HUMAN-AI - Text Humanization Engine

Repository status: human-ai is the active source of truth; HumanAI is a backup clone of the same remote, kept in sync manually.

[ English | Русский ]

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

An AI skill system that rewrites machine-generated text to sound human-written - across 9 languages. Detects and removes AI fingerprints through a 5-stage pipeline. Skill class: orchestrator - self-contained SKILL.md plus on-demand references.

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What is this?

HUMAN-AI is a system prompt (skill) for any LLM - GPT, Claude, Gemini, DeepSeek, or any capable model. It transforms AI-generated text that "smells like AI" into text that reads like a competent human wrote it.

AI writes with a fingerprint: same rhythm, same structure, same burned words, same dead perfection. HUMAN-AI strips that fingerprint - not by making text "less AI", but by making it more human.

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How it works

1. Load skills/human-ai/SKILL.md as a system prompt into any LLM.
2. Give a task: "Rewrite this to sound human. Language: de." / "Make this a case study. EN." / "Just clean the AI patterns."
3. Get output with language detected, tone set, pipeline stages applied, and changelog.

Three modes:

• Full Pipeline - 5 stages: cleanup → specificity → tone → rhythm → proofread. Every text passes through. Flexible: skip stages with declared reason.
• Single Stage - Run just one stage: cleanup only, specificity only, rhythm only, etc.
• Audit Mode - Diagnostic scan only. Don't rewrite. Flag all AI patterns found.

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The 5-Stage Pipeline

anti-ai-cleanup → specificity → tone → rhythm → proofread

Stage	What it does	Skip if
1. Cleanup	Remove AI patterns: openers, burned words, fake transitions, hedging, conclusions	No AI patterns found
2. Specificity	Replace abstractions with concrete details, numbers, examples	All claims rung 2+
3. Tone	Set the voice (7 profiles)	Tone already correct
4. Rhythm	Break metronome: vary sentence length, openers, fragments	Rhythm already varied
5. Proofread	Final scan for residual AI tells	Always runs (minimal scan)

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7 Tone Profiles

Tone	Voice	Best for
expert	The Practitioner	Technical docs, deep analysis
biz	The Consultant	B2B proposals, service pages
human	The Smart Friend	Blog posts, about pages, emails
social	The Scroller	LinkedIn, Twitter/X, Telegram
landing	The Seller	Product pages, sales pages
article	The Explainer	Long-form guides, tutorials
case	The Case Study	Portfolio, success stories

Each tone has per-language markers: fragment frequencies, conjunction rules, formality levels, cultural notes.

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9 Languages

Language	AI markers	Burned words	Tone notes
English	✓	✓	✓
Russian / Русский	✓	✓	✓
Ukrainian / Українська	✓	✓	✓
German / Deutsch	✓	✓	✓
French / Français	✓	✓	✓
Spanish / Español	✓	✓	✓
Portuguese / Português	✓	✓	✓
Italian / Italiano	✓	✓	✓
Polish / Polski	✓	✓	✓

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Architecture

human-ai/
├── skills/human-ai/                ← runtime skill package
│   ├── SKILL.md                    ← Main orchestrator (full pipeline)
│   ├── EVAL.md                     ← External evaluation framework
│   ├── shared/                     ← Data files for 9 languages
│   ├── scripts/                    ← Validation + external tools
│   ├── scenarios/                  ← 15 task-specific playbooks
│   ├── examples/                   ← Annotated before/after examples
│   └── agents/openai.yaml          ← OpenAI agent manifest
├── tests/benchmark/                ← Evaluation dataset (dev)
├── install.py                      ← Installer for Claude, OpenCode, Codex, .agents
├── README.md / README.ru.md        ← This documentation
└── PLAN.md / KNOWN_LIMITATIONS.md  ← Roadmap and limits (dev)

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Quick Start

Full pipeline

Rewrite this text to sound human. Language: ru.

Specific task (load scenario)

Rewrite this as a landing page. DE.

Single stage only

Just clean the AI patterns from this.
Add concrete details. Don't change the tone.
Fix the rhythm - it reads like a machine.

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.

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Example

Request: "Rewrite this AI-generated blog post intro to sound human. EN."

Input:

"In today's fast-paced digital landscape, businesses are increasingly leveraging cutting-edge AI solutions to optimize their workflows and streamline operations. This comprehensive guide will explore the transformative power of artificial intelligence..."

Output:

[LANG: en]
[TONE: human]
[PIPELINE: cleanup → specificity → tone → rhythm → proofread]
[QUALITY: 94/100]

Most AI tools promise to save time. They don't. They just move the work around.

We tested twelve of them over three months. Four actually helped. The rest added meetings, dashboards, and a new Slack channel you didn't ask for.

Here's what the four good ones have in common. And why the other eight failed.

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[CHANGELOG]
- Deleted "In today's fast-paced digital landscape" opener
- Burned words removed: leveraging, cutting-edge, optimize, streamline, transformative, comprehensive
- Specificity added: "twelve tools / three months / four helped"
- Tone: human - direct, opinionated, fragment opener
- Rhythm: clause mix with high variance

→ More examples in examples/

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Requirements

• Any capable LLM with a system prompt / custom instructions field
• For full skill functionality: a skill system that loads files from a folder (OpenCode, Claude Code)
• For standalone: copy skills/human-ai/SKILL.md as a system prompt - contains the complete pipeline
• External validation: ZeroGPT API key (optional) - for independent AI detection scoring via skills/human-ai/scripts/run-benchmark.ps1

Quality Validation

HUMAN-AI provides three layers of quality verification:

Layer	Method	Independence	How to run
1. Self-eval	Stage 5.6 Quality Score	LLM self-assessment	Built into pipeline
2. EVAL.md	5-metric LLM evaluation	Separate LLM call	EVAL.md prompt
3. ZeroGPT	External API detection	Fully independent	skills/human-ai/scripts/run-benchmark.ps1

ZeroGPT external validation:

# Set API key
$env:ZEROGPT_API_KEY = "your-key"

# Single text check
.\skills\human-ai\scripts\zerogpt-detect.ps1 -File "output.md"

# Full benchmark run (all test texts)
.\skills\human-ai\scripts\run-benchmark.ps1 -MaxTexts 10 -DelaySeconds 2

# Dry run (see what would be tested)
.\skills\human-ai\scripts\run-benchmark.ps1 -SkipApi

Results saved to tests/benchmark/zerogpt-results.json.

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Installation

The installer copies skills/human-ai/ into the selected agent directory:

python install.py --target all
python install.py --list
python install.py --check
python install.py --target project --dry-run

OpenCode

human-ai/ → .opencode/skills/          (project)
          → ~/.config/opencode/skills/  (global)

Claude Code

human-ai/ → ~/.claude/skills/

Any LLM (standalone)

Copy contents of skills/human-ai/SKILL.md as a system prompt. Add shared/ files for richer per-language detail.

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Ethics

Always:

• Flag invented numbers with [VERIFY]
• Preserve factual content - only change presentation
• Declare all changes in changelog

Never:

• Invent facts, statistics, testimonials, or customer names
• Silently correct factual inaccuracies (flag them in [FACTUAL NOTES])
• Rewrite legal, medical, or safety-critical text

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Integration

HUMAN-AI is designed to work with two other skills - RankWise (SEO content engine) and MindFluence (cognitive bias marketing). Together they form a complete content production pipeline.

Joint Prompt Triggers

HUMAN-AI recognizes these joint prompts and activates preservation rules automatically:

• "SEO-rewrite this (RankWise), then humanize it (human-ai)"
• "Humanize the RankWise output below"
• "human-ai pass on RankWise content"
• "MindFluence generated this. Now humanize it with human-ai."
• "Triple pipeline: RankWise → MindFluence → human-ai"

With RankWise (SEO)

The rule: RankWise handles SEO structure → human-ai handles human voice. Do not break SEO.

Preservation rules when processing RankWise content:

• Do NOT delete or alter H2/H3 headings that contain SEO keywords. Only rewrite the sentences surrounding them.
• Preserve keyword density of 0.8%-1.5%. If keyword instances are removed during cleanup, add equivalent keyword-adjacent language elsewhere.
• Maintain minimum 600 words (unless user explicitly requests shorter).
• Keep internal link anchors and placement - they are deliberately positioned for SEO.
• During Stage 1 (cleanup): skip deletion of keywords, internal links, and schema-relevant elements.
• Meta title/description: already SEO-optimized. Do not humanize them.

Conflict resolution (RankWise vs human-ai instincts):

1. Preserve keyword placement (K2, K4, K6, K8) - highest priority
2. Preserve heading structure (C13) - second priority
3. Then apply human voice cleanup
4. Accept ≤5% density fluctuation as acceptable trade-off

SEO-safe markers - do NOT delete during cleanup:

• Keywords in the first 100-150 words
• Keywords in H2 headings
• Keywords in image alt texts (if present)
• Internal link anchor text variety (exact-match ≤2)
• Meta title and description

Recommended pipeline invocation:

PIPELINE: cleanup(skipped: SEO structure) → specificity → tone → rhythm → proofread

With MindFluence (Cognitive Bias Marketing)

The rule: MindFluence engineers persuasion → human-ai humanizes the voice. Do not strip psychological structure.

Tone mapping (MindFluence → human-ai):

MindFluence Tone	human-ai Tone
bold-sell	landing
expert-calm	expert
rebel-edgy	social
warm-human	human
luxe-minimal	case

Preservation rules when processing MindFluence content:

• Do NOT strip bias markers: social proof numbers, anchoring prices, authority signals, scarcity cues, confirmation hooks.
• Do NOT delete power words that overlap with burned-word lists - they serve a psychological function.
• Do NOT break "hook" openings - they are deliberately patterned for System 1 capture.
• Preserve social proof specificity: "14,327 users this week" is a bias marker, not fluff.
• During Stage 1 (cleanup): skip cleanup of persuasion elements. Only remove generic AI patterns (throat-clearing, hedging, fake transitions).

Recommended pipeline invocation (preserving MindFluence tone):

PIPELINE: cleanup(skipped: bias structure) → specificity → tone(skipped: MindFluence tone) → rhythm → proofread

Triple Pipeline (RankWise → MindFluence → human-ai)

Recommended order and protocol:

1. RankWise Brief → SEO structure, keyword placement, heading hierarchy, link plan
2. MindFluence → Cognitive bias copy within the SEO skeleton, section-by-section bias annotations
3. human-ai → Humanize the voice while preserving BOTH SEO signals AND bias structure
4. RankWise Audit → Final 49-factor verification

Triple-pipeline preservation checklist for human-ai:

Preserve	From	Why
Keyword-containing H2/H3 headings	RankWise	SEO hierarchy breaks if altered
Internal link anchors	RankWise	Deliberate link-juice structure
Keyword density 0.8%-1.5%	RankWise	Under/over triggers ranking penalties
Social proof numbers	MindFluence	"14,327 users" - bias, not fluff
Anchoring / pricing figures	MindFluence	Reference points for value perception
Authority signals	MindFluence	Named sources, credentials, media logos
Scarcity / urgency cues	MindFluence	Genuine time/quantity limits
Power words	Both	Serve SEO sentiment + bias function
Minimum word count (600+)	RankWise	Thin content penalty threshold

Joint triple prompt template:

Triple pipeline:
1) RankWise SEO brief for [topic]. Keyword: [kw]. Language: [xx].
2) MindFluence generate from that brief. Tone: [expert-calm/warm-human/bold-sell].
3) human-ai humanize the MindFluence output. Preserve SEO structure + bias markers.
4) RankWise audit the final result.

human-ai invocation for triple pipeline:

PIPELINE: cleanup(skipped: SEO+bias elements) → specificity → tone(skipped: from MindFluence) → rhythm → proofread

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License

MIT

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GitHub Description

AI skill for rewriting machine-generated text to sound human-written across 9 languages. Active v4.1.1: pre-flight → cleanup → specificity → tone → rhythm → proofread.

GitHub Tags

ai anti-ai copywriting humanization humanizer llm multilingual prompt-engineering skill text-rewriting

Skills.sh: https://www.skills.sh/madeval/humanai