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de-ai

Testing & quality

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Updated 10-15 21:53

About this skill

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:

  • it is important to note, it should be emphasized, it is necessary to take into account
  • in the modern world, ultimately, on the whole
  • given, specified, aforementioned (excessive formal pronouns)
  • comprehensive, innovative, effective (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 officialese with lively speech

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:

Register Approach
Personal Strong subjective voice, emotional variation, first-person, sensory details
LinkedIn Break builder-post arc, vary paragraph cadence, kill signposts, inject doubt/specificity
Essay/Analysis Varied formality, allow uncertainty, nuanced positions
Critique Evaluative language, stronger opinions, clear judgments
Narrative Temporal variation, personal reflection, observed details
Technical Preserve precision, reduce only stylistic AI tells, keep terminology
Academic Maintain rigor, remove meta-commentary, preserve citations exactly

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.