Humanize Text Skill

Transforms AI-generated or flat text into output that mirrors the statistical and stylistic fingerprint of human writing. Grounded in the published detection literature; sources live in references/research.md (background only, not needed during a rewrite).

Hard rules (read first, enforce last)

These seven fail more often than everything else combined, because the model that wrote the draft is the model checking it. You systematically overproduce these patterns; your draft contains em dashes even when you don't remember writing them. Treat "my draft is probably clean" as false by default.

1. Em dashes: at most one per 300 words of output. Under 300 words, zero.
2. Semicolons: none, unless a list item itself contains commas or the register is explicitly formal/academic (Lever 8).
3. Straight quotes and apostrophes only. Never curly. Sole exception: publishing contexts where typographic quotes are house style (Lever 8).
4. Banned vocabulary (full list at end of this skill). Highest-frequency offenders: delve, leverage, utilize, robust, comprehensive, streamline, furthermore, moreover, "it is important to note".
5. No negation framing: "not just X", "not X, it's Y", "it's not about X, it's about Y", "more X than Y". Say what the thing IS. Poetic forms count: "isn't proof you failed, it's proof you showed up" is the same banned pivot wearing a nicer coat. (False binaries like "either X or Y" are handled by the Signal I checklist's either/or item.)
6. Output shape: the rewritten text only. No preamble ("Here's the humanized version:"), no trailing changelog ("Main moves:", "What I changed:"). The ONLY permitted additions are the two meta-notes mandated by protocol steps 2 and 5.6, appended after the rewrite. If the user wants the diff explained, they'll ask.
7. Sentence-length spread: in any output over ~80 words, the longest sentence must beat the shortest by 20+ words, and fewer than half the sentences may sit in the 10-to-20-word band. Your uncorrected rhythm clusters at 10-20 words with ~6 words of deviation; that uniformity is a measured tell even when every other rule passes. Verify from the written count list (step 5), never by feel.

These apply in EVERY register, including creative, lyrical, and narrative prose. An em dash in a poem is still an em dash to a detector, and creative registers are where the "this one is doing literary work" rationalization kicks in hardest.

Enforcement is positional: stated here at the top, checked at the END against the finished draft by re-reading the actual draft text and counting occurrences (protocol steps 4-5). Never mark them clean from memory. If long context forces you to drop every other rule in this skill, keep these seven.

Mental model: what detectors actually measure

Nine signals: eight stylometric plus the RLHF fingerprint. Your output must move in the human direction on ALL of them.

The levers below are the write-side counterparts of the signals ai-check grades (A–I): 1→A, 2→B, 3→C, 4→D, 5→E, 6→H, 7→F, 8→G, 9→I (RLHF subset). The full rhetorical-scaffolding catalog for Signal I is enforced by the audit pass (step 5.5), not by any single lever.

Nine humanization levers, apply all of them

Lever 1: Perplexity injection (word-level)

Replace predictable vocabulary with words a real person would choose given this context:

• Swap generic verbs for specific ones: "address" → "untangle", "utilize" → "lean on", "implement" → "wire up"
• Let the subject matter suggest the vocabulary: a Go engineer says "flush the buffer", not "clear the temporary data storage"
• One or two genuinely surprising but accurate word choices per paragraph
• Avoid: "delve", "leverage", "robust", "streamline", "significant", "comprehensive", "notably", "it is worth noting", "in today's fast-paced world"

Watch for elegant variation (synonym cycling). LLMs cycle synonyms for the same referent: "The protagonist faces challenges. The main character must adapt. The central figure triumphs." Same person, three labels. Rule: pick the canonical noun per referent and use it consistently; vary with a pronoun, not a synonym. "the company / the firm / the organization" → "the company" + "it".

Lever 2: Burstiness injection (sentence-level)

Enforce sentence length variance. Target: standard deviation of sentence word count > 8. You can't compute stdev mentally, so enforce these two countable proxies instead; BOTH are required (hard rule 7):

• Range floor: longest sentence minus shortest ≥ 20 words. In practice: at least one fragment of 5 words or fewer AND at least one 25-plus-word sentence that earns its length.
• Mid-band cap: fewer than half the sentences in the 10-to-20-word band. Satisfying the range floor with one fragment and one long sentence while everything else sits at 12-16 words still reads uniform; the middle must spread too.

Supporting rules:

• Every 3–4 sentences, insert one sentence of 5 words or fewer. Just drop it. Like that.
• Never more than 3 consecutive sentences within 5 words of each other in length.
• Burstiness fails in both directions: a run of shorts without a longer counterweight reads choppy, not punchy.
• Mid-paragraph uniformity trap: openers and closers vary, but the middle 3–4 sentences collapse into the same band. Break one.

Lever 3: Hedge surgery

Audit every softening word:

• Delete: "it is important to note that", "it is worth mentioning that", "generally speaking", "in many cases", "it can be argued", "often", "typically" (unless genuinely needed for accuracy)
• Replace with direct assertion: "This matters because X" not "It is important to consider that X may be relevant"
• Real uncertainty gets human phrasing: "I'm not sure this holds for edge cases, but..." not "while results may vary"
• Don't soften real rules with "almost always" / "generally". If exceptions exist, name them: "This breaks down when X."
• No announcement-colon openers: "The rule I use:", "The key insight:" — state the rule directly. (Pattern announcement in general is a Signal I checklist item.)
• A sentence doing two logical jobs (comparison AND conclusion) usually reads cleaner as two.

Filler-phrase substitutions (the pattern generalizes: any multi-word wrapper around a one-word meaning gets the one word):

Lever 4: Structural flattening

Rhetorical scaffolding patterns (either/or binaries, chiasmus, tricolons, balanced parenthetical pairs, anaphora, "turns out" pivots, thesis-first openers incl. "X is the easy/hard part", mini-aphorism closers, parallel-subject mirrors) are catalogued ONCE in the Signal I checklist (step 5.5); negation pivots live in hard rule 5 and the step-4 diminishment scan. Apply the checklist at write time too. This table covers only what the checklist doesn't:

Lever 5: Specificity insertion

Every abstract claim needs a grounding anchor (a number, a name, a date, a concrete example). "Performance improved significantly" → "Latency dropped from 340ms to 80ms under the same load profile." If specifics aren't available, use plausible-specificity frames: "when you're running at X scale...", "in the cases I've seen...", "the one time this bit us..."

Lever 6: Voice and register

Human writing carries the writer's perspective:

• First-person where natural ("I find that...", "In my experience...")
• Occasional second-person direct address ("If you've ever debugged this...")
• Mild rhetorical questions as transitions: "So why does this matter?"
• Self-interruption mid-thought: "— actually, that's not quite right —", "more precisely:"
• Contractions in conversational registers: "don't", "it's", "you'll"

Lever 7: Discourse coherence (non-AI transitions)

Lever 8: Punctuation normalization

Em dashes (—). The most reliable single AI tell; AI uses them at 3–5× the human rate.

• Maximum one per 300 words; under 300 words, zero (hard rule 1)
• Only when a parenthetical genuinely interrupts rather than extends — not as a fancier comma
• Most uses replace cleanly with a period, a comma, or cutting the aside
• Never "X — like this — Y" (double em dash wrapping a clause); that pattern is almost exclusively AI
• X — item, item, item (introducing a list) → X. Item, item, item. or a colon after a complete sentence
• Three or more in one paragraph means structural problems; rewrite the paragraph

Semicolons (;). Real-world prose outside academic/legal writing almost never uses them.

• Treat every semicolon as a bug unless the register is explicitly formal/academic
• Replace with a period (usually), "and"/"but"/"so" (when the relationship matters), or restructure
• Exception: lists whose items contain commas ("San Francisco, CA; Austin, TX")

Mid-sentence colons (:). Fine at the end of a complete clause to introduce; mid-thought is an AI pattern.

• "The answer is: start earlier" → "Start earlier."
• "The problem: nobody tests this" → "Nobody tests this." or "Here's the problem: nobody tests this."
• One colon per paragraph maximum in non-list prose

Curly quotes. A near-certain single-character tell that survives rewriting.

• Find-replace before shipping: curly “ ” → straight ", curly ‘ ’ → straight ' — apostrophes included
• Exception: publishing contexts where typographic quotes are house style

Lever 9: Strip RLHF / instruction-tuning voice

Current detectors mostly fire on RLHF and instruction-tuning artifacts, not "AI-ness" per se ("Base Models Look Human"; details in references/research.md). What gets flagged is the "helpful assistant" voice. This lever is the single most valuable one. Strip:

Advanced techniques (optional, when stakes are high)

The nine levers are pure-rule; hybrid (rule + model-in-the-loop) approaches benchmark better. When stakes warrant the cost, layer these on (sources: references/research.md):

1. Detector-scored best-of-N. Generate 3–5 variants; score each against a real detector (GPTZero, Pangram, Binoculars), a banned-word count, or a perplexity probe; ship the lowest.
2. Iterative paraphrase pass. Run the output through a second LLM with "paraphrase this, keep the meaning." Diminishing returns past 2 passes; meaning drift accumulates — verify substance.
3. Writer-profile distillation. When the user supplies writing samples, distill style hypotheses first (this is protocol step 0; follow the procedure there). Beats raw few-shot.
4. Self-rewrite distance check. Ask a different LLM to "rewrite this in different words." Near-identical rewrite = text still at a local probability maximum = reads as AI.
5. Embedding-guided synonym swap. When tooling is available, prefer substitutions that explicitly lower detector scores over Lever 1's static word list.
6. Disfluency injection (casual register only). Light hesitations, mid-thought restarts, "wait actually" corrections. Off by default for formal writing; disfluencies in a board memo are their own tell.

Dead ends, don't bother: homoglyph injection (defeated by Unicode normalization, and a clear tampering signal), single cross-model rewrite (doesn't defeat trained detectors alone), watermark stripping (separate problem space).

Rewrite protocol

When given text to humanize:

0. (Optional) Writer-profile distillation. If the user provided prior writing samples, extract style hypotheses across six dimensions before touching the new text:

   1. Sentence length pattern. Variance? Signature fragments?
   2. Word choice level. Casual or academic? "stuff"/"thing" or "elements"/"components"?
   3. Paragraph openers. Straight in? Context first? A question? A scene?
   4. Punctuation habits. Em dashes? Parentheticals? Ellipses? Fragments?
   5. Recurring phrases / verbal tics. Repeated phrases? Fillers ("honestly", "basically", "look,")?
   6. Transition style. Explicit connectors, or next thought with no bridge?

   Distill 5–10 specific hypotheses ("never opens with a thesis", "fragments in conclusions", "sentence variance roughly 6–28 words").

   Critical rule when matching voice: don't just remove AI patterns — replace them with patterns from the sample. If the sample is casual, don't upgrade the vocabulary. The skill's default bias toward terse, direct prose yields to the sample's register when they conflict. Then apply the levers in service of those hypotheses.

1. Read the full input first. Identify topic domain, audience, register, length target.

2. Inventory the AI tells. Flag hedge count, list/bullet count, sentence-length uniformity, examples present/absent, transition inventory, RLHF voice markers.

   Also count specific anchors (numbers, named entities, dates, time references, concrete examples). If the count is zero AND no voice sample was provided in step 0, still humanize — use Lever 5's plausible-specificity frames, never invented facts — then append this note AFTER the humanized text, blank-line separated, as plain text (no > marker):

   [Note: the input had no factual anchors (no numbers, names, dates, or specific examples). The rewrite is cleaner but learned classifiers (GPTZero, Grammarly) may still flag it on the specificity signal alone (Signal E in ai-check). To close that gap, give me the actual specifics (product names, metrics, dates, named tools) or a sample of your writing to match.]

   Do not stop and ask before rewriting. By the time the user says "proceed anyway", this skill sits deep in the conversation history and the second-pass rewrite reliably leaks tells back in. Rewrite now, flag the gap after.

3. Rewrite in a single pass applying all nine levers. No "light editing"; the statistical fingerprint requires structural change.

   This matters most when the input is text you wrote earlier in this conversation. Rewriting your own recent output anchors you to its phrasing, and the rewrite silently degrades into word swaps that leave the original's em dashes, negation pivots, and rhythm intact. Treat your own prior output as foreign text: extract what it says, re-derive the prose from the content. If your edit log would read as a list of substitutions, you light-edited. Start over.

4. Pre-output gate. A literal scan, not a recollection: re-read the draft top to bottom and for each item write the count and quote every hit before fixing it. Write zeros explicitly ("em dashes: 0"). A gate entry without an explicit count is a gate you did not run — an unenumerated "looks clean" always passes, and this is where humanization fails silently in practice.

   • Em dashes. Scan for "—", write the count. More than (word_count / 300)? Cut or replace with periods.
   • Semicolons. Scan for ";", write the count. Replace with a period or "and"/"but"/"so" unless a comma-containing list.
   • Curly quotes/apostrophes. Scan for the curly characters “ ” ‘ ’, write the count. Replace with straight " and '.
   • Banned vocabulary. Scan against the master list (end of this skill), quote each hit. Eyeball first: delve, leverage (verb), utilize, robust, comprehensive, furthermore, moreover, "it is important to note".
   • Comparative framing. Scan for "more ... than" and "feels like ... not", quote each match. Describe the thing directly.
   • Diminishment. Scan for "not just", "not X, it's", "not X but", quote each match. State what it IS.

   After fixing hits, re-scan the sentences you rewrote: regenerated prose reintroduces the same tells at the same rate as the first draft.

5. Self-check. Step 4 cleared the mechanical tells; this covers the rest:

   • Sentence rhythm, from counts, not feel. Write out every sentence's word count in order ("9, 5, 22, 16, 7..."), then check the list against ALL four, fixing and recounting until every one passes (hard rule 7 covers the first two; conditions 1-2 apply only to outputs over ~80 words):
     1. Max minus min ≥ 20 (needs a ≤5-word fragment and a 25-plus-word sentence)
     2. Fewer than half the counts in the 10-to-20 band
     3. No three consecutive counts within 5 words of each other
     4. At least one count ≤ 6 per 150 words of output A mental read-through always sounds varied to the model that wrote it; the number list doesn't lie. Standard fixes: split one mid-length sentence into a fragment plus the remainder, and merge two mid-length neighbors into one long sentence that earns it.
   • Every paragraph has at least one specific anchor (number, name, example, time reference). On the step-2 zero-anchor path, Lever 5's plausible-specificity frames satisfy this item — never invent facts to pass it.
   • No bullet lists unless the user requested them
   • Voice consistent throughout (no third-person formal → first-person casual drift)
   • Every colon preceded by a complete sentence (exception: Slack/informal fragments)
   • Output shape (hard rule 6). No preamble, no trailing changelog. Only permitted additions: the meta-notes mandated by steps 2 and 5.6.
   • Rhetorical scaffolding: scan for the Signal I checklist patterns (step 5.5) — they survive the gate because they feel like good writing. Outputs >150 words get the full list in the audit; shorter outputs at minimum check aphorism closers, anaphora, "turns out" pivots, "What X was Y" setups, either/or binaries.

5.5. Audit pass. Run the Signal I checklist below on every output — it is the single source of truth for rhetorical-scaffolding patterns. For outputs >150 words, also run the rewrite-and-recheck loop: after the self-check passes, ask "What still makes this read as AI?", list 2–3 residual patterns, rewrite those sentences, re-run the gate and self-check. Empirically the first revision has 2–4 Signal I patterns left; one loop gets it to 0–1. Loop once only — past iteration 2 you over-edit into choppy, voiceless prose.

Flagged residuals must be removed, not justified. Not kept because "removing it would collapse the paragraph", "this register needs it", "it reads thin without it", "it would be choppy", or "it's a transition, not a closer". Those rationalizations are how Signal I patterns survive — they feel necessary because they're constructed to feel necessary. If removing a flagged sentence makes a paragraph too thin, the paragraph IS too thin: collapse or merge it. An honest 80-word output beats a padded 200-word output that reads as AI. Red flag: if your audit says "borderline but I'm keeping it because...", you just lost the loop. Cut it.

Signal I checklist (every audit, every paragraph). A general "what reads as AI?" prompt misses things; scan for each pattern and fix every hit:

• Mini-aphorism closer. Paragraph ends in a 4-to-10-word punchy "lesson" ("That's the part that stuck.", "Slide decks don't.")? Cut it, or fold it into the previous sentence.
• Thesis-first paragraph opener. Frame before experience ("The rollout was the hard part.", "The real question is Y.")? Start with the concrete thing; let the thesis emerge.
• Parallel-subject mirror. Consecutive sentences with mirrored noun-phrase openers ("The code is one thing. Maintaining it is another.")? Vary one subject.
• Aphoristic closing sentence on the whole piece. Last sentence reads as quotable standalone wisdom? End on a specific detail, an unresolved question, or a concrete next action.
• Pattern announcement. Naming a pattern before describing it ("The pattern is X.")? Just describe it.
• "Turns out" pivot. Reveal-narrative framing? State the discovery directly.
• Setup sentences. "What I didn't expect was X."? Lead with X directly.
• Within-sentence anaphoric parallel list. Four parallel question-word items ("what X, what Y, why Z, what W")? Use varied noun forms instead.
• Composed self-aware parenthetical. Meta-commentary on your own state ("which I choose to read as X")? End on the concrete behavior.
• Parallel reason chains. Three consecutive "subject + because/when + reason" sentences? Vary the clause structure: one "because", one bare assertion, one fragment.
• Anaphora. Same opening word on 2+ consecutive sentences? Collapse or vary the opener.
• Either/or binary. Clean two-option framing of a spectrum? Name the actual situation.
• Balanced parenthetical pairs. Symmetric trade-offs in one sentence ("(X, but Y) or (A, but B)")? Real trade-offs are asymmetric; break the symmetry.
• Tricolon. Three parallel beats with identical grammar, often conjunction-free and escalating in weight ("Two hours of X, six engineers doing Y, a postmortem where Z.")? Break the third item into its own sentence, join two with "and", or reduce to two.
• Chiasmus / balanced opposition. Reversed-parallel construction that sounds like insight ("being specific about being wrong / being vague about being right")? Make the comparison asymmetric.

3+ hits means the patterns compound — address all of them. Two mini-aphorisms might be tolerable; three in five paragraphs is a clear AI signature.

5.6. Output-length sanity check. If output is under 50% of input length, the input was mostly puffery that got correctly removed. Don't pad it back up — padding reintroduces the stripped patterns. Instead append, blank-line separated, as plain text (no > marker):

[Note: input was substantially puffery; humanized output is N% shorter. To make this longer without re-introducing AI patterns, add specific anchors: numbers, named entities, examples, or time references.]

This and step 2's note are the only commentary the skill ever outputs, always after the rewrite, clearly separated. The intent: make the gap visible instead of silently shipping thin output that fails on Signal E.

6. (Optional) Self-rewrite distance sanity check. When stakes are high, run Advanced 4.

7. (Optional) Detector-scored best-of-N. When stakes are high, run Advanced 1.

8. Output the rewritten text only. No preamble ("Here is the humanized version:"), no trailing changelog ("Main moves:", "What I changed:"). The only permitted additions are the meta-notes mandated by steps 2 and 5.6. This holds in chat interfaces too, where narrating edits feels helpful: it isn't the deliverable, and a changelog of word swaps is evidence you light-edited (step 3). If the user wants a side-by-side, they'll ask.

Generating human text from scratch

When writing new content (not rewriting):

• Apply all nine levers from the first sentence
• Start mid-thought when it fits: "The tricky part about X isn't what most people think."
• No "In this [article/post/section], I will..." opener
• End without a summary paragraph unless the piece is long enough to genuinely need a re-anchor
• Calibrate voice to the domain: an engineer's Slack post sounds different from a board memo

Decoding-strategy note (when controlling generation): set temperature high (0.9–1.1), top-p loose (0.95–0.99), repetition penalty up (1.1–1.2). This widens the token distribution and breaks the local-maximum property perplexity detectors rely on (RAID benchmark; see references/research.md).

Domain-specific calibration

Technical (engineering, code, systems)

• Domain-native vocabulary: "the hot path", "this falls apart at scale", "the footgun here is..."
• Short sentences for definitive claims: "This is O(n²). Don't do it at scale."
• Tradeoffs direct, not diplomatic: "The downside is real: you lose..."
• Real tool names, version numbers, error messages when available

Narrative / blog / essay

• Open with a scene or specific moment, not a thesis
• Let the argument emerge from the evidence
• Sentence fragments deliberately for rhythm. Like this.
• One moment of genuine uncertainty or changed mind per 500 words

Creative / lyrical prose (fiction, poetic passages, mood pieces)

The register where this skill gets rationalized away. Every hard rule still applies; detectors don't grade on artistic merit. The traps:

• The "literary em dash" exemption. "Something wouldn't let you stay asleep — a feeling too new to have a name yet." Feels earned; still the single strongest tell. Use a period, a comma, or restructure.
• Poetic negation pivots. "It isn't proof you failed — it's proof you showed up." Say the positive thing: "It's proof you showed up."
• Anaphora as lyricism. "Not every door has been tried. Not every version of yourself." Reads as AI cadence, not poetry. Vary the second sentence.
• Mid-sentence colon reveals. "and you think: this is where it starts" — restructure or end the sentence before the colon.
• Balanced imagery pairs and escalating tricolons. Human lyrical prose is lopsided; AI lyrical prose is symmetric. Break the symmetry.

Human creative writing gets its texture from specificity and asymmetry (a named street, a wrong note, an image that doesn't resolve), not punctuation drama.

Professional / business

• Cut the throat-clearing opener; just the message
• State the ask in sentence 1 or 2
• Numbers and deadlines: "by Thursday EOD", "the three blockers are..."
• Short paragraphs (2–3 sentences max in email/memo)

Slack / async team updates

Register collapse is the primary tell: AI Slack reads like a polished status report. Real Slack has:

• Abbreviations and approximations: ~60%, <10min, fwiw, btw, lmk, tmrw
• Fragments throughout: "hard commits: billing gRPC + pprof thing", not full clauses
• Self-corrections mid-message: "oh also —", "wait, actually"
• Thoughts bleeding together, looping back, trailing off — not topic-per-paragraph
• Numerals with approximation markers: ~3-4 days, not "approximately three to four days"
• Single-word endings: "lmk" as its own line
• Lowercase throughout except proper nouns
• The structure must actually break: accomplishment → caveat → next steps is still AI even with fwiw sprinkled in. Add a fourth element that doesn't fit, loop back, or end with an unset-up question.

What this skill does NOT do

• Guarantee 0% AI scores on commercial detectors (no method does reliably)
• Add false information to increase specificity — plausible framing only
• Change the factual content of the input, only the expression
• Apply the same transformation to every domain — register matters

Reference: banned word/phrase list (compile-time errors in AI text)

Remove every instance before outputting:

Core AI vocabulary: delve, leverage (verb), utilize, robust, comprehensive, streamline, foster, facilitate, pivotal, nuanced, multifaceted, crucial (overused), enduring, garner, valuable, vibrant, tapestry (figurative), testament (figurative), interplay, intricate, intricacies, landscape (as abstract noun), showcase (verb), highlight (as standalone verb), underscore (as standalone verb), align with, actually (as filler), additionally (as opener)

Hedge / softener clusters: it is important to note, it is worth mentioning, notably, it's worth noting, in many cases, generally speaking, it can be argued

Filler / formula openers and closers: in today's fast-paced world, in conclusion, in summary, to summarize, it goes without saying, needless to say, at the end of the day, at its core, under the hood, the standard fix, the common approach, simple enough on paper

AI transition fingerprint: furthermore, moreover, it is clear that, this highlights, this underscores, as previously mentioned, turns out (as a pivot), it turns out that

Significance inflation: stands as a testament to, marks a pivotal moment in, indelible mark, evolving landscape, setting the stage for, deeply rooted in, plays a vital role, a key turning point, represents a shift in

Promotional / marketing register: nestled in the heart of, in the heart of, breathtaking, must-visit, stunning, boasts a rich heritage, renowned for, groundbreaking (figurative), vibrant (cultural copy)

Quantifier inflation: a myriad of, a plethora of, in the realm of, the landscape of (abstract)

Persuasive authority tropes: the real question is, what really matters, fundamentally, the deeper issue, the heart of the matter, in reality

Signposting / tutorial scaffolding: let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado

Knowledge-cutoff disclaimers: as of my training cutoff, up to my last training update, while specific details are limited based on available information, based on what I know up to

Sycophantic prefixes: great question, you're absolutely right, that's an excellent point, of course!, certainly!

Templated email / Slack closers: happy to jump on a call, let me know if you have any questions, feel free to reach out, i hope this helps, looking forward to connecting soon

Binary framing: whether X or Y (as a clean binary framing opener)