Linkedin Humanizer

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

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