moai-domain-humanize

name	moai-domain-humanize
description	AI text humanization and 윤문 (post-editing) specialist that detects and removes AI tells while preserving meaning, facts, and figures. Covers Korean, English, Japanese, and Chinese with a shared severity model (S1/S2/S3), quality grades (A/B/C/D), and 30%/50% over-editing guardrails. Use to make AI-generated text read as human-authored without changing what it says (de-ai, naturalness pass).
when_to_use	Use for AI-text humanization and post-editing (윤문): detecting and removing AI tells across Korean, English, Japanese, and Chinese, applying the S1/S2/S3 severity model and quality grades while preserving meaning, facts, and figures.
license	Apache-2.0
compatibility	Designed for Claude Code
allowed-tools	Read, Write, Edit, Grep, Glob
user-invocable	false
metadata	{"version":"1.0.0","category":"domain","status":"active","updated":"2026-06-15","tags":"humanize, ai-tell, 윤문, post-edit, naturalness, multilingual"}
progressive_disclosure	{"enabled":true,"level1_tokens":100,"level2_tokens":5000}

moai-domain-humanize

Post-editing specialist that removes "AI tells" from generated text and rewrites it to read as human-authored, while preserving meaning. This is the editing counterpart to text generation: it does not write new content, it refines how existing content is said. Covers Korean, English, Japanese, and Chinese.

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Quick Reference

Operating Principles (4)

1. Meaning preservation is the top rule. Facts, numbers, statistics, named entities, quotations, citations, and the author's stance/certainty stay intact. Any meaning drift forces a rollback.
2. Evidence-based edits only. Every change must trace to a detected tell on a specific span. Stylistic "improvements" unconnected to a catalogued tell are themselves an over-editing signal and are forbidden.
3. Genre and register preservation. Humanize within the source register — academic stays academic, casual stays casual. Never push formal text into slang or vice versa.
4. Over-editing prevention. Flag at >30% change (WARN), halt at >50% change (forced stop / human review). Above 50% you are regenerating, not humanizing.

Mode Selection

• Fast mode (default, up to ~5,000 chars): a single pass — detect, rewrite, self-verify against the meaning-preservation checklist.
• Strict mode (long or high-stakes text, or when requested): separate stages — detect → surgical rewrite → content-fidelity audit (facts/figures/stance unchanged) → naturalness review. Re-run a second pass when the result lands at Grade C.

Output Contract

Return two things:

1. The humanized text.
2. A short change report: categories hit (with counts), the final quality grade (A/B/C/D), and the percent changed (character-level edit distance ÷ source length). When a guardrail fires, state it explicitly (WARN at >30%, HALT at >50%).

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Common Severity Model (shared by all 4 languages)

Each tell carries one severity tier. Detectors gate by occurrence count and overlap, because a single tell rarely proves AI authorship — confidence comes from clustering.

Tier	Name	Rule
S1	Decisive	A single occurrence strongly confirms AI authorship → remove on first occurrence.
S2	Strong	Acceptable at 1–2 instances → remove at 3 or more.
S3	Weak	Problematic only when overlapping other tells → downgrade-only contributor.

Common Quality Grades (shared by all 4 languages)

Graded after the rewrite, on the residual S1/S2 counts plus improvement % (= proportion of detected tells removed without introducing new ones).

Grade	Criteria	Action
A	0 residual S1, ≤2 residual S2, ≥70% improvement	Pass — reads as human-authored
B	0 residual S1, ≤4 residual S2, ≥50% improvement	Pass — minor polish remains
C	1–2 residual S1, OR <50% improvement, OR over-edit WARN (>30%)	Trigger a second pass
D	≥3 residual S1, OR over-edit HALT (>50%), OR meaning drift detected	Request human review; do not auto-ship

Hard rule: any residual S1 caps the grade at C; any meaning-distortion flag forces D. S3 tells affect the grade only when ≥3 of them overlap and reinforce an S1/S2 finding.

Over-Editing Guardrails (shared)

Change rate = character-level edit distance ÷ source length; target band ~5–30%.

• >30% changed → WARN. Surface a caution and cap at Grade C until each edit is justified by a detected tell. Note: padding-removal legitimately shrinks text, so a length drop alone is not a violation — flag when meaning-bearing spans are altered.
• >50% changed → HALT. Stop and require human confirmation; revert to the last safe state.

Meaning-Preservation Checklist (shared, all must hold)

1. Anchor facts first — fix the claims, numbers, names, dates, and certainty level before editing.
2. Edit at sentence/phrase level, not whole-document regeneration.
3. Add no new facts — never invent specifics to replace vagueness; simplify instead, or flag for the author.
4. Drop no load-bearing facts — removing an inflated wrapper must keep the substantive claim inside.
5. Preserve genuine certainty/hedging and technical terminology verbatim.
6. Final diff check — compare facts, tone, certainty, and examples against the original; revert any edit that drifts.

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Language Routing

Each target language has its own tell catalogue (categories, before/after examples in the target language, per-category severity). Load the module that matches the text being edited:

Language	Module	Source basis
Korean (한국어)	modules/korean.md	Faithful port of the im-not-ai (Humanize KR) taxonomy (10 categories A–J, 100+ subcategories)
English	modules/english.md	Web-researched catalogue (10 categories EN-A … EN-J)
Japanese (日本語)	modules/japanese.md	Web-researched catalogue (9 categories JA-01 … JA-09)
Chinese (中文)	modules/chinese.md	Web-researched catalogue (11 categories CN-A … CN-K)

The Korean module is a faithful port of the open-source im-not-ai taxonomy; the English, Japanese, and Chinese modules are independently web-researched catalogues modeled on the same architecture. The common severity model and quality grades above apply uniformly to every module — the modules add only the language-specific tell categories, severities, and example rewrites.

For mixed-language text, detect the dominant language and route to its module; apply each module independently to its spans when the text is genuinely multilingual.

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Implementation Guide

Workflow (per text)

1. Identify language and mode. Pick the module by dominant language; pick Fast vs Strict by length / stakes.
2. Anchor facts. Record the numbers, names, dates, quotations, and stance that must not change (meaning-preservation checklist item 1).
3. Detect tells. Scan against the module's catalogue. Record each hit with its category ID, span, and severity. Count occurrences (S2/S3 gate on repetition).
4. Rewrite surgically. Edit only flagged spans. Replace each tell with a natural rendering in the same register. Do not touch unflagged text.
5. Measure change rate. Compute character-level edit distance ÷ source length. Apply guardrails (WARN >30%, HALT >50%).
6. Self-verify (Fast) or audit + review (Strict). Re-run the meaning-preservation checklist. In Strict mode, run the content-fidelity audit and naturalness review as separate stages.
7. Grade. Count residual S1/S2 and improvement %; assign A/B/C/D. Second pass on C; human review on D.
8. Emit the humanized text + change report.

Detection note (shared across languages)

Automated AI-text detectors are unreliable across these four languages (notably weak on CJK polite registers, where they false-positive on correct formal writing). This skill is a pattern-based editing tool, not a detection oracle: rely on the catalogued tell categories and the clustering-based severity gates, not on a detector's verdict.

Common pitfalls

• Re-injecting AI-ness. Rewriting AI text with a fresh full regeneration tends to add new tells. Favor surgical edits to flagged spans over wholesale rewriting.
• Fabricating specifics. When a module calls for concrete detail to replace vague filler and no real specifics exist, simplify or flag for the author — never invent.
• Style drift. "Cleaning up" beyond the flagged tells violates principle 2 and inflates the change rate toward the HALT threshold.
• Mixing registers mid-document. Keep one consistent register (and, for Japanese/Chinese, one consistent politeness/sentence-ending style) across the whole output.

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Works Well With

• sync-auditor: independent skeptical review. Use it to score whether the humanized output preserved meaning against the original and met the target grade.

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Korean patterns adapted from the im-not-ai (Humanize KR) open-source skill — see NOTICE.md.

Version: 1.0.0