| 86.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 3 | | adverbTags | | 0 | "she repeated flatly [flatly]" | | 1 | "Eva said quietly [quietly]" | | 2 | "Quinn said quickly [quickly]" |
| | dialogueSentences | 53 | | tagDensity | 0.34 | | leniency | 0.679 | | rawRatio | 0.167 | | effectiveRatio | 0.113 | |
| 91.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1809 | | totalAiIsmAdverbs | 3 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | | | 2 | |
| | highlights | | 0 | "deliberately" | | 1 | "really" | | 2 | "quickly" |
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| 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) | |
| 61.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1809 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "practiced ease" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | |
| | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "methodical" | | 3 | "perfect" | | 4 | "complex" | | 5 | "practiced ease" | | 6 | "etched" | | 7 | "standard" | | 8 | "weight" | | 9 | "flicker" | | 10 | "database" | | 11 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 106 | | matches | (empty) | |
| 88.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "happened to" | | 2 | "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 | 141 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1795 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 1 | | matches | | 0 | "First dead body, she guessed." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1184 | | uniqueNames | 9 | | maxNameDensity | 1.69 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 20 | | Camden | 1 | | Reeves | 11 | | Morris | 3 | | London | 1 | | Eva | 10 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Reeves" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.655 | | windowScore | 0.5 | |
| 5.07% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 4 | | matches | | 0 | "quite pain, wasn't quite surprise" | | 1 | "quite surprise" | | 2 | "quite agree on what they were seeing" | | 3 | "looked like she belonged in a university" | | 4 | "as if searching" |
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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 | 1795 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 141 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 28.05 | | std | 20.25 | | cv | 0.722 | | sampleLengths | | 0 | 30 | | 1 | 60 | | 2 | 15 | | 3 | 44 | | 4 | 39 | | 5 | 3 | | 6 | 26 | | 7 | 41 | | 8 | 49 | | 9 | 5 | | 10 | 14 | | 11 | 23 | | 12 | 23 | | 13 | 33 | | 14 | 4 | | 15 | 59 | | 16 | 7 | | 17 | 3 | | 18 | 26 | | 19 | 7 | | 20 | 42 | | 21 | 26 | | 22 | 8 | | 23 | 6 | | 24 | 41 | | 25 | 7 | | 26 | 4 | | 27 | 60 | | 28 | 41 | | 29 | 43 | | 30 | 11 | | 31 | 71 | | 32 | 2 | | 33 | 49 | | 34 | 8 | | 35 | 51 | | 36 | 7 | | 37 | 46 | | 38 | 6 | | 39 | 56 | | 40 | 35 | | 41 | 15 | | 42 | 1 | | 43 | 72 | | 44 | 67 | | 45 | 14 | | 46 | 58 | | 47 | 30 | | 48 | 6 | | 49 | 34 |
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| 85.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 106 | | matches | | 0 | "been finished" | | 1 | "were manicured" | | 2 | "was frozen" | | 3 | "been removed" | | 4 | "been swept" | | 5 | "been drawn" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 214 | | matches | | 0 | "was looking" | | 1 | "were seeing" | | 2 | "was spinning" | | 3 | "wasn't looking" | | 4 | "was taking" | | 5 | "was consulting" | | 6 | "was going" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 141 | | ratio | 0.071 | | matches | | 0 | "The abandoned Tube station smelled of wet concrete and something else—something copper-sharp that Harlow Quinn had learned to associate with the worst days of her eighteen years on the force." | | 1 | "Graffiti bloomed across the walls in layers—tags, political slogans, elaborate murals that had never been finished." | | 2 | "The victim wore expensive clothes—tailored trousers, a cashmere coat that had seen better days but spoke of money." | | 3 | "She'd learned that Reeves saw what he expected to see—a dead addict in a forgotten corner of London, unfortunate but hardly surprising." | | 4 | "The tunnel curved, and she found what she was looking for—a service door, partially hidden behind a collapsed maintenance cart." | | 5 | "She found the first chalk mark on the wall—a symbol she didn't recognize, drawn in white with careful, deliberate strokes." | | 6 | "In the center of the circle, something had been drawn—a complex pattern of lines and curves that hurt to look at directly, as if her eyes couldn't quite agree on what they were seeing." | | 7 | "\"Eva Kowalski. I work with the British Museum's archives division. I was asked to consult.\" The woman stepped closer, tucking a strand of hair behind her left ear—a nervous habit she tried to hide with a confident tilt of her chin." | | 8 | "Quinn retrieved the object with a gloved hand—a compass, its casing green with age, its face etched with markings that looked nothing like cardinal directions." | | 9 | "But she'd also spent three years haunted by questions that had no answers—questions about what had really happened to DS Morris in that warehouse, about the thing she'd glimpsed in the shadows before the explosion, about the cold certainty that what she'd seen wasn't human." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1200 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.025 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.01 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 12.73 | | std | 10.31 | | cv | 0.81 | | sampleLengths | | 0 | 30 | | 1 | 20 | | 2 | 25 | | 3 | 5 | | 4 | 10 | | 5 | 15 | | 6 | 19 | | 7 | 16 | | 8 | 9 | | 9 | 16 | | 10 | 17 | | 11 | 6 | | 12 | 3 | | 13 | 22 | | 14 | 4 | | 15 | 32 | | 16 | 9 | | 17 | 13 | | 18 | 18 | | 19 | 5 | | 20 | 13 | | 21 | 5 | | 22 | 14 | | 23 | 23 | | 24 | 2 | | 25 | 21 | | 26 | 3 | | 27 | 22 | | 28 | 3 | | 29 | 5 | | 30 | 4 | | 31 | 16 | | 32 | 5 | | 33 | 2 | | 34 | 16 | | 35 | 11 | | 36 | 4 | | 37 | 5 | | 38 | 4 | | 39 | 3 | | 40 | 2 | | 41 | 1 | | 42 | 26 | | 43 | 7 | | 44 | 3 | | 45 | 7 | | 46 | 13 | | 47 | 19 | | 48 | 4 | | 49 | 1 |
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| 67.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4326241134751773 | | totalSentences | 141 | | uniqueOpeners | 61 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 92 | | matches | | 0 | "She ducked beneath the yellow" | | 1 | "He'd learn to eat breakfast" | | 2 | "She found Reeves crouched beside" | | 3 | "He glanced up at her" | | 4 | "His shoes were leather, hand-stitched." | | 5 | "she repeated flatly" | | 6 | "She'd learned that Reeves saw" | | 7 | "She stood and walked a" | | 8 | "She crouched again, studying the" | | 9 | "His expression was frozen in" | | 10 | "His eyes were open, staring" | | 11 | "She checked his pockets." | | 12 | "She left Reeves muttering about" | | 13 | "She found the first chalk" | | 14 | "They led deeper into the" | | 15 | "She looked like she belonged" | | 16 | "She didn't touch anything, but" | | 17 | "She pointed to a spot" | | 18 | "She'd built her career on" | | 19 | "She stood, brushing dust from" |
| | ratio | 0.25 | |
| 46.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 92 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "She ducked beneath the yellow" | | 2 | "The transit police had cordoned" | | 3 | "He'd learn to eat breakfast" | | 4 | "Quinn nodded and descended the" | | 5 | "Graffiti bloomed across the walls" | | 6 | "Water damage crawled up the" | | 7 | "She found Reeves crouched beside" | | 8 | "He glanced up at her" | | 9 | "Quinn settled onto her heels" | | 10 | "Reeves gestured to the body" | | 11 | "Quinn leaned closer, her brown" | | 12 | "The victim wore expensive clothes—tailored" | | 13 | "His shoes were leather, hand-stitched." | | 14 | "she repeated flatly" | | 15 | "Quinn didn't argue." | | 16 | "She'd learned that Reeves saw" | | 17 | "Another statistic for the pile." | | 18 | "She stood and walked a" | | 19 | "The victim's hands were clean." |
| | ratio | 0.826 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 92 | | matches | | 0 | "If he'd been stumbling through" | | 1 | "Because the truth was too" |
| | ratio | 0.022 | |
| 21.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 8 | | matches | | 0 | "Quinn nodded and descended the stairs, her footsteps echoing against tiles that hadn't seen regular passengers since the sixties." | | 1 | "At fifty-three, he had the kind of face that had given up pretending to care about appearances." | | 2 | "His eyes were open, staring at the arched ceiling as if he'd seen something there in his final moments." | | 3 | "She left Reeves muttering about overcomplicating things and followed the platform south, her flashlight cutting through the darkness." | | 4 | "Quinn retrieved the object with a gloved hand—a compass, its casing green with age, its face etched with markings that looked nothing like cardinal directions." | | 5 | "The needle was spinning, slow and deliberate, as if searching." | | 6 | "But she'd also spent three years haunted by questions that had no answers—questions about what had really happened to DS Morris in that warehouse, about the thi…" | | 7 | "She turned to meet Reeves at the threshold, leaving Eva Kowalski standing in the candlelit chamber with her satchel full of books and her knowledge of things th…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she repeated flatly (repeat)" | | 1 | "Eva continued (continue)" |
| | dialogueSentences | 53 | | tagDensity | 0.132 | | leniency | 0.264 | | rawRatio | 0.286 | | effectiveRatio | 0.075 | |