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

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)

Pattern	Why	Fix
\u2014 (em dash —) above ~1 per 100 words (1-2 per post)	Density tell, not a character tell. GPT-5.4 uses fewer than humans; 23% of top-creator LinkedIn posts contain one (author-relative ratio 1.09, not a tell). 3+ in a short post is the old GPT-4 glue habit	Replace only the excess: , or : or ( ) or a rewrite. Never . (fragment stacking is worse)
\u2014 at zero across a 300+ word post that reads as if it wanted one	Below the human baseline; reads as dash self-censoring	Leave one in
\u2013 (en dash –) between clauses	Same family	Replace with ,; number ranges stay
--	Same family	Replace with , or rewrite
\u201C\u201D (curly quotes)	Copy-paste artifact	Convert to "

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)

Pattern	Why	Fix
External link in post body	-40 to -60% reach penalty; LinkedIn suppresses off-platform traffic	Move link to first comment, or summarize the insight inline
"Comment YES if you agree" / "Drop a 🙌" / manufactured CTA	Algorithm explicitly detects and demotes engagement bait	Ask a specific open question tied to the post's thesis
Press-release / corporate-polished tone	Underperforms personal voice 3x; suppresses authenticity signals	Rewrite in first person with a concrete moment
Humble-brag opener ("honored to announce…")	Failures outperform humble brags 8.5x	Lead with what broke or what you learned
Significant edits within first hour of posting	Resets the algorithm's initial distribution test	Fix typos only in first 60 min; hold structural edits
Posts >3x/week from one author	Diminishing returns; cannibalizes own reach	Cap at 2-3x/week, same time/days
Company-page-only distribution	Employee posts get 6-8x more reach than company pages	Publish from personal profile, let company reshare
Pure vanity-metric chasing (likes only)	Likes are weakest signal; saves > comments > shares > likes	Design for saves: frameworks, templates, data
Announcement openers ("I'm excited to share")	Reads as PR; kills voice	Replace with the concrete moment that prompted the post

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