| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 20 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.125 | | effectiveRatio | 0.1 | |
| 93.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1539 | | totalAiIsmAdverbs | 2 | | 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) | |
| 77.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1539 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "pulse" | | 1 | "footsteps" | | 2 | "unsettled" | | 3 | "stomach" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 85 | | matches | (empty) | |
| 42.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | | 0 | "managed to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 89 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1561 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1340 | | uniqueNames | 21 | | maxNameDensity | 0.97 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 13 | | Vauxhall | 1 | | Raven | 1 | | Nest | 1 | | Portuguese | 1 | | Herrera | 6 | | Whitechapel | 1 | | Soho | 2 | | Peckham | 1 | | Camden | 2 | | Bermondsey | 2 | | Morris | 2 | | Underground | 1 | | Tube | 2 | | London | 2 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Shook | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | 0 | "Vauxhall" | | 1 | "Portuguese" | | 2 | "Whitechapel" | | 3 | "Soho" | | 4 | "Peckham" | | 5 | "Camden" | | 6 | "Bermondsey" | | 7 | "London" | | 8 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 2 | | matches | | 0 | "quite categorize and had, in Quinn's experience, gone thoroughly underground" | | 1 | "looked like when it was being faked" |
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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 | 1561 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 39.03 | | std | 35.71 | | cv | 0.915 | | sampleLengths | | 0 | 38 | | 1 | 86 | | 2 | 7 | | 3 | 95 | | 4 | 11 | | 5 | 21 | | 6 | 96 | | 7 | 80 | | 8 | 39 | | 9 | 11 | | 10 | 19 | | 11 | 2 | | 12 | 92 | | 13 | 17 | | 14 | 55 | | 15 | 10 | | 16 | 76 | | 17 | 3 | | 18 | 55 | | 19 | 7 | | 20 | 82 | | 21 | 34 | | 22 | 3 | | 23 | 99 | | 24 | 24 | | 25 | 11 | | 26 | 109 | | 27 | 6 | | 28 | 1 | | 29 | 6 | | 30 | 103 | | 31 | 50 | | 32 | 9 | | 33 | 51 | | 34 | 90 | | 35 | 17 | | 36 | 11 | | 37 | 7 | | 38 | 15 | | 39 | 13 |
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| 84.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 85 | | matches | | 0 | "been caught" | | 1 | "being faked" | | 2 | "been taught" | | 3 | "been shown" | | 4 | "been rebuilt " |
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| 9.52% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 210 | | matches | | 0 | "was going" | | 1 | "wasn't heading" | | 2 | "was already looking" | | 3 | "was pulling" | | 4 | "wasn't pleading" | | 5 | "was warning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 96 | | ratio | 0.135 | | matches | | 0 | "She watched the green neon sign across the street buzz and gutter — The Raven's Nest, the letters flickering like something half-dead." | | 1 | "Former paramedic, former NHS, former holder of a license to practice medicine — right up until he'd been caught administering treatments the regulatory board couldn't quite categorize and had, in Quinn's experience, gone thoroughly underground." | | 2 | "The rain worked for her — it made everyone hurry, made everyone hunched and anonymous, made her just another salt-and-pepper silhouette in a city that didn't look up." | | 3 | "He ran the way frightened people run, all out and ugly, and Quinn ran the way she'd been taught to run — short strides, arms driving, breath held low in the belly." | | 4 | "He ran better than he should have — better than a medic should have — and he was pulling away, and Quinn felt the old black frustration rise in her, the same frustration she'd felt three years ago in a stairwell in Bermondsey with Morris two steps behind her and then —" | | 5 | "Beyond it, the mouth of an abandoned Underground station gaped in the side of a low embankment — a Tube station that wasn't on any map she'd ever been shown, its old tiled façade smeared with decades of soot, its boards across the entrance pried loose and leaning." | | 6 | "Within ten steps the rain was a muffled drum overhead, and the air changed — cool, mineral, tasting of rust and old tile and something else underneath, something sweetish and herbal she couldn't name, like a shop that sold things no shop should sell." | | 7 | "\"Herrera!\" Her voice came back at her wrong — flattened, doubled, as if the tunnel had its own opinions about her being there." | | 8 | "Ahead, at the end of the platform, a section of the wall had been rebuilt — new brick in an old wall, and set into it an iron door with no handle." | | 9 | "Herrera stood in front of it, soaked and gasping, one hand pressed flat against the metal, and as her light found him she saw the medallion at his throat flash — Saint Christopher, patron of travelers, bouncing against his chest with every ragged breath." | | 10 | "He looked at her for a long moment, rain dripping off his curls, and something in his face shifted — not defiance." | | 11 | "Then he turned to the iron door, and from inside his jacket he took out a token — small, yellowish, and as her flashlight caught it her stomach turned over slowly, because it was a disc of carved bone — and pressed it into a slot she hadn't seen." | | 12 | "Noise — a low human murmur, a hundred distant voices, the smell of smoke and metal and those same sweet herbs, a wash of warm light from somewhere deep below spilling up the stairs beyond like the glow of a forge." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1333 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Former paramedic, former NHS," |
| | adverbCount | 37 | | adverbRatio | 0.0277569392348087 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008252063015753939 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 16.26 | | std | 15.97 | | cv | 0.982 | | sampleLengths | | 0 | 38 | | 1 | 22 | | 2 | 31 | | 3 | 33 | | 4 | 7 | | 5 | 30 | | 6 | 2 | | 7 | 35 | | 8 | 10 | | 9 | 18 | | 10 | 11 | | 11 | 6 | | 12 | 15 | | 13 | 3 | | 14 | 4 | | 15 | 42 | | 16 | 19 | | 17 | 28 | | 18 | 5 | | 19 | 24 | | 20 | 5 | | 21 | 6 | | 22 | 40 | | 23 | 6 | | 24 | 33 | | 25 | 6 | | 26 | 5 | | 27 | 11 | | 28 | 8 | | 29 | 2 | | 30 | 32 | | 31 | 4 | | 32 | 8 | | 33 | 34 | | 34 | 14 | | 35 | 3 | | 36 | 14 | | 37 | 3 | | 38 | 52 | | 39 | 6 | | 40 | 3 | | 41 | 1 | | 42 | 28 | | 43 | 48 | | 44 | 3 | | 45 | 14 | | 46 | 6 | | 47 | 6 | | 48 | 29 | | 49 | 7 |
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| 62.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.40625 | | totalSentences | 96 | | uniqueOpeners | 39 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 78 | | matches | | 0 | "Then she left the car" | | 1 | "Somewhere ahead, footsteps." | | 2 | "Then he turned to the" | | 3 | "Then she holstered her weapon," |
| | ratio | 0.051 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 78 | | matches | | 0 | "She watched the green neon" | | 1 | "She'd been building a file" | | 2 | "He was good." | | 3 | "She'd give him that." | | 4 | "He didn't check over his" | | 5 | "She kept the far side" | | 6 | "They crossed out of Soho." | | 7 | "He cut east, then north" | | 8 | "His flat was in Peckham." | | 9 | "She'd sat outside it eleven" | | 10 | "He got off near the" | | 11 | "She got off four stops" | | 12 | "He saw her at fifty" | | 13 | "She saw him see her." | | 14 | "Her voice cracked across the" | | 15 | "He ran the way frightened" | | 16 | "He vaulted a railing." | | 17 | "She went around it and" | | 18 | "He tore down a service" | | 19 | "He didn't answer." |
| | ratio | 0.5 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 78 | | matches | | 0 | "The rain had been falling" | | 1 | "She watched the green neon" | | 2 | "Soho poured itself around the" | | 3 | "Quinn sat with the engine" | | 4 | "The man came out at" | | 5 | "Olive skin, dark curls flattened" | | 6 | "She'd been building a file" | | 7 | "Herrera turned up the collar" | | 8 | "Quinn gave him half a" | | 9 | "He was good." | | 10 | "She'd give him that." | | 11 | "He didn't check over his" | | 12 | "She kept the far side" | | 13 | "The rain worked for her" | | 14 | "They crossed out of Soho." | | 15 | "He cut east, then north" | | 16 | "His flat was in Peckham." | | 17 | "She'd sat outside it eleven" | | 18 | "This was a different vector" | | 19 | "He got off near the" |
| | ratio | 0.846 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 9 | | matches | | 0 | "The rain had been falling for three hours and Detective Harlow Quinn had been sitting in an unmarked Vauxhall for four, and the combination had put a patience i…" | | 1 | "Soho poured itself around the bar in the usual ways: umbrellas bobbing, taxis hissing through standing water, a drunk singing something in Portuguese that might…" | | 2 | "Olive skin, dark curls flattened by the rain, moving with the quick economical stride of somebody who knew exactly where he was going and didn't want anybody to…" | | 3 | "Off-the-books sutures, a gunshot wound treated in a bedsit, a chemist in Whitechapel who'd stopped returning her calls." | | 4 | "He didn't check over his shoulder more than a man legitimately checking for traffic, and he changed his pace at corners in a way that looked natural unless you'…" | | 5 | "The rain worked for her — it made everyone hurry, made everyone hunched and anonymous, made her just another salt-and-pepper silhouette in a city that didn't lo…" | | 6 | "Beyond it, the mouth of an abandoned Underground station gaped in the side of a low embankment — a Tube station that wasn't on any map she'd ever been shown, it…" | | 7 | "Within ten steps the rain was a muffled drum overhead, and the air changed — cool, mineral, tasting of rust and old tile and something else underneath, somethin…" | | 8 | "An actual market, down there, under Camden, in a dead Tube station, and Quinn stood on the platform with her gun up and her heart going and the whole shape of h…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 20 | | tagDensity | 0.15 | | leniency | 0.3 | | rawRatio | 0.333 | | effectiveRatio | 0.1 | |