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 — SCRUB (delete or replace)

Punctuation:

• —

  →

  .

  or

  ,

• –

  →

  -

  or

  to

• --

  →

  .

  or

  ,

• " "

  →

  "

Vocabulary (regex-strip and replace):

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

Phrase-level:

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

Pass 2 — 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 — 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 ≠ 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 — 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

  — this file

• references/scrub-rules.md

  — full regex patterns and replacement mapping

• references/voice-fingerprint.md

  — how to preserve user voice while scrubbing

Related skills

• linkedin-post-audit

  — detection-only pass (no rewrite)

• linkedin-post-writer

  — generates drafts that already pass the humanizer