| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1391 | | 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) | |
| 85.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1391 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "footsteps" | | 1 | "familiar" | | 2 | "electric" | | 3 | "weight" |
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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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1406 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1259 | | uniqueNames | 25 | | maxNameDensity | 0.48 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Street" | | discoveredNames | | Soho | 1 | | Herrera | 5 | | Silas | 1 | | Wardour | 2 | | Street | 3 | | Cross | 1 | | Road | 3 | | Giles | 1 | | Euston | 1 | | Camden | 3 | | High | 2 | | Tomás | 2 | | Stables | 1 | | Lock | 1 | | Army | 1 | | Farm | 1 | | London | 1 | | Light | 1 | | Underground | 1 | | Saint | 1 | | Christopher | 1 | | Fifteen | 1 | | Morris | 1 | | Quinn | 6 | | Three | 3 |
| | persons | | 0 | "Herrera" | | 1 | "Silas" | | 2 | "Tomás" | | 3 | "Army" | | 4 | "Light" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Cross" | | 4 | "Road" | | 5 | "Giles" | | 6 | "Euston" | | 7 | "Camden" | | 8 | "High" | | 9 | "Farm" | | 10 | "London" | | 11 | "Three" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 57.75% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.422 | | wordCount | 1406 | | matches | | 0 | "not like a criminal, not wild, but like a man late for something" | | 1 | "not wild, but like a man late for something" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 89 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 37 | | std | 26.56 | | cv | 0.718 | | sampleLengths | | 0 | 66 | | 1 | 13 | | 2 | 5 | | 3 | 80 | | 4 | 9 | | 5 | 73 | | 6 | 15 | | 7 | 5 | | 8 | 79 | | 9 | 9 | | 10 | 29 | | 11 | 28 | | 12 | 18 | | 13 | 11 | | 14 | 95 | | 15 | 58 | | 16 | 61 | | 17 | 54 | | 18 | 60 | | 19 | 6 | | 20 | 69 | | 21 | 43 | | 22 | 32 | | 23 | 49 | | 24 | 8 | | 25 | 10 | | 26 | 31 | | 27 | 49 | | 28 | 58 | | 29 | 43 | | 30 | 10 | | 31 | 6 | | 32 | 68 | | 33 | 52 | | 34 | 27 | | 35 | 64 | | 36 | 3 | | 37 | 10 |
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| 88.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 83 | | matches | | 0 | "been taught" | | 1 | "been issued" | | 2 | "was gone" | | 3 | "been answered" |
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| 97.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 196 | | matches | | 0 | "was still pulling" | | 1 | "was running" | | 2 | "was standing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 89 | | ratio | 0.135 | | matches | | 0 | "Quinn had been watching it for three weeks, that sign — THE RAVEN'S NEST, humming above a door on a Soho street that most people walked past without slowing — and in three weeks she had learned exactly one useful thing: nobody who went in ever came out in a hurry." | | 1 | "Through the bar's front window she'd watched him do what he always did — sit with his back to the maps and black-and-white photographs covering Silas's walls, drink nothing, talk to no one, leave." | | 2 | "He walked like a man who had been taught to walk — paramedic, she knew, before the NHS threw him out for treating the wrong patients — and he used every shop window between the bar and Wardour Street as a mirror." | | 3 | "He ran the way she'd suspected he would — not like a criminal, not wild, but like a man late for something that mattered, cutting between the market's chained gates and through a gap in the hoarding that she would never have found in daylight." | | 4 | "Forty-one years old and she still ran the way the Army had taught her, short strides, shoulders down, breath metered — and he was still pulling away, younger, faster, lighter." | | 5 | "Then the arches — the great black brick viaduct that carried the railway over Camden, arch after arch of shuttered steel and darkness — and Herrera cut left between two of them, and for four seconds she lost him completely." | | 6 | "Three weeks of sitting outside that bar, and she'd lost the first live thread she'd touched since—" | | 7 | "In his raised fist, something pale caught the light — a chip of carved bone, thumbnail-sized, which he pressed into a brass slot worn smooth by ten thousand hands before his." | | 8 | "He looked at her the way paramedics looked at casualties they'd already assessed — sorry, and certain." | | 9 | "Something moved behind his eyes — almost a laugh, quickly buried." | | 10 | "\"Then hear me on this.\" He glanced at the door, and when he looked back his voice had changed — flat, and quick, and afraid in a way she didn't think was for himself." | | 11 | "Far below, she could hear it now — a murmur like a market waking, a bell, music from something with strings — and behind her the door met its frame with a soft, deliberate click." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1250 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0048 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 15.8 | | std | 12.86 | | cv | 0.814 | | sampleLengths | | 0 | 15 | | 1 | 51 | | 2 | 13 | | 3 | 5 | | 4 | 7 | | 5 | 18 | | 6 | 6 | | 7 | 34 | | 8 | 12 | | 9 | 3 | | 10 | 9 | | 11 | 42 | | 12 | 19 | | 13 | 4 | | 14 | 8 | | 15 | 15 | | 16 | 5 | | 17 | 31 | | 18 | 3 | | 19 | 2 | | 20 | 43 | | 21 | 9 | | 22 | 7 | | 23 | 22 | | 24 | 28 | | 25 | 18 | | 26 | 8 | | 27 | 3 | | 28 | 3 | | 29 | 45 | | 30 | 22 | | 31 | 25 | | 32 | 4 | | 33 | 26 | | 34 | 28 | | 35 | 2 | | 36 | 8 | | 37 | 30 | | 38 | 5 | | 39 | 3 | | 40 | 13 | | 41 | 3 | | 42 | 11 | | 43 | 40 | | 44 | 19 | | 45 | 3 | | 46 | 18 | | 47 | 3 | | 48 | 17 | | 49 | 6 |
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| 75.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4943820224719101 | | totalSentences | 89 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 75 | | matches | | 0 | "Only tonight he hadn't lingered" | | 1 | "Then he was down the" | | 2 | "Then the arches — the" |
| | ratio | 0.04 | |
| 65.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 75 | | matches | | 0 | "She sat up in the" | | 1 | "He'd been inside for forty" | | 2 | "He walked like a man" | | 3 | "She hung back, let a" | | 4 | "She could tail a man" | | 5 | "She ran for her car." | | 6 | "He was out of the" | | 7 | "Her voice cracked over the" | | 8 | "He didn't stop." | | 9 | "He ran the way she'd" | | 10 | "His footsteps drummed ahead of" | | 11 | "He vaulted a barrier." | | 12 | "His sleeve rode up, and" | | 13 | "Her lungs burned in the" | | 14 | "He didn't look back now." | | 15 | "He'd stopped checking." | | 16 | "She pulled up at the" | | 17 | "She'd lost him." | | 18 | "It spilled from a black" | | 19 | "She was standing beneath the" |
| | ratio | 0.387 | |
| 93.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 75 | | matches | | 0 | "The green neon bled into" | | 1 | "Quinn had been watching it" | | 2 | "The medic came out fast." | | 3 | "She sat up in the" | | 4 | "Tomás Herrera, twenty-nine, olive-skinned, short" | | 5 | "He'd been inside for forty" | | 6 | "Tonight he walked." | | 7 | "Quinn gave him a count" | | 8 | "He walked like a man" | | 9 | "She hung back, let a" | | 10 | "She could tail a man" | | 11 | "She ran for her car." | | 12 | "The chase went north in" | | 13 | "The cab ran the amber" | | 14 | "The cab slowed at the" | | 15 | "He was out of the" | | 16 | "Quinn left her car in" | | 17 | "Her voice cracked over the" | | 18 | "He didn't stop." | | 19 | "He ran the way she'd" |
| | ratio | 0.733 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 7 | | matches | | 0 | "Quinn had been watching it for three weeks, that sign — THE RAVEN'S NEST, humming above a door on a Soho street that most people walked past without slowing — a…" | | 1 | "He walked like a man who had been taught to walk — paramedic, she knew, before the NHS threw him out for treating the wrong patients — and he used every shop wi…" | | 2 | "He ran the way she'd suspected he would — not like a criminal, not wild, but like a man late for something that mattered, cutting between the market's chained g…" | | 3 | "Beyond it, cobbled horse tunnels ran beneath the Stables, low brick throats that funneled the rain into silver ropes off their arches." | | 4 | "Then the arches — the great black brick viaduct that carried the railway over Camden, arch after arch of shuttered steel and darkness — and Herrera cut left bet…" | | 5 | "The door behind him sagged open on complaining hinges, exhaling warm air that smelled of candle tallow and wet stone and, beneath both, something faint and swee…" | | 6 | "Three years of files with holes in them, of a partner's death written up as cardiac arrest by a coroner who hadn't seen the room, of doors in this city that ope…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |