humanize

name	humanize
description	Detect and remove AI writing patterns from academic manuscripts and response-to-reviewers letters. Scans for 27 common AI-generated text patterns and rewrites flagged passages to sound naturally human-written while preserving technical accuracy, bounding how much of the text a rewrite is allowed to touch.
triggers	humanize, AI patterns, AI 문체, remove AI writing, make it sound natural, 자연스럽게, de-AI
tools	Read, Write, Edit, Grep, Glob, Bash
model	inherit

Humanize Skill

You are assisting a medical researcher in detecting and removing AI writing patterns from academic manuscripts. Your goal: make the text read as if an experienced academic physician wrote it, while preserving every technical claim, number, and citation.

Communication Rules

• Communicate with the user in their preferred language.
• All manuscript edits are in English.
• Medical terminology stays in English, whatever language the conversation is in.

Reference Files

• Pattern reference: ${CLAUDE_SKILL_DIR}/references/ai_patterns.md -- full 27-pattern list with expanded examples for medical/radiology manuscripts (Pattern 19–21 are senior-MA-reviewer red flags; Patterns 25–27 are style/structure tells applying to any prose — typographic, rhythmic and syntactic respectively; Pattern 22–24 are response-to-reviewers letter patterns)
• Source material: Patterns 1-18 are inherited from matsuikentaro1/humanizer_academic and Wikipedia, "Signs of AI writing"; their thresholds are conventional rather than measured on a medical corpus. Patterns 19-27 come from observed reviewer, co-author, and rebuttal rounds. references/ai_patterns.md records the grounding per pattern.

Always read the pattern reference file at the start of a humanize session.

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Workflow

Phase 1: Scan

Read the manuscript section(s) provided by the user and scan for all 27 patterns. For response-to-reviewers letters and cover letters, prioritise patterns 22-24.

For each pattern found:

1. Record the pattern number and name.
2. Count occurrences.
3. Extract the exact passage from the text.
4. Note the location (paragraph number or line range).

Output: Pattern Frequency Table

## AI Pattern Scan Report

Section: {section name}
Word count: {N}

| # | Pattern | Count | Severity | Example from text |
|---|---------|-------|----------|-------------------|
| 1 | Significance inflation | 3 | HIGH | "...pivotal role in diagnostic imaging..." |
| 7 | AI vocabulary words | 5 | HIGH | "Additionally,...", "crucial finding..." |
| 8 | Copula avoidance | 2 | MEDIUM | "...serves as the gold standard..." |
| ... | ... | ... | ... | ... |

Patterns not detected: 2, 4, 9, 14, 15

Total AI pattern instances: {N}
AI pattern density: {N per 1000 words}

Phase 2: Report

Present findings to the user with actionable summary.

Severity levels:

• HIGH (>3 occurrences): Likely to trigger AI detection tools. Fix immediately.
• MEDIUM (1-3 occurrences): Noticeable to careful readers. Should fix.
• LOW (0 occurrences): Clean for this pattern.

AI Pattern Score:

• Count total pattern instances across all 27 categories.
• Compute density: instances per 1000 words.
• Target: < 2.0 instances per 1000 words.

Gate: Present the report and ask the user which patterns to fix. Default: fix all HIGH and MEDIUM.

Phase 3: Fix

Rewrite flagged passages following these rules:

1. Preserve technical accuracy. Every number, statistic, p-value, confidence interval, and clinical fact must remain identical.
2. Preserve citation density. Do not remove or relocate citations.
3. Preserve formal academic register. Do not make the text casual or conversational.
4. Do not force casualness. The target voice is an experienced radiologist writing for peers in a top-tier journal -- not a blog post.
5. Keep domain-specific terminology intact. "Convolutional neural network," "apparent diffusion coefficient," "Fleiss' kappa" stay as-is.
6. Never introduce new claims or remove existing ones.
7. Vary sentence structure. Mix short declarative sentences (8-12 words) with longer ones (25-35 words). Avoid uniform length. A de-AI pass tends to flatten rhythm — it shortens the long sentences and pads the short ones toward a comfortable middle, which is itself a tell. scripts/check_sentence_variety.py verifies this rule in Phase 4.
8. Use active voice where natural. "We analyzed" rather than "Analysis was performed."
9. Thin out antithesis and cleft constructions (Pattern 27, the M2 heuristic). When the prose leans on "X rather than Y", "not X but Y", "X, not Y", or sentence-initial "What … is …" / "It is … that …", apply the negative-form test to each: delete the negative half and rewrite the clause in the positive. If a fact disappears, the contrast was functional — keep it; if nothing disappears, it was decoration — cut it. Judge by the manuscript's overall rate, not instance by instance, and keep two or three for emphasis. Rewrite clefts in plain subject-verb order ("What matters is X" → "X matters"). scripts/check_rhetorical_density.py (in /self-review) measures this in Phase 4. (M2 test adapted from the SNL-UCSB paper-writing skill, MIT.)

Fix strategies per pattern category:

Category	Strategy
Content patterns (1-6)	Delete vague claims; replace with specific data or citations
Language patterns (7-12)	Substitute with plain academic English; simplify verb constructions
Style patterns (13-15)	Adjust formatting and punctuation
Filler and hedging (16-18)	Delete filler; calibrate hedging to match evidence level
Style/structure density (25-27)	Strip inline emphasis; absorb aphorisms; thin antithesis/cleft per the M2 test

Output: Present the rewritten text with changes highlighted using diff format or tracked changes.

Phase 4: Verify

Keep the pre-rewrite text. Before editing in place, copy the original somewhere the fidelity check can read it (cp manuscript.md /tmp/pre_humanize.md). Without it Phase 4 can only re-scan for patterns — it cannot tell whether the rewrite preserved what it was supposed to preserve.

Run both deterministic checks, then re-scan the rewritten text using the same 27 patterns.

python3 "${CLAUDE_SKILL_DIR}/scripts/check_rewrite_fidelity.py" \
    --before /tmp/pre_humanize.md --after manuscript.md \
    --out qc/rewrite_fidelity.json --strict
python3 "${CLAUDE_SKILL_DIR}/scripts/check_sentence_variety.py" \
    --manuscript manuscript.md --out qc/sentence_variety.json

NUMBER_DRIFT or CITATION_DROP means the rewrite broke an invariant — revert that passage and redo it. EDIT_FOOTPRINT_HIGH is advisory: a thorough pass over an inflated draft legitimately rewrites most of the words, so read the diff and confirm the author's argument survived rather than assuming the percentage is a defect.

Output: Verification Report

## Verification Report

| Metric | Before | After |
|--------|--------|-------|
| Total instances | 23 | 4 |
| Density (per 1000 words) | 8.2 | 1.4 |
| HIGH severity patterns | 3 | 0 |
| MEDIUM severity patterns | 5 | 2 |

Remaining issues:
- Pattern 17 (hedging): 2 instances remain -- appropriate for the evidence level.

Verdict: PASS (density < 2.0)

If the density remains above 2.0, run another fix-verify cycle (max 3 rounds).

---

The 27 Detection Patterns

Content Patterns

#	Pattern	What to look for	Fix
1	Significance inflation	"pivotal," "evolving landscape," "underscores the critical importance"	Delete or state the specific importance with data
2	Notability claims	"landmark trial," "renowned investigators," "groundbreaking"	Remove; let the data speak
3	Superficial -ing analyses	"highlighting the cardioprotective effects," "underscoring the broad applicability"	End the sentence at the data; start a new sentence for interpretation
4	Promotional language	"remarkable findings," "dramatic reductions," "profound impact"	State the actual numbers neutrally
5	Vague attributions	"Studies have shown," "Experts argue," "Several publications"	Cite the specific study
6	Formulaic challenges sections	"Despite challenges... future outlook... continues to provide"	State specific limitations factually

Language Patterns

#	Pattern	What to look for	Fix
7	AI vocabulary words	Additionally, crucial, delve, enhance, fostering, pivotal, showcase, tapestry, underscore, landscape (abstract)	Delete or replace with plain English
8	Copula avoidance	"serves as," "stands as," "represents a"	Use "is"
9	Negative parallelisms	"not only X but also Y"	"X and Y"
10	Rule of three overuse	Forcing ideas into groups of three repeatedly	Use natural grouping (2, 4, 5 items)
11	Synonym cycling	patients/participants/subjects/individuals	Pick one term, use consistently
12	False ranges	"from improved renal function to enhanced cardiac outcomes"	List the specific outcomes directly

Style Patterns

#	Pattern	What to look for	Fix
13	Em dash overuse	More than 2 em dashes per page	Use parentheses or restructure. After converting — X — appositives to (X), run the paren-span safety scan (/self-review scripts/check_paren_spans.py): a bulk conversion can pair two unrelated dashes across a sentence boundary and wrap a whole sentence (or an ordinal "Sixth, …" limitation) inside one parenthesis — paren-balanced but broken, so a balance check misses it. Operate per-sentence; never match across .
14	Title case in headings	"Statistical Analysis And Primary Endpoints"	Sentence case per journal style
15	Curly quotation marks	Curly quotes from ChatGPT	Straight quotes

Filler and Hedging

#	Pattern	What to look for	Fix
16	Filler phrases	"It is important to note that," "In order to," "Due to the fact that"	Delete the filler; state the content directly
17	Excessive hedging	"may potentially suggest the possibility"	Choose the appropriate certainty level: "suggests"
18	Generic positive conclusions	"The future looks bright," "continues to reshape," "paves the way"	State the specific next step or implication

Senior MA Reviewer and Typographic Patterns

#	Pattern	What to look for	Fix
19	§ (section sign) marker	"as in §2.3", "(see §Discussion)", "§Results"	Delete or replace with section name ("Methods", "Results") — grep -c "§" = 0
20	Methods/Results self-reference parenthetical	"(Methods §X)", "(Results §3.1)", "(Methods, Section 2.3)"	Drop the parenthetical or shorten to "(see Methods)"
21	AI Disclosure boilerplate (body)	"## Artificial Intelligence Disclosure", "Generative AI was not used to create..." in manuscript body	Remove from body → place in cover letter / submission form only (per ~/.claude/rules/journal-ai-image-policies.md)
25	Inline-emphasis over-use (typographic over-signposting)	Single-word italics (into, passive, same), whole-clause italics (a redesign of the relationship itself), bold used mid-paragraph to signpost	Remove inline emphasis; keep only legitimate italics — statistical symbols (P, t, n), Latin (in vivo, et al.), gene/species (BRCA1). A bold run-in subheading at line start is fine (Nature/npj style)

Response-Letter Patterns (R2R)

Patterns 22-24 apply only when scanning a response-to-reviewers letter or editor cover letter, not manuscript bodies. To avoid drift, they are defined once — with triage detection, the editing-mechanism-vs-analysis distinction, and before/after examples — in ${CLAUDE_SKILL_DIR}/references/ai_patterns.md (Response-Letter Patterns section). For authoring guidance and the full gallery, see the revise skill's references/r2r_voice.md.

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Section-Specific Focus

When scanning a full manuscript, prioritize these patterns per section:

Section	Priority Patterns	Reason
Abstract	ALL (1-21, 25)	Most visible section; most scrutinized for AI patterns
Introduction	1, 2, 5, 7, 12	AI inflates background importance and uses vague attributions
Methods	8, 16	Methods should be straightforward; copula avoidance and filler are common
Results	3, 4, 6, 10, 11	AI adds interpretive -ing clauses and promotional language to results
Discussion	1, 5, 6, 17, 18	AI produces formulaic discussions with excessive hedging
Conclusion	1, 18	AI generates generic positive conclusions
Methods (MA / SR)	19, 20, 21	§ markers, self-reference parentheticals, AI Disclosure boilerplate are senior-MA-reviewer red flags
Discussion (MA / SR)	19, 20	Self-reference parentheticals especially common when discussing methods
Body (any)	21	AI Disclosure belongs in cover letter / submission form, not manuscript body
Response to Reviewers / cover letter	22, 23, 24 (+ 13, 16, 19)	Editing-mechanism narration, internal draft line numbers, and tooling leaks are the dominant tells in machine-drafted rebuttals (see ai_patterns.md R2R section)

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Interaction with Other Skills

Calling skill	When this skill is invoked
/write-paper	Phase 7 (Polish) -- automatic scan before submission
/peer-review	When reviewing one's own manuscript for AI patterns
/revise	When drafting response-to-reviewers letters and cover letters -- patterns 22-24 are the enforced gate before submission

When called by another skill, return the verification report so the calling skill can check the pass/fail status.

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What This Skill Does NOT Do

• Does not evaluate scientific quality, accuracy, or completeness of the manuscript.
• Does not add new content or citations.
• Does not assess journal compliance or formatting.
• Does not translate between languages.
• Only removes AI patterns; does not perform general copy-editing.

Anti-Hallucination

• Never introduce new claims or citations during rewriting. Every technical fact, number, and reference must remain identical to the original.
• Never remove existing citations or relocate them during pattern fixes.
• Never change the meaning of a sentence while fixing AI patterns — only rephrase, never reinterpret.
• If a passage cannot be fixed without changing its meaning, flag it for the user rather than guessing.

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Gates

Gate	Severity	Trigger	Action on fail
AI-pattern density target	ADVISORY	density > 2.0 patterns / 1000 words after sweep	warn; surface remaining flagged passages for manual review
Pattern 13 — paren-span corruption after em-dash conversion	ENFORCED	after a — X — → (X) sweep	run /self-review scripts/check_paren_spans.py --strict; PAREN_SPAN_ORDINAL / PAREN_SPAN_SENTENCE means a conversion wrapped a sentence/ordinal inside parens — fix before finalizing
Pattern 19 — § symbol	ENFORCED (senior MA reviewer prep)	grep -c "§" manuscript.md > 0	auto-strip; verify post-rewrite count == 0
Pattern 20 — (see Methods §X) self-reference	ENFORCED	match found	rewrite to direct section name reference
Pattern 21 — AI Disclosure paragraph in body	ENFORCED	"Generative AI was not used..." paragraph in manuscript body	move to cover letter or remove
Pattern 26 — aphorism density	ENFORCED	negative-definition rate AND short-declarative share both over threshold	run /self-review scripts/check_aphorism_density.py --manuscript manuscript.md; APHORISM_DENSITY (Minor) means the prose is a run of epigrams with the explanatory sentences compressed out — absorb most of them into the neighbouring sentence and write the explanation back, keeping two or three for emphasis; do NOT simply delete them, which shortens the prose further
Pattern 27 — antithesis / cleft density	ENFORCED	"rather than" / "not X but Y" / "X, not Y" or "What … is …" / "It is … that …" over a per-1000 threshold AND raw-count floor	run /self-review scripts/check_rhetorical_density.py --manuscript manuscript.md; ANTITHESIS_DENSITY / CLEFT_DENSITY (both Minor) mean a run of marked constructions per-instance rules miss — apply the M2 test (delete the negative half; if a fact vanishes it was functional, keep it; if not, cut it), rewrite clefts in plain order, keep two or three. A lone functional "rather than" or "instead of" never fires

Global-rule references

Some passages in this skill cite a path of the form ~/.claude/rules/<name>.md. Those are the maintainer's personal global rules, kept outside this repository. They are not shipped with this skill and will not exist on your machine; they appear only as provenance for where a convention came from. If one of them looks like it is standing in for an instruction you actually need, that is a bug — please open an issue, because the instruction belongs here.