| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 1 | | adverbTags | | 0 | "the disciplinary board had charitably [charitably]" |
| | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1298 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 53.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1298 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "database" | | 1 | "quickened" | | 2 | "scanning" | | 3 | "weight" | | 4 | "pulse" | | 5 | "silence" | | 6 | "scanned" | | 7 | "velvet" | | 8 | "perfect" | | 9 | "could feel" | | 10 | "measured" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | 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 | 1311 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1306 | | uniqueNames | 23 | | maxNameDensity | 0.92 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 12 | | Old | 1 | | Compton | 1 | | Street | 2 | | Raven | 2 | | Nest | 2 | | Tomás | 1 | | Herrera | 12 | | Saint | 1 | | Christopher | 1 | | National | 1 | | Insurance | 1 | | Dean | 1 | | London | 1 | | Underground | 1 | | Tube | 1 | | Glock | 1 | | Barking | 1 | | Morris | 3 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "Dean" | | 5 | "London" | | 6 | "Barking" | | 7 | "Veil" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 42.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 3 | | matches | | 0 | "vial that seemed to shift colors of its own volition" | | 1 | "looked like a rat skeleton strung on a ne" | | 2 | "something like it once before, in the person" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1311 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 52.44 | | std | 30.6 | | cv | 0.583 | | sampleLengths | | 0 | 59 | | 1 | 54 | | 2 | 63 | | 3 | 32 | | 4 | 73 | | 5 | 107 | | 6 | 29 | | 7 | 73 | | 8 | 61 | | 9 | 40 | | 10 | 22 | | 11 | 4 | | 12 | 101 | | 13 | 93 | | 14 | 10 | | 15 | 88 | | 16 | 56 | | 17 | 61 | | 18 | 97 | | 19 | 58 | | 20 | 22 | | 21 | 34 | | 22 | 5 | | 23 | 63 | | 24 | 6 |
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| 83.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 98 | | matches | | 0 | "was concerned" | | 1 | "was tiled " | | 2 | "was obscured" | | 3 | "been logged" | | 4 | "was gone" | | 5 | "was made" |
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| 69.28% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 204 | | matches | | 0 | "was being" | | 1 | "was coming" | | 2 | "was doing" | | 3 | "was checking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 98 | | ratio | 0.092 | | matches | | 0 | "He was moving fast but not running — not yet." | | 1 | "Herrera looked over both shoulders, his warm brown eyes scanning the street with an alertness that confirmed what she already suspected — the man was trained." | | 2 | "The way he moved, the way he carried his weight on the balls of his feet, the subtle way he kept his scarred left forearm angled toward his body — this was someone who'd learned to protect himself long before he'd lost his license." | | 3 | "The steps descended farther than she expected — two flights, three — the sound of the city fading above her until the only noise was the drip of water and her own controlled breathing." | | 4 | "The floor was tiled — old London Underground tile, cream and maroon, cracked and water-stained." | | 5 | "A child — or something shaped like a child — wove through the crowd clutching what looked like a rat skeleton strung on a necklace." | | 6 | "The last time she'd felt this way — this particular flavor of wrong — had been three years ago." | | 7 | "Herrera produced something from his jacket pocket — small, pale, the size of a coin — and placed it on the counter." | | 8 | "She'd built a case from fragments — encrypted messages on a seized phone, financial records that didn't add up, the testimony of an informant who'd gone catatonic mid-sentence and hadn't spoken since." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1299 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.03002309468822171 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006158583525789068 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 13.38 | | std | 9.42 | | cv | 0.704 | | sampleLengths | | 0 | 17 | | 1 | 34 | | 2 | 8 | | 3 | 17 | | 4 | 10 | | 5 | 6 | | 6 | 21 | | 7 | 8 | | 8 | 27 | | 9 | 14 | | 10 | 14 | | 11 | 5 | | 12 | 5 | | 13 | 22 | | 14 | 22 | | 15 | 23 | | 16 | 6 | | 17 | 2 | | 18 | 20 | | 19 | 11 | | 20 | 3 | | 21 | 20 | | 22 | 26 | | 23 | 3 | | 24 | 44 | | 25 | 11 | | 26 | 4 | | 27 | 8 | | 28 | 6 | | 29 | 9 | | 30 | 34 | | 31 | 17 | | 32 | 4 | | 33 | 2 | | 34 | 2 | | 35 | 5 | | 36 | 19 | | 37 | 15 | | 38 | 27 | | 39 | 6 | | 40 | 10 | | 41 | 24 | | 42 | 3 | | 43 | 19 | | 44 | 4 | | 45 | 16 | | 46 | 14 | | 47 | 29 | | 48 | 25 | | 49 | 17 |
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| 40.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.30612244897959184 | | totalSentences | 98 | | uniqueOpeners | 30 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 92 | | matches | | 0 | "Then he stopped." | | 1 | "Just steps disappearing into darkness" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 92 | | matches | | 0 | "She had eyes for only" | | 1 | "He was moving fast but" | | 2 | "He thought he was being" | | 3 | "He thought the Saint Christopher" | | 4 | "She'd seen the hospital reports." | | 5 | "She quickened her pace, keeping" | | 6 | "He was checking for a" | | 7 | "He descended a set of" | | 8 | "She touched the wall to" | | 9 | "She slowed, pressing herself against" | | 10 | "She inched forward." | | 11 | "It was a market." | | 12 | "She didn't draw it." | | 13 | "Her instincts, honed by a" | | 14 | "She pushed the memory down." | | 15 | "It wasn't useful." | | 16 | "She scanned the crowd for" | | 17 | "He stopped at a booth" | | 18 | "She could feel it in" | | 19 | "She'd built a case from" |
| | ratio | 0.293 | |
| 63.26% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 92 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn pushed through" | | 2 | "She had eyes for only" | | 3 | "He was moving fast but" | | 4 | "He thought he was being" | | 5 | "He thought the Saint Christopher" | | 6 | "Quinn had been watching Herrera" | | 7 | "The patients he'd treated had" | | 8 | "She'd seen the hospital reports." | | 9 | "Whatever Herrera was doing and" | | 10 | "She quickened her pace, keeping" | | 11 | "Herrera turned left onto Dean" | | 12 | "He was checking for a" | | 13 | "Herrera passed a kebab shop," | | 14 | "Quinn froze against the brick" | | 15 | "Herrera looked over both shoulders," | | 16 | "The way he moved, the" | | 17 | "He descended a set of" | | 18 | "Quinn counted to five, then" | | 19 | "The stairwell smelled of wet" |
| | ratio | 0.793 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 92 | | matches | | 0 | "If she went in, she" | | 1 | "If she was made, she" |
| | ratio | 0.022 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 8 | | matches | | 0 | "She quickened her pace, keeping her distance, staying close enough to the clusters of pub-goers and club kids to blur her silhouette." | | 1 | "Herrera turned left onto Dean Street, then right, cutting a path that made no logical sense for someone trying to get anywhere specific." | | 2 | "Herrera looked over both shoulders, his warm brown eyes scanning the street with an alertness that confirmed what she already suspected — the man was trained." | | 3 | "The way he moved, the way he carried his weight on the balls of his feet, the subtle way he kept his scarred left forearm angled toward his body — this was some…" | | 4 | "She scanned the crowd for Herrera and spotted him thirty meters ahead, threading his way between stalls with the ease of someone who'd done this many times befo…" | | 5 | "She'd built a case from fragments — encrypted messages on a seized phone, financial records that didn't add up, the testimony of an informant who'd gone cataton…" | | 6 | "Sure in the way that lived in her gut, beneath the evidence, beneath procedure, in the place where grief had calcified into something harder and more useful." | | 7 | "The noise of the market shifted around her, conversations dipping and resuming like a wave passing through water." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |