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BytesAgain β€Ί Skills β€Ί Linkedin Humanizer
πŸ¦€ ClawHub

Linkedin Humanizer

by @sergebulaev

Aggressively rewrites LinkedIn text to remove AI indicators and add human traits, ensuring posts and comments read authentically before publishing.

Versionv1.0.0
Downloads287
#security#writing#social-media#legal
TERMINAL
clawhub install linkedin-humanizer

πŸ“– About This Skill

name: linkedin-humanizer description: Remove AI tells from any LinkedIn post or comment draft. Aggressive scrubber that strips em dashes, AI vocabulary (leverage, fundamentally, delve, harness), rule-of-three lists, filler openers, and uniform sentence rhythm. Adds human fingerprints (specific numbers, named entities, varied sentence length). Use before publishing any AI-drafted content. Keywords: humanizer, AI detection, OriginalityAI, GPTZero, scrub AI tells, rewrite human.

LinkedIn Humanizer

Aggressively rewrites any text to pass AI detectors and read authentically human. Based on Wikipedia's "Signs of AI writing" taxonomy plus 2026 LinkedIn-specific patterns.

When to use

  • Before publishing any AI-drafted post or comment
  • When linkedin-post-audit flags AI tells
  • When a draft feels "off" and you can't pinpoint why

    • Input

    Any text (post, comment, reply, DM). Optional: target voice samples (past human posts by the user).

    Output

  • Rewritten text with AI tells removed
  • Diff showing what changed and why
  • Per-sentence perplexity estimate (higher = more human)
  • Confidence: "human", "mixed", "AI-likely"

    • The three passes

    Pass 1 β€” SCRUB (delete or replace)

    Punctuation:

  • β€” β†’ . or ,
  • – β†’ - or to
  • -- β†’ . or ,
  • " " β†’ "

    • Vocabulary (regex-strip and replace):

  • leverage β†’ use
  • utilize β†’ use
  • facilitate β†’ help
  • streamline β†’ simplify
  • delve β†’ look
  • navigate β†’ handle
  • unlock β†’ find
  • harness β†’ use
  • foster β†’ build
  • cultivate β†’ grow
  • fundamentally β†’ (delete)
  • essentially β†’ (delete)
  • ultimately β†’ (delete)
  • crucially β†’ (delete)
  • notably β†’ (delete)
  • landscape β†’ field (or delete)
  • ecosystem β†’ (contextual)
  • paradigm β†’ approach
  • realm β†’ area
  • robust β†’ solid
  • seamless β†’ smooth

    • Phrase-level:

  • "It's not just X, it's Y" β†’ rewrite as a single claim
  • "In today's fast-paced world" β†’ delete opener entirely
  • "game-changer" β†’ specific descriptor
  • "deep dive" β†’ "look" or "analysis"
  • "at the end of the day" β†’ delete

    • Pass 2 β€” BREAK (force burstiness)

    Target: Flesch reading ease >55. Sentence length variance >40%.

  • If all sentences are 15-22 words, force-break at least 1 in 3 into <8-word sentences
  • Add at least one sentence fragment ("Worth it.", "Every time.")
  • Break rule-of-three lists into twos or fours
  • Break perfect parallel structures with one asymmetric sentence

    • Pass 3 β€” ADD (human fingerprints)

    Require at least:

  • 1 specific number per 100 words (replace "many" / "significant" / "massive")
  • 1 named entity (real person, company, date, city)
  • 1 first-person sensory detail
  • 1 contradiction or self-correction
  • 1 moment of vulnerability or stakes

    • If the input lacks these, ask the user for a specific number or anecdote to plug in. Don't fabricate.

    Non-negotiable rules

  • Preserve the user's actual claim. Humanizing β‰  changing meaning.
  • Capitalize all names (Dharmesh, Felix, HubSpot, Claude).
  • Never introduce facts that weren't in the input. If a number is missing, ask.
  • Keep the user's sentence-level voice quirks (lowercase starts, .. soft pauses).

    • Example

    > Input: > "In today's fast-paced landscape, businesses must fundamentally leverage AI to unlock robust ROI β€” here's what I've learned." > > Output: > "businesses need AI to cut costs. here's what we learned running 35k LinkedIn profiles through our system daily." > > Diff: removed em dash, removed "in today's fast-paced landscape", removed "fundamentally", removed "leverage", removed "unlock", removed "robust", added specific number (35k), added named entity (LinkedIn).

    Files

  • SKILL.md β€” this file
  • references/scrub-rules.md β€” full regex patterns and replacement mapping
  • references/voice-fingerprint.md β€” how to preserve user voice while scrubbing

    • Related skills

  • linkedin-post-audit β€” detection-only pass (no rewrite)
  • linkedin-post-writer β€” generates drafts that already pass the humanizer

    • ⚑ When to Use

    TriggerAction
    - When `linkedin-post-audit` flags AI tells
    - When a draft feels "off" and you can't pinpoint why

    πŸ’‘ Examples

    > Input: > "In today's fast-paced landscape, businesses must fundamentally leverage AI to unlock robust ROI β€” here's what I've learned." > > Output: > "businesses need AI to cut costs. here's what we learned running 35k LinkedIn profiles through our system daily." > > Diff: removed em dash, removed "in today's fast-paced landscape", removed "fundamentally", removed "leverage", removed "unlock", removed "robust", added specific number (35k), added named entity (LinkedIn).