| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 48 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0.067 | | effectiveRatio | 0.042 | |
| 77.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1548 | | totalAiIsmAdverbs | 7 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "slightly" | | 1 | "loosely" | | 2 | "carefully" | | 3 | "very" | | 4 | "lazily" | | 5 | "deliberately" |
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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) | |
| 67.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1548 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "chill" | | 1 | "glinting" | | 2 | "database" | | 3 | "scanning" | | 4 | "weight" | | 5 | "etched" | | 6 | "standard" | | 7 | "could feel" |
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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) | |
| 92.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1533 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.99% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 982 | | uniqueNames | 10 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 11 | | Miller | 9 | | Twenty-eight | 1 | | Players | 1 | | Please | 1 | | Victory | 1 | | Rolls | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Rolls" | | 4 | "Morris" |
| | places | | | globalScore | 0.94 | | windowScore | 1 | |
| 32.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 3 | | matches | | 0 | "as if embracing the darkness above" | | 1 | "looked like charcoal, though it smudged d" | | 2 | "curves that seemed to shift the longer she looked at it" |
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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 | 1533 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 27.87 | | std | 17.02 | | cv | 0.61 | | sampleLengths | | 0 | 22 | | 1 | 58 | | 2 | 52 | | 3 | 21 | | 4 | 65 | | 5 | 47 | | 6 | 3 | | 7 | 36 | | 8 | 5 | | 9 | 9 | | 10 | 31 | | 11 | 4 | | 12 | 44 | | 13 | 59 | | 14 | 41 | | 15 | 56 | | 16 | 5 | | 17 | 21 | | 18 | 33 | | 19 | 53 | | 20 | 5 | | 21 | 39 | | 22 | 28 | | 23 | 39 | | 24 | 20 | | 25 | 20 | | 26 | 27 | | 27 | 38 | | 28 | 39 | | 29 | 21 | | 30 | 15 | | 31 | 4 | | 32 | 25 | | 33 | 28 | | 34 | 40 | | 35 | 15 | | 36 | 47 | | 37 | 11 | | 38 | 14 | | 39 | 8 | | 40 | 3 | | 41 | 28 | | 42 | 16 | | 43 | 29 | | 44 | 3 | | 45 | 33 | | 46 | 23 | | 47 | 56 | | 48 | 7 | | 49 | 36 |
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| 97.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 85 | | matches | | 0 | "was tucked" | | 1 | "was etched" |
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| 7.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 173 | | matches | | 0 | "was auditioning" | | 1 | "was already moving" | | 2 | "was already moving" | | 3 | "was pointing" | | 4 | "was waiting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 118 | | ratio | 0.093 | | matches | | 0 | "The abandoned Tube station smelled of wet concrete and something else—something copper-sweet that Harlow Quinn had learned to associate with bad days." | | 1 | "She noted the faded advertisements on the walls—Players Please, a woman in Victory Rolls smiling down at them like a ghost from another era." | | 2 | "The forensic technicians parted as she approached, their faces wearing that particular expression she'd come to recognize—the one that said they'd seen something that didn't fit neatly into their reports." | | 3 | "The victim's clothes were damp but intact—a decent jacket, good boots." | | 4 | "\"His fencing operation dealt in televisions and laptops, Miller. Not exactly the kind of merchandise you need to meet about in an abandoned Tube station at—\" she checked her watch \"—two in the morning.\"" | | 5 | "Hand-drawn in what looked like charcoal, though it smudged differently—darker, denser." | | 6 | "And something else—something that prickled at the back of her neck, a sensation she'd learned to trust over three very long years." | | 7 | "Disturbed dust, in a pattern that suggested footprints—but not the boots of the forensic team." | | 8 | "The face was etched with markings she didn't immediately recognize—sigils of some kind, protective or otherwise." | | 9 | "The needle spun lazily, pointing toward—" | | 10 | "But she could feel it now—that same prickle at the base of her skull, the same sense that there were layers to this scene she wasn't yet equipped to see." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 690 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03333333333333333 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004347826086956522 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 12.99 | | std | 9.29 | | cv | 0.715 | | sampleLengths | | 0 | 22 | | 1 | 25 | | 2 | 12 | | 3 | 21 | | 4 | 13 | | 5 | 19 | | 6 | 20 | | 7 | 4 | | 8 | 13 | | 9 | 4 | | 10 | 19 | | 11 | 46 | | 12 | 11 | | 13 | 12 | | 14 | 24 | | 15 | 3 | | 16 | 17 | | 17 | 19 | | 18 | 5 | | 19 | 4 | | 20 | 5 | | 21 | 6 | | 22 | 22 | | 23 | 3 | | 24 | 4 | | 25 | 14 | | 26 | 30 | | 27 | 19 | | 28 | 12 | | 29 | 4 | | 30 | 5 | | 31 | 19 | | 32 | 8 | | 33 | 33 | | 34 | 8 | | 35 | 11 | | 36 | 10 | | 37 | 7 | | 38 | 20 | | 39 | 2 | | 40 | 1 | | 41 | 2 | | 42 | 6 | | 43 | 15 | | 44 | 33 | | 45 | 19 | | 46 | 34 | | 47 | 5 | | 48 | 3 | | 49 | 14 |
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| 66.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4322033898305085 | | totalSentences | 118 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 93.68% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 76 | | matches | | 0 | "She stepped over the police" | | 1 | "He gestured toward the eastern" | | 2 | "She noted the faded advertisements" | | 3 | "She stepped down onto the" | | 4 | "His hands were clean, the" | | 5 | "She noticed something glinting at" | | 6 | "she checked her watch \"—two" | | 7 | "She didn't respond." | | 8 | "Her attention had shifted to" | | 9 | "She leaned in, tilting her" | | 10 | "It was a slip of" | | 11 | "she said, straightening" | | 12 | "She turned away, scanning the" | | 13 | "She walked a slow circle" | | 14 | "She pointed toward a service" | | 15 | "He waved over one of" | | 16 | "She crouched again, tilting her" | | 17 | "She picked it up carefully," | | 18 | "She held up the compass" | | 19 | "She watched the needle as" |
| | ratio | 0.316 | |
| 32.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 76 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "She stepped over the police" | | 2 | "Uniforms milled about the platform," | | 3 | "Someone had brought in portable" | | 4 | "DC Miller materialized at her" | | 5 | "Quinn's sharp jaw tightened." | | 6 | "The skipper thought right, but" | | 7 | "He gestured toward the eastern" | | 8 | "Quinn was already moving, her" | | 9 | "Military precision in every step," | | 10 | "She noted the faded advertisements" | | 11 | "Miller hurried to keep pace" | | 12 | "Miller's pace faltered slightly." | | 13 | "Quinn stopped at the platform" | | 14 | "The tunnel beyond swallowed the" | | 15 | "She stepped down onto the" | | 16 | "The forensic technicians parted as" | | 17 | "The body lay on its" | | 18 | "Quinn crouched beside it, studying" | | 19 | "Something closer to surprise, perhaps." |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 45.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 6 | | matches | | 0 | "Quinn was already moving, her shoes clicking against the grimy tiles." | | 1 | "The forensic technicians parted as she approached, their faces wearing that particular expression she'd come to recognize—the one that said they'd seen somethin…" | | 2 | "The body lay on its back between the rusted rails, arms spread wide as if embracing the darkness above." | | 3 | "The design was angular, almost geometric, but with curves that seemed to shift the longer she looked at it." | | 4 | "And something else—something that prickled at the back of her neck, a sensation she'd learned to trust over three very long years." | | 5 | "She never talked about Morris, not to junior detectives who wouldn't understand the weight of losing a partner to something you couldn't explain, couldn't prove…" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "Miller had, his tone shifting to one of genuine interest" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 48 | | tagDensity | 0.083 | | leniency | 0.167 | | rawRatio | 0.25 | | effectiveRatio | 0.042 | |