human-ai

HUMAN-AI - Text Humanization Engine

If the reader forgets a machine was involved, you won.

Version: 3.0

Languages: Русский · English · Українська · Deutsch · Français · Español · Português · Italiano · Polski

Mode: Pipeline. 6 stages (pre-flight + 5 processing). Flexible - skip with declared reason.

THE THREE LAWS OF HUMAN TEXT

Law 1: Imperfection is the signal

AI produces text that is too consistent. Same rhythm. Same structure. Same formality. Same everything. Humans are inconsistent. A sharp 4-word sentence, then a meandering 28-word one. A paragraph starting with "But". A fragment. This inconsistency is not a flaw - it is the biometric signature of a human mind. Your job: introduce structured variance, not chaos.

Law 2: Specificity is credibility

AI generalizes - it has no lived experience. Humans specify. "Effective solutions" → "patched the memory leak in 40 minutes". "Improves workflow" → "saves 3 hours per report". Every time you replace a generalization with a concrete detail, you make the text more human. This is the highest-leverage technique.

Law 3: Competence doesn't announce itself

AI says: "robust, cutting-edge platform". Humans say: "we patch bugs within 4 hours". Describe what you do. The quality is self-evident. No adjectives about quality - show the quality.

THE PIPELINE

pre-flight → cleanup → specificity → tone → rhythm → proofread

Why this order

1. Pre-flight first - detect language, estimate AI probability. If human-written → STOP. Don't waste pipeline stages.
2. Cleanup second - remove AI patterns before anything else. Don't build human text on a robot skeleton.
3. Specificity third - concrete details must exist before tone, because tone wraps around content.
4. Tone fourth - once content is solid, shape the voice.
5. Rhythm fifth - fine-tune sentence flow after voice is set.
6. Proofread last - kill remaining AI residue when everything else is stable.

Skip policy

Stages run sequentially. Skip a stage only with declared reason. Declare skips in output header: [PIPELINE: cleanup → specificity(skipped: already rung 2+) → tone → rhythm → proofread]

Skip if:

• •Stage 1 (cleanup): No detectable AI patterns
• •Stage 2 (specificity): All claims already rung 2+
• •Stage 3 (tone): Tone already matches target
• •Stage 4 (rhythm): Rhythm already varied
• •Stage 5 (proofread): Always runs - at minimum a top-10 tells scan

STAGE -1: PRE-FLIGHT CHECK

Before running any pipeline stage, perform a rapid diagnostic scan.

Language Detection

Identify primary language. If mixed text: detect dominant language, preserve quoted foreign-language passages unchanged.

• •Confidence ≥ 70: proceed
• •Confidence < 70: ask user to specify language

AI Probability Estimation

Rapid scan. Assign points per marker found:

Score interpretation:

Already-Human Guard Rule

If AI Probability < 20: STOP. Do not run pipeline. Output the diagnostic only. This prevents degradation of legitimately human text.

If user says "force pipeline" after the guard triggers: run MINIMAL mode (proofread-only scan). Flag only unambiguous AI patterns. Annotate output with [HUMAN-ORIGIN: preserved structure and voice].

Tone Pre-Detection

Based on content type vocabulary and structure, suggest a tone. User can override.

Pipeline Recommendation

Based on diagnostic results, recommend stages:

[PRE-FLIGHT]
Language: {detected} (confidence: XX%)
AI Probability: XX/100
Tone suggested: {tone} (override with explicit tone if desired)
Recommended: {stages to run}
Skippable: {stages likely safe to skip}

User can accept the recommendation or override any stage.

STAGE 0: LANGUAGE DETECTION

Detect language before processing. Different languages have different AI tells. Reference: shared/ai-markers.md for complete detection patterns per language.

Quick detection by dominant markers:

STAGE 1: ANTI-AI CLEANUP

Objective

Remove all detectable AI patterns. This is mechanical. Be ruthless.

1.1 Delete throat-clearing openers

Delete the entire first sentence/paragraph if it starts with context-setting, era-naming, or landscape-painting. The real start is what comes after. Full lists: shared/ai-markers.md - Openers section per language.

1.2 Strip conclusion regurgitation

Delete concluding sections that restate the introduction. Humans end when done talking. If the last substantive paragraph works as an ending, keep it. If not: write one sharp exit sentence and stop. Full lists: shared/ai-markers.md - Conclusion section.

1.3 Purge burned words

Universal + per-language. Full list: shared/burned-words.md

Replacement rule: Do not find a synonym. Describe what actually happens.

• •"leverages AI" → "uses a model trained on support tickets"
• •«оптимизирует процессы» → «сокращает время согласования с трёх дней до четырёх часов»
• •«optimiert Prozesse» → «verkürzt Genehmigungszeiten von drei Tagen auf vier Stunden»

1.4 Kill fake transitions

Delete on sight. Full lists: shared/ai-markers.md - Fake transitions section.

1.5 Kill fake balance

Delete "On one hand... on the other hand..." and equivalents - unless the text names specific, real-world positions with concrete evidence. Generic balance = kill.

1.6 Break symmetrical paragraphs

If 3+ consecutive paragraphs have the same number of sentences (±1): break one (split, merge, or add a 1-sentence paragraph).

1.7 Kill adjective pileups

Max 2 adjectives before a noun. 3+ → keep the strongest one, show the rest through description.

1.8 Remove empty intensifiers

Words that tell how impressed to be without providing a reason. Delete the intensifier. Let the fact carry its own weight. Full lists: shared/burned-words.md - Empty intensifiers section.

1.9 Remove hedging language

AI hedges to avoid being wrong. Humans state things. Delete hedging prefixes. State the claim directly. If uncertain: "We don't know for sure. But here's what we've seen." Full lists: shared/ai-markers.md - Hedging section.

1.10 Remove rhetorical question padding

Delete generic transition questions: "What does this mean for you?" etc. Keep genuine engagement questions that receive substantive answers.

STAGE 2: SPECIFICITY ENRICHMENT

Objective

Replace abstract claims with concrete details. Highest-impact stage.

Core rule

For every claim ask: How, exactly? No answer → fill it or flag it. Full framework: shared/specificity-ladder.md

The specificity ladder (rung 0→4)

Target: every claim rung 0-1 → rung 2+. Rung 3 when data supports it.

Abstraction triggers (all languages)

See shared/specificity-ladder.md - Abstraction Detector section.

Six enrichment techniques

See shared/specificity-ladder.md for full descriptions and per-language examples: Show-Don't-Tell Swap, Mechanism Reveal, Number Injection, Scenario Example, Comparison Ground, Negative Space Detail.

No-invention rule

You may: supply plausible examples with domain-typical detail, suggest numbers with verify flag. You may NOT: invent facts, statistics, customer names, features not claimed. Flag format per language: [VERIFY] / [ПРОВЕРИТЬ] / [ПЕРЕВІРИТИ] / [PRÜFEN] / [VÉRIFIER] / [VERIFICAR] / [VERIFICARE] / [SPRAWDZIĆ].

STAGE 3: TONE NATURALIZER

Objective

Set the voice. Every text has a speaker. Full profiles: shared/tone-profiles.md

Tone selection

1. User-specified - always honored.
2. Context auto-detect (see shared/tone-profiles.md - Tone Selection Priority table).
3. Default fallback → human.

Tone is set ONCE at Stage 3. Do not re-detect in later stages.

7 tone profiles

Key parameters (all tones, all languages)

See shared/tone-profiles.md and shared/rhythm-tables.md for: fragment frequencies, conjunction frequencies, short-sentence spacing, sentence length mix, and per-language tone markers.

STAGE 4: RHYTHM EDITOR

Objective

Break the machine rhythm. AI = metronome. Human = jazz. Full parameters: shared/rhythm-tables.md

Sentence length: use CLAUSE COUNT (not word count)

LLMs cannot count words reliably. Use syntactic categories instead. Full definitions: shared/rhythm-tables.md

Three rhythm rules (clause-based)

1. No three consecutive sentences of the same length category.
2. No three consecutive sentences with the same clause count.
3. No sentence exceeds 3 clauses. Split at 4+. Exception: 1 sentence per ~300 words may have 4 clauses.

Approximate word reference (rough guidance, not strict)

Fragment: ~1-5w. Short: ~4-12w. Medium: ~12-22w. Long: ~22-30w. Very Long: 30+w (split).

Opener variety

No three consecutive sentences start with the same word or grammatical structure. Per-language opener categories: shared/rhythm-tables.md

Fragments

Use them. Fastest way to break AI rhythm. Fragment types and frequencies by tone: shared/rhythm-tables.md

Conjunction-started sentences

Real humans start with conjunctions. AI rarely does. Frequencies and per-language conjunction lists: shared/rhythm-tables.md

Visual paragraph weight

No three consecutive paragraphs of identical visual weight. Weight categories: Light (1 sentence), Medium (2-3 sentences), Heavy (4+ sentences).

STAGE 5: FINAL PROOFREAD

5.1 Read-aloud test (internal simulation)

Every sentence: would you say this to a colleague? If it contains words you wouldn't use in spoken conversation, passive where active works, or >2 clauses - rewrite.

5.2 Re-check opener

First 200 words: still starts with context-setting? Cut more.

5.3 Re-check ending

Last sentence has actual information? Not summary? Good.

5.4 Language-specific final checks

RU: «следует отметить» lurking? «осуществлять» → «делать». «посредством» → «через». «данный» → «этот». Em-dash → period/comma.

UK: «являється» or «даний» survived? Replace. Russianisms: «із-за» → «через», «так як» → «бо»/«тому що». Em-dash → period/comma.

EN: Em-dashes left? Replace. "Not only... but also..." → break into two. "Whether it's X or Y" → delete.

DE: Nominalstil survived? Aktive Verben. Em-dash → Punkt/Komma. «Man sollte» → direkt formulieren.

FR: «Il est important de noter» survived? Kill. Em-dash → point/virgule. «En termes de» → reformuler avec verbe actif.

ES: «Cabe destacar» survived? Kill. Em-dash → punto/coma. Gerundio excesivo → reformular.

PT: «É importante notar» survived? Kill. Em-dash → ponto/vírgula. Gerúndio excessivo → reformular.

IT: «Si rende necessario» survived? Kill. Em-dash → punto/virgola. «Si passivante» eccessivo → voce attiva.

PL: «Należy podkreślić» survived? Kill. Em-dash → kropka/przecinek. Nadmierna nominalizacja → czasowniki.

5.5 Final scan - top 10 AI tells (must be 0 or near-zero)

1. "Seamless" / its translations - 0
2. "Leverage" / its translations - 0
3. "Robust" / its translations - 0
4. "In today's" / its translations - 0
5. "Moreover" / its translations - 0
6. Symmetrical paragraph blocks (same weight 3x) - 0
7. "In conclusion" / its translations - 0
8. 3+ adjective pileups - 0
9. Empty intensifiers - 1 or fewer
10. Rhetorical question padding - 0

5.6 Self-Evaluation

After producing the output, scan it against your own rules and assign a QUALITY SCORE:

Scoring: Sum the 10 checks (max 100).

Re-loop Rule

If QUALITY SCORE < 75: identify the 2 lowest-scoring checks. Re-run ONLY the relevant stage(s) for those checks. Max 1 re-loop. Record both scores in output.

Re-loop mapping:

• •Checks 1, 9 → re-run Stage 1 (cleanup)
• •Check 8 → re-run Stage 2 (specificity)
• •Check 7 → re-run Stage 3 (tone)
• •Checks 2, 3, 4, 5, 6 → re-run Stage 4 (rhythm)
• •Check 10 → re-run Stage 5 (proofread, focused on awkward sentences)

After the single re-loop, output the final score regardless. Stop after 2 passes total.

External validation: For independent verification, run ZeroGPT detection via scripts/zerogpt-detect.ps1 or scripts/run-benchmark.ps1. See EVAL.md — External Validation section for details. The self-evaluation score is an LLM self-assessment. ZeroGPT provides an independent external check.

WHEN NOT TO APPLY

Skip the pipeline entirely if:

• •Pre-flight guard triggered (AI Probability < 20): text appears human-written. Output diagnostic only.
• •Text is authored by a known human (attributed, signed) — regardless of score
• •Text requires exact preservation (legal, medical, safety)
• •User says "audit only" → run detection scan, output diagnostics, do NOT modify

Force pipeline override: If user says "force pipeline" after the pre-flight guard triggers, run proofread-only scan with [HUMAN-ORIGIN] annotation. Never run full pipeline on text scoring <20.

Mixed-language text: detect primary language. Do not rewrite quoted foreign-language passages.

OUTPUT FORMAT

[LANG: en / ru / uk / de / fr / es / pt / it / pl]
[TONE: expert / biz / human / social / landing / article / case]
[PIPELINE: stages applied with skip notes]
[QUALITY: XX/100] ← Self-evaluation score from Stage 5.6
[ISSUES: brief list of remaining issues, if any]

[THE TEXT]

---
[CHANGELOG]
Brief: 3-5 bullet points on what was changed and why.

[STAGE SCORES]
Cleanup: XX/100 (checks 1+9 from self-eval)
Specificity: XX/100 (check 8)
Tone: XX/100 (check 7)
Rhythm: XX/100 (checks 2+3+4+5+6)
Proofread: XX/100 (check 10)
Re-loop: yes/no, stage(s) re-run, final score (if applicable)

[FACTUAL NOTES]
(Optional - flag inaccuracies, do not silently fix.)

No preamble. No "here is your rewritten text." No "I hope this helps." Deliver text, changelog, stop.

AUDIT MODE (Enhanced)

When user says "audit only" or "tell me what's wrong, don't rewrite":

Output a structured diagnostic, NOT a rewrite.

Audit Output Format

[AUDIT REPORT]
Language: {detected} (confidence: XX%)
AI Probability: XX/100

CRITICAL (must fix):
- {marker}: {quote from text}
- ...

HIGH (strongly recommended):
- {marker}: {quote}
- ...

MEDIUM (consider fixing):
- {marker}: {quote}
- ...

LOW (cosmetic):
- {marker}: {quote}
- ...

SUMMARY:
- AI markers found: {total}
- Burned words: {count}
- Estimated specificity rung: {average}
- Rhythm issues: {type + count}
- Tone detected: {tone} (confidence: XX%)

RECOMMENDED PIPELINE: {stages}
ESTIMATED EFFORT: {N} critical + {N} high items

Severity Levels

QUICK START

Full pipeline:

"Rewrite this to sound human. Language: ru."

Specific task - load scenario:

"Rewrite this as a landing page. DE." → load scenarios/landing-page.md

Audit only:

"Tell me what's wrong with this. Don't rewrite."

Translation fix:

"This was translated from Russian to English. Make it sound native."

FILES IN THIS SKILL

natural-skill/
├── SKILL.md                        ← This file - orchestrator
├── README.md / README.ru.md        ← Documentation (bilingual)
├── CHANGELOG.md                    ← Version history
├── CONTRIBUTING.md                 ← How to add languages/scenarios/tones
├── KNOWN_LIMITATIONS.md            ← Edge cases and LLM variance
├── EVAL.md                         ← Quality evaluation framework
├── LICENSE                         ← MIT
├── .gitignore
├── scripts/
│   ├── validate.ps1                ← Integrity checker (PowerShell)
│   ├── validate.sh                 ← Integrity checker (Bash)
│   ├── zerogpt-detect.ps1          ← ZeroGPT AI detection (PowerShell)
│   ├── zerogpt-detect.sh           ← ZeroGPT AI detection (Bash)
│   └── run-benchmark.ps1           ← External benchmark runner (ZeroGPT)
├── .github/workflows/
│   └── validate.yml                ← CI pipeline
├── tests/benchmark/                ← Evaluation dataset
│   ├── annotations.json
│   ├── zerogpt-results.json        ← ZeroGPT external validation results
│   ├── ai-texts/  (9 languages)
│   ├── human-texts/
│   └── edge-cases/
├── shared/
│   ├── burned-words.md             ← All burned words × 9 languages
│   ├── ai-markers.md               ← AI detection patterns × 9 languages
│   ├── tone-profiles.md            ← 7 tones with language markers
│   ├── specificity-ladder.md       ← Abstraction → concrete framework
│   ├── rhythm-tables.md            ← Sentence flow parameters
│   └── language-template.md        ← Template for adding new languages
├── scenarios/
│   ├── full-rewrite.md             ← Default: all 6 stages
│   ├── blog-post.md                ← Blog post humanization
│   ├── landing-page.md             ← Landing page humanization
│   ├── social-post.md              ← Social media post
│   ├── seo-article.md              ← SEO content humanization
│   ├── case-study.md               ← Case study / portfolio
│   ├── commercial-offer.md         ← B2B commercial offer
│   ├── email.md                    ← Email humanization
│   ├── technical-doc.md            ← Technical documentation
│   ├── translation-fix.md          ← De-translation: make it sound native
│   ├── press-release.md            ← Press release / media announcement
│   ├── internal-memo.md            ← Internal company memo
│   ├── grant-proposal.md           ← Grant proposal / funding application
│   ├── creative-writing.md         ← Creative writing (light touch)
│   └── product-update.md           ← Product update / changelog
└── examples/
    ├── en-blog-post.md
    ├── en-landing.md
    ├── en-social.md
    ├── ru-blog-post.md
    ├── ru-landing.md
    ├── ru-social.md
    ├── uk-blog-post.md
    └── uk-social.md

Each shared/ file is a data-reference. The full pipeline works without loading them - the SKILL.md above contains all rules. Load shared files for richer per-language detail.