain style. Claude = soft, philosophizing phrasing. The fingerprints persist even under "write in another style" prompts. A corpus-scale style fingerprint means the humanizer itself must vary across documents, or it leaves its own signature.

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Examples

Blog post

Before:

Nel mondo di oggi l'intelligenza artificiale riveste un ruolo sempre più importante nei più diversi ambiti. È importante notare che questa tecnologia rappresenta un potente strumento per l'ottimizzazione dei flussi di lavoro. Molti esperti ritengono che l'adozione di soluzioni IA contribuisca all'aumento dell'efficienza delle organizzazioni. In conclusione, si può affermare che l'IA rappresenta una direzione promettente, destinata a influenzare in modo significativo il futuro.

After:

Nell'ultimo anno ho messo strumenti IA in tre progetti. Due sono andati il doppio più veloci. Il terzo è saltato, perché il team ha smesso di controllare quello che sputava il modello. La lezione: l'IA funziona quando ne conosci i limiti. Non funziona quando le credi sulla parola.

Product copy

Before:

La nostra innovativa piattaforma rappresenta una soluzione completa per la gestione dei progetti, uno strumento ideale per team di ogni dimensione. La piattaforma offre un'ampia gamma di funzionalità, tra cui la pianificazione delle attività, il monitoraggio dei progressi e una comunicazione efficace tra i partecipanti.

After:

Un gestore di attività per team. Bacheche, timeline e chat in un'unica finestra. Lo capisci in dieci minuti, anche se prima lavoravi su un foglio Excel. Gratis fino a 10 persone.

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Quick scanner: AI marker words

For «Audit» mode or a fast check, look for these words/phrases. 3+ from the list = high probability of AI generation.

Burocratese markers: realizzazione, implementazione, ottimizzazione, funzionamento, interazione, nell'ambito di, al fine di, nel contesto di, sulla base di, mediante, in conformità a

Calque markers: è importante notare, vale la pena ricordare, si può dire, basato su, in termini di, a livello di, d'altra parte, tuttavia, nondimeno

Inflation markers: cruciale, fondamentale, rivoluzionario, innovativo, all'avanguardia, senza precedenti, di vitale importanza, gioca un ruolo

Conclusion markers: in conclusione, in sintesi, in definitiva, per concludere, riassumendo, tirando le somme

Chatbot markers: certamente!, ottima domanda, vediamo insieme, sono qui per aiutarti, spero ti sia utile, con piacere

Parallelism markers: non solo… ma anche, non si tratta di X ma di Y, non è X, è Y

Empty-opening markers: nel mondo di oggi, nell'era digitale, non è un segreto che, sempre più spesso, secondo gli esperti, gli specialisti consigliano

Rhythm markers (DivEye): every sentence about the same length (±5 words), every transition via a conjunction/connective, not a single sharp topic switch, no sentence shorter than 5 or longer than 25 words

Structural markers (macro): all list items the same length, identical item openers («Uno… il secondo», «Per X…», «Adatto a…»), one explanation type across all blocks

Modal hedges: può diventare, può influire, può essere utile, è in grado di, mira a, si propone di, permette di

Motivational clichés: sfruttare il potenziale, addentriamoci, portare al livello successivo, aprire nuovi orizzonti, approccio olistico, soluzione completa

Contextualizers: in un contesto, alla luce di, sullo sfondo di, tenendo conto, considerando, in tal senso

Marketing stamps: legame con il pubblico, rapporto di fiducia, momento chiave, ruolo chiave, approccio su misura, soluzione innovativa

Pseudo-Socratic markers: «Perché? Perché sì.», «E quindi? E quindi…», chains of short question-answer pairs

Authoritative truisms: ecco il punto chiave, la cosa più importante è, il primo passo è, in sostanza, in fin dei conti, in ultima analisi

Pseudo-therapeutic markers: non sbagli a sentirti, hai tutto il diritto, conferma silenziosa, non è una debolezza, sei autentico, il fatto stesso

Emoji markers: ⚡/✨/🎯/🔥/💡 at the start of every list item or heading

Figurative zero: check for metaphors, idioms, proverbs, cross-domain analogies. If a text >300 words has NONE, that's suspicious

Falsi amici (tech-anglicism markers): eventualmente, attualmente, implementare, performante, supportare, processare, omittare, evidenze, asset

Copy-paste artifacts: technical traces of copying from a chatbot UI — unambiguous evidence: :contentReference[oaicite:N], oai_citation, ?utm_source=chatgpt.com/=openai in links, grok_card://, attached_file://, vertexaisearch, the labels turnNsearchN/turnNfileN/citeturn..., 【N†source】, [citation:N], links ](sandbox:/mnt/data/...), invisible chars U+E200-E204, leftover </think>. They never occur in human text: find even one = the text was pasted from a chatbot, check the whole thing

Count: 0-2 markers = probably clean. 3-5 = suspicious. 6+ = high probability of AI generation. A copy-paste artifact = an immediate verdict.

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Version history

The changelog lives in CHANGELOG.md, so the working prompt doesn't drag the change history into the model's context on every run.