De AI

De-AI Text Humanization Skill

Objective

Transform AI-sounding text into human, authentic writing while strictly preserving meaning and facts. Focus on quality improvement over detection evasion.

Core Principles

1. Meaning Preservation First: Never sacrifice accuracy for "humanness"
2. Language-Aware: Optimize for language-specific patterns (Russian ≠ English ≠ German)
3. Iterative Dialogue: Understand context before processing
4. Transparency: Explain changes when requested
5. Professional Quality: Focus on readability and authenticity, not academic cheating

Workflow

Phase 1: Context Gathering (if interactive=true)

Use AskUserQuestion to understand:

1. Purpose & Audience

   • Why was this written? (inform, persuade, document, entertain)
   • Who will read it? (general public, specialists, stakeholders)

2. Constraints & Priorities

   • Must preserve: facts, citations, technical terms, specific phrasing?
   • Flexibility: can restructure? can cut redundancy? can add subjectivity?
   • Tone target: formal/casual, confident/exploratory, personal/objective?

3. Language-Specific

   • For Russian: formality level, should preserve/add participles?
   • For German: compound words acceptable? prefer simple structures?
   • For English: US/UK/International conventions?

Skip questions if:

• User explicitly said "don't ask questions"
• Interactive mode disabled
• Context is obvious from text itself

Phase 2: AI Tell Diagnosis

Identify patterns at six levels:

1. Structural Level

• Uniform paragraph length
• List-like enumeration
• Symmetrical organization
• Predictable flow

2. Sentence Level

• Uniform complexity (all mid-range)
• Similar lengths
• Predictable syntax
• No fragments or run-ons

3. Lexical Level

Universal AI Words (any language):

• crucial, transformative, robust, comprehensive
• delve, underscore, paradigm, foster, navigate
• landscape, realm, leverage, synergy

Russian AI Tells:

• важно отметить, следует подчеркнуть, необходимо учитывать
• в современном мире, в конечном счете, в целом
• данный, указанный, вышеуказанный (excessive formal pronouns)
• комплексный, инновационный, эффективный (overused adjectives)

German AI Tells:

• Es ist wichtig zu beachten, Man sollte bedenken
• Im Hinblick auf, Vor diesem Hintergrund
• Darüber hinaus, Ferner, Zudem (transition overuse)
• umfassend, nachhaltig, ganzheitlich, zielgerichtet

English AI Tells:

• "it is important to note", "in order to", "let's explore"
• "it's worth noting", "the fact that", "in today's world"

LinkedIn AI Tells (platform-specific):

• Uniform single-paragraph-per-insight cadence (each paragraph = one neat point)
• "Here's what I learned" / "Here's the thing" signposting
• Feature changelogs disguised as prose (bullet points rewritten as sentences)
• The builder-post arc: problem → learnings list → "I built a thing" → CTA/link
• Perfectly steady confidence throughout (no doubt, no mess)
• One-line paragraph openers for dramatic effect (overused)
• Numbered insights ("Three things I learned:", "5 takeaways:")
• Engagement-bait closers ("What's your experience?", "Drop a comment")

4. Voice Level

• Emotional flatness
• Balanced phrasing throughout
• No subjective markers
• Consistent confidence

5. Rhetorical Level

• Meta-signposting ("here's the thing", "the key is")
• Rhetorical Q + immediate answer
• False binaries
• Over-explaining

6. Predictability Level

• Too-safe word choices
• Expected patterns
• Low perplexity
• No surprises

Phase 3: Humanization

Apply language-appropriate transformations:

Universal Techniques

Structural Variation:

• Paragraph length: 1-8 sentences (mix aggressively)
• Include 1+ very short paragraph (1 sentence)
• Include 1+ longer paragraph (6+ sentences)
• Break symmetry

Sentence Diversity:

• Very simple: 3-5 words
• Very complex: 25+ words
• Use fragments naturally
• Occasional run-ons
• Start with And/But/So when conversational

Lexical Diversity:

• Ban stock AI vocabulary
• Unexpected (appropriate) word choices
• No phrase repetition
• Mix formal/informal register

Voice Variation:

• Emotional range (doubt, certainty, frustration, enthusiasm)
• Subjective markers when appropriate
• Vary confidence levels
• Let opinions show

Increase Unpredictability:

• Less predictable words
• Break expected patterns
• Surprising connections
• Avoid formulaic transitions

Cut Meta-Commentary:

• Remove signposting
• State points directly
• No preamble phrases
• No "let's explore" or "it's worth noting"

Trust the Reader:

• Don't explain everything
• Leave implications unstated
• Use concrete specifics without setup
• Let readers connect

Reduce Transitions:

• Let adjacent ideas stand alone
• Allow abrupt shifts when natural
• Don't over-connect

Allow Imperfection:

• Keep rough edges
• Not every thought perfectly polished
• Minor tone inconsistencies are human
• Embrace occasional messiness

Language-Specific Optimization

Russian:

• Reduce excessive participles
• Replace formal pronouns with simpler forms
• Break long compound sentences
• Add ellipsis, dashes for rhythm
• Mix formality appropriately for audience
• Use colloquial particles sparingly
• Replace канцелярит with живую речь

German:

• Break excessive compound words when clarity helps
• Vary sentence structure (not all Hauptsatz-Nebensatz)
• Use shorter sentences occasionally
• Add conversational particles (doch, halt, eben) appropriately
• Mix Nominalstil with Verbalstil
• Avoid Schachtelsätze (nested clauses)

English:

• Use contractions naturally
• Mix latinate and germanic vocabulary
• Vary sentence openings beyond subject-verb
• Add occasional dialect/regional flavor if appropriate
• Use active voice predominantly

Platform-Specific: LinkedIn

Break the Builder-Post Arc:

• Don't follow problem → learnings → "I built X" → link. Rearrange, start mid-story, or drop sections entirely
• The arc is the single biggest AI tell on LinkedIn -- every AI-assisted post follows it

Vary Paragraph Cadence:

• AI LinkedIn posts have uniform 1-paragraph-per-insight rhythm. Break it: merge two ideas in one paragraph, split one across three, use a single-sentence paragraph that isn't a dramatic opener
• Not every paragraph should start with a hook or topic sentence

Kill Signposts:

• Remove "Here's what I learned", "Here's the thing", "Three things I noticed"
• State insights directly without announcing them
• Numbered lists ("5 takeaways") are the most obvious AI LinkedIn tell

Inject Doubt and Specificity:

• Replace steady confidence with actual uncertainty ("I'm not sure this scales", "Could be wrong")
• Add concrete sensory details (names, places, objects) instead of generic descriptions
• Self-deprecation and false starts ("Sounds dumb. Works every time.", "More like --") read as human

Skip the Engagement Bait:

• Remove "What's your experience?", "Drop your thoughts below", "Agree or disagree?"
• If there's a CTA, make it specific and useful ("GitHub link in comments"), not engagement-farming

Tone: Between personal and essay. First-person, opinionated, but grounded in professional context. Allow rough edges -- LinkedIn readers scroll fast, so a slightly messy but authentic post outperforms a polished-but-generic one.

Phase 4: Register Adaptation

Match humanization intensity to text type:

Phase 5: Quality Check

Verify across dimensions:

• Meaning preserved (facts unchanged, intent maintained)
• Perplexity increased (less predictable words, varied vocabulary)
• Structural variation (sentence/paragraph length diversity)
• Lexical diversity (no repetitive phrases or stock AI words)
• Voice authenticity (emotional range, subjective elements)
• Syntactic complexity (mix of very simple and very complex)
• Clarity maintained (if unclear or too messy, refine)
• Language-specific patterns addressed

Phase 6: Output

Default: Revised text only (no commentary)

If explain mode: Revised text + short bullet list of main AI tells removed

If text too generic: Ask 2-3 targeted questions to avoid inventing details

Error Handling

If text is already human: "This text already reads as human-written. Only minor refinements applied."

If meaning at risk: Stop and ask: "This change might alter meaning: [specific example]. Proceed?"

If language detection fails: Ask user to specify language explicitly

If technical terms unclear: Ask before replacing

Usage

# Process a file
/de-ai path/to/article.md

# With options via natural language
/de-ai make this more human, it's a Russian essay

# Quick non-interactive
/de-ai --no-questions path/to/draft.txt

Output: creates [original]-humanized.[ext] or replaces inline.

Learnings

2026-02-25

Context: First run after converting from old skill.yaml format to SKILL.md. Humanized a LinkedIn post (personal register, explain mode).

What Worked:

• Skipping interactive questions when register and explain flag are provided via args -- context was obvious from the file itself.
• Diagnosis-then-rewrite flow: listing specific AI tells before rewriting gives user transparency and makes the changes defensible.
• Personal register produces the best results -- adding self-deprecation ("Sounds dumb. Works every time"), sensory details ("in his kitchen"), and false starts ("More like --") are high-impact, low-effort humanizations.

Pattern Discovered:

• LinkedIn posts have their own AI-tell signature: uniform single-paragraph-per-insight cadence, "Here's what I learned" signpost, feature changelogs disguised as prose, perfectly steady confidence throughout. These are distinct from essay or article tells.
• The biggest single improvement: breaking the "problem -> learnings list -> I built a thing -> link" template that every AI-assisted LinkedIn builder post follows.

What to Improve:

• Could add a LinkedIn-specific register (between personal and essay) that targets the platform's specific AI patterns.
• The old format (skill.yaml + system.md) silently failed -- no error message, just "Unknown skill". Worth noting for other skills that may have the same issue.