/linkedin-humanizer

AI Tells — Complete Blacklist (V3, 2026-09)

Scored the way readers read: by density per paragraph, not per word. One marker in a paragraph is English. Three is a signature. The exceptions that fail on a single hit are listed as such.

Contents

• Punctuation (regex)
• Vocabulary markers (density-scored)
• Phrase blacklist (single hit)
• Opening-line tells
• Closing-line tells
• Structural tells
• 2026 dos-and-donts blockers (auto-fail)
• Attention budget
• Regex patterns (for audit implementation)

Punctuation (regex)

Vocabulary markers (density-scored)

Count per paragraph. 3+ = rewrite the paragraph. 2 = replace the weakest. 1 = leave it. AI vocabulary is the one marker that is consistently reach-negative on LinkedIn in our own corpus (0.74-0.84 author-relative), so this pass stays even though the word list changed.

Durable 2026 set (common words, 2-5x human rate across GPT-5.5 / Claude 4.8 / Gemini 3.1): significant, crucial, notably, particularly, comprehensive, insights, robust, leverage, foster, landscape, nuanced, multifaceted, holistic, streamline, elevate, empower

Older corporate verbs (weaker but still cited by readers): utilize, facilitate, harness, unlock, navigate, seamless, ecosystem

Filler adverbs: fundamentally, essentially, ultimately, crucially, notably, particularly

Grammar markers: sentence-opening "-ing" clause ("Leveraging our data, we..."), nominalisation ("the implementation of"), stacked abstract nouns (alignment / transformation / optimization / synergy)

2026 LinkedIn layer: quietly, "X matters." as a sentence, compound(s), "a signal", "the work", "built different", load-bearing, "doing the heavy lifting", "let that sink in", "that's the real story"

Decaying 2023-24 set (count as one marker each, but do not chase in isolation): delve, tapestry, realm, intricate, journey, paradigm, cultivate

Phrase blacklist (single hit = fix)

Reveal bridges and negative parallelism are scrubbed on one hit because they are reach-negative on LinkedIn (vendor data, 2026):

• "The result?" / "The catch?" / "The kicker?" (-4.8%)
• "It's not just X, it's Y" and all 6 negative-parallelism forms (-4.9%)
• "Stop X, start Y" (-6.7%)
• "Here's what / Here's how / Here's the thing" (-4.3%)
• "In today's fast-paced world"
• "Game-changer"
• "Deep dive"
• "Needle-moving"
• "Move the needle"
• "At the end of the day"
• "When it comes to"
• "In the age of AI"
• "Paradigm shift"
• "The hard truth is" / "The uncomfortable reality is"
• Sincerity announcements as opener or pivot: "let me be honest", "I'll be real", "honestly?", "to be direct", "the honest version is", "honest caveat", "real talk", "full transparency", "unpopular opinion:"

Opening-line tells

• Any sentence starting with "In today's..."
• Rhetorical question hooks ("Have you ever wondered...?") — dead on LinkedIn
• All-caps first line ("THIS CHANGED EVERYTHING.")
• "Most people don't realize..."
• "Here's a hard truth..."

Closing-line tells

• "What do you think?"
• "Thoughts?"
• "Agree or disagree?"
• "Let me know in the comments!"
• "Tag someone who needs this."

Structural tells

• Every sentence the same length, machine-flat (expert readers cite structure 36% of the time). Fix only where it reads flat; on LinkedIn sentence-length variance is not a reach lever in either direction (our corpus, within-creator: null to slightly negative), so never manufacture it
• Staccato stacks: "Short. Punchy. Done.", "Simple. Effective. Easy.", "No X. No Y. Just Z.", "All the X. None of the Y."
• One-word paragraphs ("Still." "Mostly." "Exactly.")
• More than 2 standalone fragments (<4 words) in the post
• Long/short/long/short seesaw across the whole post (mechanical alternation is a humanizer fingerprint)
• Pseudo-Socratic Q&A ("Why? Because...")
• Every paragraph 3 lines
• Perfect parallel structure across a list
• Stacked or perfectly parallel triads, or 3+ triads in one post ("faster, cheaper, better"). One natural triad is fine
• Hedging stacks: "perhaps", "might", "could potentially", "it seems" (performed hesitancy runs 2x human rate)
• Framed confession: a sincerity sentence wrapped around a fact ("I'll be honest, this hurt: we lost the client"). The fact alone is fine
• Passive voice >10% of clauses
• Uniformly flat tone with no reaction, no opinion, no concrete detail (the over-scrubbed fingerprint)

2026 dos-and-donts blockers (auto-fail)

Attention budget

Average user screen attention is 47 seconds (down from 150 seconds in 2004). Post dwell-time target: 31-60 seconds.

Flag any draft that demands >60s of continuous reading without a visual break, list, or fragment sentence — it'll lose the skim layer.

Regex patterns (for audit implementation)

import re

# Verb stems that should match every inflection (-s, -ing, -ed, -es).
# Use a non-capturing inflection suffix so "harnessed", "fostering", "unlocks" all match.
_VERB_STEMS = (
    "leverag", "utiliz", "facilitat", "streamlin", "delv", "navigat",
    "unlock", "harness", "foster", "cultivat", "elevat", "empower",
)
_VERB_GROUP = "|".join(_VERB_STEMS)

# DENSITY-SCORED markers: count hits per paragraph. 3+ = rewrite paragraph, 2 = replace weakest, 1 = leave.
DENSITY_PATTERNS = {
    "vocab_verbs": rf"\b(?:{_VERB_GROUP})(?:e|es|ed|ing|s)?\b",
    "vocab_2026": r"(?i)\b(significant(ly)?|crucial(ly)?|notably|particularly|comprehensive|insights?|robust|landscape|nuanced|multifaceted|holistic|seamless|ecosystem)\b",
    "adverb_filler": r"(?i)\b(fundamentally|essentially|ultimately|arguably|certainly|definitely|undoubtedly)\b",
    "ing_opener": r"(?m)^[\s>*\-]*[A-Z][a-z]+ing\b[^.\n]{0,60},",
    "nominalisation": r"(?i)\bthe \w+(?:tion|sion|ment|ance|ence|ization|isation) of\b",
    "linkedin_2026": r"(?i)\b(quietly|compound(s|ing)?|(a|the) signal|the work|built different|load-bearing|doing the heavy lifting)\b",
    "linkedin_2026_matters": r"(?im)^\w+ matters\.$",
    "decaying_2024": r"(?i)\b(delve|delving|tapestry|realm|intricate|journey|paradigm)\b",
}

# SINGLE-HIT patterns: one match = fix.
AI_PATTERNS = {
    "en_dash": r"\u2013",
    "double_dash": r"--",
    # Reveal bridges (reach-negative on LinkedIn).
    "reveal_bridge": r"(?im)^(the (result|outcome|answer|lesson|catch|kicker|truth)\?|here'?s (what|how|why|the thing)\b|stop \w+[^.\n]{0,40}[.,] ?start \b|plot twist:)",
    "inflated_symbolism": r"(?i)not just \w+, it'?s \w+",
    "neg_parallel": r"(?i)\b(isn'?t|not) (about )?[^,.\n]{1,40}, it'?s (about )?\b",
    # Staccato / forced rhythm.
    "staccato_stack": r"(?m)^(\w+\. ){2,}\w+\.$",
    "one_word_paragraph": r"(?m)^\w+\.$",
    "no_no_just": r"(?i)\bno \w+\. no \w+\. (just|only) \w+",
    "all_none": r"(?i)\ball (of )?the \w+\. none of the \w+",
    "pseudo_socratic": r"(?i)\b(why|how)\? (because|simple)\b",
    # Sincerity announcements as opener or pivot.
    "sincerity_marker": r"(?im)^[\s>*\-]*(let me be (honest|real|direct|clear)|i'?ll be (honest|real|direct)|honestly\?|honest (caveat|version|answer)|the honest (version|answer|truth) is|to be (direct|honest|transparent)|real talk|full transparency|can i be (honest|vulnerable)|not gonna lie|ngl|unpopular opinion)\b",
    # Case-insensitive opener match; allow leading whitespace, bullets, or quote marks.
    "opener_filler": r"(?im)^[\s>*\-]*[\"'\u201c]?(In today's|Have you ever|Most people don't realize|Here's a hard truth)",
    # Generic closing-question CTA: matches "What do you think?" / "What are your thoughts?" / "Thoughts?" / "Your take?" etc.
    "closer_filler": r"(?i)(what (do|are) you (think|your? thought)|what(?:'s| is) your (take|thoughts?)|thoughts\?|agree or disagree\?|let me know in the comments|tag someone|let that sink in|that'?s the real story)",
}

def em_dash_excess(text: str) -> int:
    """Em dashes above the cap (~1 per 100 words, floor 1, ceiling 2 per post). 0 = fine."""
    words = len(text.split())
    cap = max(1, min(2, round(words / 100)))
    return max(0, text.count("\u2014") - cap)

def fragment_count(text: str) -> int:
    """Standalone sentences under 4 words. More than 2 per post = forced rhythm."""
    return sum(1 for s in re.split(r"(?<=[.!?])\s+", text) if 0 < len(s.split()) < 4)

def paragraph_density(paragraph: str) -> int:
    return sum(len(re.findall(p, paragraph)) for p in DENSITY_PATTERNS.values())

# Compile-time sanity: catches inflected and conjugated forms.
assert re.search(DENSITY_PATTERNS["vocab_verbs"], "We harnessed cross-functional synergy.")
assert re.search(DENSITY_PATTERNS["vocab_verbs"], "We fostered alignment.")
assert re.search(DENSITY_PATTERNS["vocab_verbs"], "We unlocked 47% gains.")
assert re.search(DENSITY_PATTERNS["ing_opener"], "Leveraging our data, we cut churn.")
assert re.search(DENSITY_PATTERNS["linkedin_2026"], "The work quietly compounds.")
assert re.search(DENSITY_PATTERNS["linkedin_2026_matters"], "Consistency matters.")
# Inline (?i)/(?m) flags are only legal at position 0 from Python 3.11 on; a mid-pattern
# flag raises re.error at import and takes the whole audit down before it scores anything.
for _name, _pat in {**DENSITY_PATTERNS, **AI_PATTERNS}.items():
    re.compile(_pat)
assert re.search(AI_PATTERNS["closer_filler"], "What are your thoughts?")
assert re.search(AI_PATTERNS["closer_filler"], "What's your take?")
assert re.search(AI_PATTERNS["reveal_bridge"], "The result? We doubled.")
assert re.search(AI_PATTERNS["no_no_just"], "No meetings. No decks. Just code.")
assert re.search(AI_PATTERNS["sincerity_marker"], "Let me be honest: this one hurt.")
assert em_dash_excess("a \u2014 b " * 3 + "word " * 90) == 2
assert em_dash_excess("one \u2014 dash in " + "word " * 120) == 0