Awesome Humanize En

Humanize English text

A skill for editing English text that carries traces of AI generation. The goal is to make the text read naturally without distorting its meaning. It draws on the Wikipedia AI Cleanup project and its "Signs of AI writing" guidance.

When to use

• English text reads as mechanical, flat, or templated.
• You need to check text generated by another model.
• The user asks to "humanize", "rewrite", or "remove the AI traces".
• Text is being prepared for publication (article, post, email, document).
• The text contains unambiguous copy-paste chatbot markers: :contentReference[oaicite:N], ?utm_source=chatgpt.com, grok_card://, and similar.

When not to use

• Text that is not in English. Decline and ask for English.
• Source code, configuration files, technical logs. This skill is for connected prose only.
• Legal documents, statutes, contracts — there officialese is mandatory by genre.
• Literary prose, poetry, literary essays — there the em-dash, the rule of three, and complex syntax may be an authorial device, not a machine tell. See references/false-positives.md.

Decision tree

Received text
  ↓
Is it English? — no → decline
  ↓ yes
Genre? — code / config → decline
       — contract / statute → apply only #16-21 (style/markup); do NOT touch #8 officialese
       — fiction / poetry → do NOT apply #13 rule of three, #16 em-dash; see false-positives.md
       — academic / scientific → do NOT count passive voice, hedges, logical connectives; see false-positives.md §11
       — opinion / column / essay → rule of three and parallelism may be craft; count #13 only alongside other tells
       — marketing / blog → full set
  ↓
Run the regexes from chatbot-artifacts.md
  ↓
Any unambiguous marker found? — yes → delete it, check the rest of the text; almost certainly AI
  ↓ no
Count the soft tells by category (content, language, structural, communicative)
  ↓
0–2 tells → text is probably human, do not edit
3–5 tells → selectively fix the critical ones (🔴), leave the rest
6+ tells → rewrite wholesale while preserving the facts
  ↓
If there are source citations → run source-fabrication.md
  ↓
Final pass against the checklist (see below)

Marker severity scale

• 🔴 Instant marker — gives away AI almost certainly, must be removed.
• 🟡 Strong signal — unnatural for a human, common in AI output.
• 🟢 Weak signal — a statistical tell that also occurs in human writing; works only in combination.

Vocabulary tiers — density gating for word-level tells

Vocabulary tells (pattern #10) are gated by density, not flagged one-by-one:

• Tier 1 — flag on sight: delve, tapestry, seamless, robust, testament, boasts, "leverage" as a verb.
• Tier 2 — flag only when 2+ co-occur in one paragraph: harness, foster, elevate, streamline, crucial, pivotal.
• Tier 3 — flag only at high density (≈3%+ of running words): significant, innovative, effective, comprehensive.

Each entry covers its morphological variants (-ly, -ing, plural, comparative, conjugations) unless a variant has a distinct honest sense ("load-bearing wall" is a literal noun, not the metaphor). A single Tier-2/3 word in otherwise living text is not a tell.

Masked contrast patterns

The "it's not X, it's Y" tell (pattern #12 family) hides in split and trailing forms that plain regexes miss:

• Split sentences: "X is a symptom. Y is the cause." / "The headline isn't the speed. The real story is Y."
• Answer-reveal form: "The answer isn't X. It's Y." / "It feels like X. It's actually Y." / "stops being X and starts being Y."
• Reverse/appended order in one sentence: "It's Y, and not X." / "good for X, not for Y."
• Trailing negation fragments: "…, no guessing.", "…, no fluff."
• Multi-negation countdowns: "No X. No Y. Just Z."

Count these as #12 variants. Repair: state the positive directly; if the distinction genuinely matters, name both sides as parallel positive clauses. False-positive carve-out: necessary/sufficient-condition statements in logic, math, and formal proofs ("X holds if and only if not Y") are exempt. Also check rhythm: five consecutive sentences of similar length is a structural tell (#15f family).

Additional communicative tells

• Fake-candor openers — "Honestly?", "Let's be honest", "Here's the thing:", "The uncomfortable truth is" as a theatrical pause-and-reveal. Flag at document level only when 2+ occur; a mid-sentence "honestly" is normal speech.
• False agency / narrator-from-a-distance — "the data tells us", "the decision emerges", "nobody designed this". Ordinary metonymy ("the paper argues") is fine.
• Content-free verdict sentences — freestanding evaluations that could close any text: "This is a noteworthy finding.", "The implications are significant."
• Asserted causation without evidence (post-hoc) — "launched in Q3, so adoption increased." Repair by adding the proof or downgrading to correlation; never patch it with a hedge.
• Summary-stamp openers (as a move, not a fixed phrase) — any label announcing a summary before delivering it: "In conclusion", "Here's the TL;DR:", "In short:", "一句话总结:". Ban the move, which catches novel variants a phrase-list misses.
• Redundant plain-language restatement — explaining a point, then re-explaining it "simply": "In other words…", "Put simply…", "简单来说…" blocks that add no new information.
• Conditional next-step menu — staged offers where the user must say a magic phrase to unlock the next action: "If you want, I can also…", "If you tell me X, I'll Y." Distinct from leftover chat turns (#22).
• Emphasis crutches — "Full stop.", "Let that sink in.", "Read that again."
• Circular/tautological definitions ("the system enables users to use the functionality") and noun stacking ("production-ready deployment system infrastructure") — 🟢 weak tells.
• Diff-anchored prose — text narrating its last revision ("has been updated to", "now uses", "previously") instead of the current state; fine in changelogs and migration guides.
• Reasoning-chain leakage — "Let me think", "Step 1:", "Breaking this down" in connected prose (extends #22); Cyrillic/Greek letter homoglyphs inside Latin words (extends the A.10 marker class).

Edit order: rhythm before vocabulary

Restructure sentence rhythm first, then fix word choice — rhythm carries most of the achievable improvement, and deleting an intensifier without restructuring makes the shortened sentence fit AI cadence even better.

• Rhythm targets per suspicious paragraph: at least one short (5–8 words) and one long (20+ words) sentence. Machine-uniform spread (coefficient of variation of sentence lengths below ~0.30) reads as AI; repair toward ≥0.35 — and re-count after rewriting, surface word swaps don't fix rhythm.
• Removing transition crutches must not produce choppy asyndeton — a run of short, connector-less sentences is itself a tell of automated cleanup. Repair menu: substitute a natural connective, echo a key noun from the previous sentence, or merge the sentences. Decision test per connective: does it inflate meaning (delete) or make logic explicit (keep)?
• Hedge calibration is bidirectional: stacked hedges collapse to one, but an over-assertive causal claim built on observational evidence gets a cushion added.

Anchor verification (meaning preservation)

For standard/deep/voice-match edits on texts longer than a couple of sentences:

1. Before editing, extract up to 3 semantic anchors per paragraph — Claim, Polarity, Causation, Quantifier, Negation. Internal working notes; never shown to the user.
2. After editing, verify each anchor. Soft failures — specific→vague ("revenue up 30%" → "up significantly"), precision loss ("p residue, "Source+digit" run-ons, file_search markers turn0file2, Gemini citation tags [cite_start] / [cite: N]`, zero-width characters and Unicode watermarks, plus the old generation | When copy-paste from a chat is suspected |

| references/source-fabrication.md | Citation checks: 404, DOI resolves to a different article, non-existent ISBN, author died before publication, book citation with no page, stale access date | Always when source citations are present | | references/false-positives.md | What is NOT an AI tell: em-dash in fiction, curly quotes from macOS autocorrect, rule of three in rhetoric and journalism, officialese in legal text, academic and scientific register, ineffective indicators, human syntax, different error types in humans vs models, Title Case in headings | Before ruling on machine origin | | references/llm-fingerprints.md | Model fingerprints by vendor: OpenAI GPT-5.5, Anthropic Claude Fable 5 / Sonnet 5 / Opus 4.8, Google Gemini 3.5 (+ Deep Research), xAI Grok 4.3, DeepSeek V4, Qwen 3.7, Meta Muse Spark, Mistral Large 3 / Magistral, Perplexity, Amazon Nova, Cohere Command A+ | When working with fresh 2025–2026 text | | references/test-fixtures.md | Reference "sample / expectation" pairs for every regex + full before/after edits | When updating the skill, for regression protection | | scripts/check_markers.py | Automated run of every regex across three sample levels; runs in CI and before release. The --scan mode checks arbitrary text for markers | When updating markers: python3 scripts/check_markers.py; to scan text: python3 scripts/check_markers.py --scan file.md |

The main rule

No single soft tell is sufficient grounds for the verdict "this text was written by AI". Only these are sufficient:

• One unambiguous marker from references/chatbot-artifacts.md.
• A confirmed source fabrication from references/source-fabrication.md.
• A combination of three or more soft tells from different categories.

Better to miss machine text than to ruin a person's living text.

Five key editing principles

1. Cut the filler. Remove empty opening phrases and crutch words.
2. Break the templates. Avoid paired comparisons, dramatic lists, rhetorical wind-ups.
3. Vary the rhythm. Alternate sentence length. Two items beat three. Vary how paragraphs end.
4. Trust the reader. State facts plainly. Skip the over-explaining and the justifications.
5. No slogans. If a phrase sounds like a marketing tagline — rewrite it.

Signs of lifeless text

• Sentences of the same length and structure.
• No point of view, only a neutral report.
• No acknowledgment of uncertainty or mixed feelings.
• No first person where it would be natural.
• No humor, irony, or edge.
• The text reads like a press release.

Output format

Return only the finished rewritten text (unless the user explicitly asks for an explanation). No opening "Here is your text:" and no closing "Hope this helps!". If you are unsure about an edit — ask; do not edit silently.

Pre-submit checklist

• ✓ Ran the regexes from chatbot-artifacts.md — no unambiguous markers?
• ✓ If there are citations — were they all checked via source-fabrication.md?
• ✓ Genre accounted for (fiction / contract / opinion)? See false-positives.md.
• ✓ Removed opening filler like "certainly", "it's important to note"?
• ✓ Replaced bulky "serves as / functions as / represents" with "is" or a plain verb?
• ✓ Checked the rule of three — changed forced triples to twos or fours where it is not rhetoric?
• ✓ Removed excess epithets and averaging (pattern #1)?
• ✓ Does the text end on a concrete fact rather than a vague moral?
• ✓ No unnatural false ranges "from X to Y"?
• ✓ Curly quotes handled sensibly (kept if it is just macOS autocorrect in a personal text; flagged only as a weak tell)?
• ✓ Removed excess bold, emoji, and gratuitous tables?
• ✓ Heading hierarchy consistent (H1 → H2 → H3)?
• ✓ Removed leftover chat turns ("Certainly!", "Hope this helps")?
• ✓ Removed meaningless participial tails ("underscoring…", "highlighting…")?
• ✓ After the edit, does the text sound like something a real person would say?

Quality scoring (0–10 per criterion)

| Criterion | What it checks | |---|---| | Directness | Does it say things plainly or circle around them? | | Rhythm | Is there variation between short and long sentences? | | Trust | Is it overloaded with explanations of the obvious? | | Naturalness | Does it read like a real person, free of clichés? | | Concision | Are excess words, markup artifacts, and jargon removed? |

A total of 45–50 — AI traces removed. 35–44 — acceptable, room to improve. Below 35 — rework.

On the symmetry of this documentation

This file and references/* are built to one template: each pattern follows "Problem → Marker → What to do → False-positive boundary → Before/After". The symmetry here is the navigational convenience of a reference manual, not a generation signal. Do not confuse it with pattern #13 "symmetric sections" from language-patterns.md: there, symmetry inside authored content is treated as an AI tell.

The core idea

An LLM uses statistical algorithms to predict the next word. The result gravitates toward the statistically most probable option, applicable to the widest possible range of cases. A living human is asymmetry and imperfection. To humanize text is to return that imperfection to it.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: khasky
• Source: khasky/awesome-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.