| 63.64% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 3 | | adverbTags | | 0 | "Tomás stepped back [back]" | | 1 | "The familiar sign blinked weakly [weakly]" | | 2 | "Morris leaned casually [casually]" |
| | dialogueSentences | 39 | | tagDensity | 0.564 | | leniency | 1 | | rawRatio | 0.136 | | effectiveRatio | 0.136 | |
| 96.98% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1655 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 36.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1655 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "scanned" | | 1 | "predator" | | 2 | "vibrated" | | 3 | "familiar" | | 4 | "pulse" | | 5 | "charm" | | 6 | "flicked" | | 7 | "chaotic" | | 8 | "calculating" | | 9 | "reminder" | | 10 | "mechanical" | | 11 | "echoing" | | 12 | "could feel" | | 13 | "otherworldly" | | 14 | "stomach" | | 15 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
| | 1 | | label | "stomach dropped/sank" | | count | 1 |
|
| | highlights | | 0 | "knuckles went white" | | 1 | "stomach dropped" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 116 | | matches | | |
| 81.28% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 116 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1673 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1336 | | uniqueNames | 26 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Saint | 2 | | Christopher | 2 | | Ironic | 1 | | Tuesday | 1 | | Met | 1 | | Garden | 1 | | Thames | 1 | | Soho | 3 | | Raven | 2 | | Nest | 2 | | Tomás | 8 | | Herrera | 1 | | Spanish | 1 | | Elias | 2 | | Renwick | 3 | | Quinn | 17 | | Tube | 2 | | Veil | 2 | | Market | 2 | | Three | 2 | | Seeing | 1 | | Morris | 7 | | René | 1 | | Santiago | 1 | | Herrarar | 1 | | Biennale | 1 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Tuesday" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Elias" | | 6 | "Renwick" | | 7 | "Quinn" | | 8 | "Market" | | 9 | "Seeing" | | 10 | "Morris" | | 11 | "René" | | 12 | "Santiago" | | 13 | "Herrarar" | | 14 | "Biennale" |
| | places | | 0 | "Met" | | 1 | "Garden" | | 2 | "Thames" | | 3 | "Soho" | | 4 | "Raven" | | 5 | "Veil" |
| | globalScore | 0.864 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared near a crime scene exactly twenty-four hours before" |
| |
| 80.45% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.195 | | wordCount | 1673 | | matches | | 0 | "not once, but twice" | | 1 | "not just on finding Renwick, but on accepting a version of reality she wasn't sure she was pr" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 133 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 44.03 | | std | 29.26 | | cv | 0.665 | | sampleLengths | | 0 | 62 | | 1 | 79 | | 2 | 27 | | 3 | 59 | | 4 | 77 | | 5 | 125 | | 6 | 13 | | 7 | 17 | | 8 | 21 | | 9 | 19 | | 10 | 26 | | 11 | 22 | | 12 | 36 | | 13 | 103 | | 14 | 42 | | 15 | 13 | | 16 | 29 | | 17 | 48 | | 18 | 53 | | 19 | 36 | | 20 | 39 | | 21 | 28 | | 22 | 75 | | 23 | 48 | | 24 | 26 | | 25 | 16 | | 26 | 35 | | 27 | 19 | | 28 | 109 | | 29 | 24 | | 30 | 58 | | 31 | 31 | | 32 | 26 | | 33 | 7 | | 34 | 44 | | 35 | 49 | | 36 | 24 | | 37 | 108 |
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| 90.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 116 | | matches | | 0 | "been disturbed" | | 1 | "was known" | | 2 | "was – opened" | | 3 | "was prepared" | | 4 | "was wrapped" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 206 | | matches | | 0 | "was coming" | | 1 | "was looking" | | 2 | "was watching" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 18 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 133 | | ratio | 0.12 | | matches | | 0 | "Old habits die hard – she checked the time." | | 1 | "The medallion – too clean for a drain, too deliberate for a lost item." | | 2 | "Found without her head at the Thames barrier – another ritual gone wrong." | | 3 | "This was the fourth disappearance in as many months – each time, the medallion appeared near a crime scene exactly twenty-four hours before." | | 4 | "The air hung thick with the bitter scent of coffee and something else – sulphur." | | 5 | "Short, curly dark hair positioned just so, with that olive skin and scar along the left forearm – Tomás Herrera, former paramedic, now off-the-books miracle worker for whatever needed fixing." | | 6 | "Elias Renwick's ID photo – another checkpoint supervisor who'd disappeared three nights ago." | | 7 | "But she saw what she was looking for – a side alley that wasn't visible from the main street, leading down to a boarded staircase." | | 8 | "\"You working for them now, Tommy?\" Quinn's hand instinctively went to her holster – a memory more than a movement." | | 9 | "The familiar sign blinked weakly above – \"Veil Market.\" The occasional neon reflected off the slick stone walls, casting strange shapes in the darkness." | | 10 | "Even in the briefest moment, Quinn could feel it – the thrumming arc of energy, the energy that hummed under Soho – was stronger here, amplifying as she stepped onto the disused platform." | | 11 | "She'd seen this face not once, but twice – in her office, at her doorstep, and now here, deep in the Veil Market, peering at her as if from across a connect 자가주간 테러 위협 경고 시스템 (ID-1875)." | | 12 | "\"Wrong town, wrong name.\" Morris leaned casually against a deteriorating pillar, her bearing unchanged by death – or something else." | | 13 | "Seeing Morris – or whoever this was – opened wounds she thought had finally scarred over." | | 14 | "But this – this changed everything." | | 15 | "Working with René – people who dealt in things supernatural." |
| |
| 92.76% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1328 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Short, curly dark hair" |
| | adverbCount | 55 | | adverbRatio | 0.04141566265060241 | | lyAdverbCount | 24 | | lyAdverbRatio | 0.018072289156626505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 12.58 | | std | 8.41 | | cv | 0.668 | | sampleLengths | | 0 | 12 | | 1 | 19 | | 2 | 6 | | 3 | 17 | | 4 | 7 | | 5 | 1 | | 6 | 1 | | 7 | 15 | | 8 | 7 | | 9 | 2 | | 10 | 9 | | 11 | 17 | | 12 | 6 | | 13 | 14 | | 14 | 5 | | 15 | 1 | | 16 | 2 | | 17 | 13 | | 18 | 6 | | 19 | 8 | | 20 | 11 | | 21 | 17 | | 22 | 8 | | 23 | 10 | | 24 | 13 | | 25 | 18 | | 26 | 18 | | 27 | 13 | | 28 | 23 | | 29 | 3 | | 30 | 2 | | 31 | 18 | | 32 | 11 | | 33 | 15 | | 34 | 15 | | 35 | 6 | | 36 | 15 | | 37 | 7 | | 38 | 30 | | 39 | 8 | | 40 | 13 | | 41 | 14 | | 42 | 3 | | 43 | 6 | | 44 | 15 | | 45 | 12 | | 46 | 7 | | 47 | 13 | | 48 | 13 | | 49 | 7 |
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| 70.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.45864661654135336 | | totalSentences | 133 | | uniqueOpeners | 61 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 106 | | matches | | 0 | "Her eyes scanned the alleyway" | | 1 | "she said into her radio," | | 2 | "Her team knew better than" | | 3 | "Her military precision kicked in," | | 4 | "She saw him at the" | | 5 | "He glared at her from" | | 6 | "he replied, his smooth Spanish" | | 7 | "His knuckles went white as" | | 8 | "She slid a photograph across" | | 9 | "She knew that sound." | | 10 | "They'd found something." | | 11 | "Her feet splashed through puddles," | | 12 | "He wore a smirk." | | 13 | "It was an invitation, a" | | 14 | "He raised a hand to" | | 15 | "she said to the officers," | | 16 | "she had demanded, her voice" | | 17 | "he replied, his dark-brown eyes" | | 18 | "She'd seen this face not" |
| | ratio | 0.179 | |
| 59.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 106 | | matches | | 0 | "The clear plastic evidence bag" | | 1 | "Quinn didn't reach for it" | | 2 | "Her eyes scanned the alleyway" | | 3 | "The skip bag had been" | | 4 | "A commuter temptationients on a" | | 5 | "Quinn's leather watch vibrated against" | | 6 | "Morris had always said she" | | 7 | "Morris would've spotted this detail" | | 8 | "The medallion – too clean" | | 9 | "she said into her radio," | | 10 | "The staticky response came back" | | 11 | "Her team knew better than" | | 12 | "Covent Garden bustled despite the" | | 13 | "The medallion's image stayed fixed" | | 14 | "A Saint Christopher medallion failed" | | 15 | "The path led to Soho," | | 16 | "Quinn forced herself to walk" | | 17 | "The distinctive green neon sign" | | 18 | "This was the fourth disappearance" | | 19 | "A calling card." |
| | ratio | 0.802 | |
| 47.17% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 106 | | matches | | 0 | "Even in the briefest moment," |
| | ratio | 0.009 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "A commuter temptationients on a wet Tuesday filed past as if nothing had been disturbed." | | 1 | "Elias Renwick's ID photo – another checkpoint supervisor who'd disappeared three nights ago." | | 2 | "But she saw what she was looking for – a side alley that wasn't visible from the main street, leading down to a boarded staircase." | | 3 | "Even in the briefest moment, Quinn could feel it – the thrumming arc of energy, the energy that hummed under Soho – was stronger here, amplifying as she stepped…" | | 4 | "She'd seen this face not once, but twice – in her office, at her doorstep, and now here, deep in the Veil Market, peering at her as if from across a connect 자가주…" |
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| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 4 | | matches | | 0 | "she said, her voice crisp and all business" | | 1 | "she had, her voice low despite no one else around" | | 2 | "Quinn breathed, the word tasting strange on her tongue" | | 3 | "Morris leaned casually, her bearing unchanged by death – or something else" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 6 | | fancyTags | | 0 | "she pressed (press)" | | 1 | "Tomás whispered (whisper)" | | 2 | "she had (have)" | | 3 | "Quinn breathed (breathe)" | | 4 | "Tomás had (have)" | | 5 | "Quinn whispered (whisper)" |
| | dialogueSentences | 39 | | tagDensity | 0.359 | | leniency | 0.718 | | rawRatio | 0.429 | | effectiveRatio | 0.308 | |