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, across two genre surfaces: prose (columns, reports, blog posts, formal documents) and marketing copy (headlines, CTAs, landing pages, brand storytelling, slide titles). Each language module carries a prose catalogue and a copy-layer catalogue; the shared machinery below (severity model, dual grading, mode-specific guardrails) applies uniformly.

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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. In copy mode, "meaning" is defined by the fact anchors plus the core promise/benefit — see the copy-mode guard below.
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. Copy and slide genres apply their own structural rules (noun-phrase title boundaries, appeal-vs-informational voice) defined in each module's copy layer.
4. Over-editing prevention. In prose mode, flag at >30% change (WARN) and halt at >50% change (forced stop / human review) — above 50% you are regenerating, not humanizing. In copy mode, the change-rate guard is REPLACED by the fact-anchor preservation guard (see Over-Editing Guardrails below).

Genre Mode Selection (Prose vs Copy)

Two operating genres select which guardrail and grading table apply. Default from the text's genre; an explicit user instruction overrides.

Mode	Genres	Over-editing guard	Grading table
Prose mode (default)	column, report, blog, formal/official document	Change-rate guard (WARN >30%, HALT >50%)	Prose-mode grades
Copy mode	marketing copy, headline, CTA, landing page, brand story, slides	Fact-anchor preservation guard	Copy-mode grades

Processing Mode Selection (Fast / Strict)

• 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 — in prose mode — the estimated percent changed. When a guardrail fires, state it explicitly (prose mode: WARN at >30%, HALT at >50%; copy mode: any fact-anchor loss).

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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 — dual tables)

Graded after the rewrite. The genre mode selects the table: prose mode grades on residual tells plus change rate; copy mode grades on residual S1 plus fact-anchor integrity, with NO change-rate band.

Prose-Mode Grade Table

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

Copy-Mode Grade Table

Residual S1 (including the module's copy-layer S1 tells), fact-anchor integrity, and self-verification — no change-rate band, because a legitimate headline rewrite routinely changes most of its characters while preserving every anchor.

Grade	Criteria	Action
A	0 residual S1, 0 fact-anchor loss, self-verification passed	Pass — ships as human copy
B	0 residual S1, ≤1 conservative fact-anchor concern	Pass with an explicit note
C	1 residual S1, OR self-verification partially failed	Trigger a second pass
D	2+ residual S1, OR 2+ fact-anchor losses	Request human review; do not auto-ship

Hard rule (both modes): 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)

Prose mode — change-rate guard. Change rate = the proportion of the text altered; 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.
• Conservative judgment near the thresholds. This skill carries no quantitative measurement layer, so the change rate is an LLM estimate, not a reproducible metric. Treat a borderline estimate as OVER the threshold: near ~30%, issue the WARN; near ~50%, HALT. Bias toward caution so an over-edit never slips through on an optimistic estimate. (Known limitation: without a computed metric, before/after improvement percentages are estimates as well — report them as such.)

Copy mode — fact-anchor preservation guard (REPLACES the change-rate guard). In copy mode, meaning invariance is anchored differently: numbers, dates, prices, proper nouns, and legal notation are preserved 100% character-intact, AND the core promise/benefit of the copy keeps its meaning — while expression and sentence structure MAY be rewritten freely. The change-rate guard does not apply, because copy humanization legitimately rewrites most of a headline; the guard that replaces it is absolute on anchors:

• Any altered number, date, price, proper noun, or legal notation → rollback of that edit.
• Core promise/benefit drift → rollback. The rewritten copy must promise the same thing to the same audience.
• No invented specifics. Replacing vague copy with concrete claims is only allowed when the concrete facts exist in the source or are supplied by the author.

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	Original catalogue — prose (10 categories A–J) + copy layer (A-20…A-25, L-1…L-8, M-1…M-3)
English	modules/english.md	Web-researched catalogue — prose (EN-A…EN-J) + copy layer (ENC-1…ENC-9)
Japanese (日本語)	modules/japanese.md	Web-researched catalogue — prose (JA-01…JA-09) + copy layer (JA-10…JA-14)
Chinese (中文)	modules/chinese.md	Web-researched catalogue — prose (CN-A…CN-K) + copy layer (CN-L…CN-Q)

The Korean module is an original catalogue; the English, Japanese, and Chinese modules are independently web-researched catalogues built on the same architecture. Each module's copy layer is language-native — copy tells do NOT transfer mechanically between languages (English headlines are natively terse; Japanese 体言止め is prestigious craft gated by frequency, not presence; Chinese 对偶/排比 is judged content-first, not by count) — so never apply one language's copy rules to another. 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.

Genre-Module Routing

Two genre modules stack ON TOP of the language routing above — they never replace the language module:

Surface / invocation	Additional module
Display-surface copy — landing page, slide/card deck, design-tool result copy	modules/design-copy.md (genre structure rules + per-language native measures), loaded in addition to the matching language module
Post-generation QA-gate review — copy produced by another tool or workflow, reviewed before application	modules/copy-review.md (review-only mode: detect and propose, never auto-apply; six-stage pipeline + per-language formula dictionaries)

When both conditions hold (a QA-gate review of display-surface copy), load both genre modules alongside the language module. The Language Routing table above remains the language axis and is unchanged by this extension.

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Implementation Guide

Workflow (per text)

1. Identify language, genre mode, and processing mode. Pick the module by dominant language; pick prose vs copy mode by genre (see Genre Mode Selection); 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). In copy mode these anchors are the guard itself.
3. Detect tells. Scan against the module's catalogue — the prose categories in prose mode, plus the module's Copy Layer categories in copy mode. 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. Apply the mode's guardrail. Prose mode: estimate the change rate and apply the change-rate guard (WARN >30%, HALT >50%, conservative near the thresholds). Copy mode: verify every fact anchor and the core promise/benefit instead.
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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Category-catalogue structure inspired by the im-not-ai (Humanize KR) project.

Version: 1.2.0