| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1446 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "carefully" | | 1 | "sharply" | | 2 | "utterly" | | 3 | "slowly" |
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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) | |
| 44.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1446 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "constructed" | | 2 | "echoed" | | 3 | "pounding" | | 4 | "familiar" | | 5 | "weight" | | 6 | "silence" | | 7 | "shimmered" | | 8 | "velvet" | | 9 | "fleeting" | | 10 | "thundered" | | 11 | "stark" | | 12 | "scanning" | | 13 | "gloom" |
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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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 22 | | totalWords | 1439 | | ratio | 0.015 | | matches | | 0 | "This is where you turn back," | | 1 | "You call for backup. You perimeter the exit. You do it by the book." | | 2 | "No signal." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 78 | | wordCount | 1437 | | uniqueNames | 32 | | maxNameDensity | 1.39 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 2 | | Quinn | 20 | | Royal | 1 | | Military | 1 | | Police | 4 | | Tomás | 4 | | Herrera | 13 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | North | 1 | | London | 3 | | Morris | 4 | | High | 1 | | Street | 1 | | Northern | 1 | | Victorian | 1 | | Except | 1 | | Underground | 1 | | Blitz | 1 | | Transport | 1 | | Metropolitan | 2 | | Saint | 1 | | Christopher | 1 | | Chief | 1 | | Superintendent | 1 | | Glock | 1 | | Met | 1 | | Veil | 1 | | Market | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Raven" | | 6 | "Morris" | | 7 | "Except" | | 8 | "Underground" | | 9 | "Metropolitan" | | 10 | "Saint" | | 11 | "Christopher" | | 12 | "Market" |
| | places | | 0 | "Camden" | | 1 | "Royal" | | 2 | "Soho" | | 3 | "North" | | 4 | "London" | | 5 | "High" | | 6 | "Street" | | 7 | "Chief" |
| | globalScore | 0.804 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like ordinary street-dwellers, wra" |
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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 | 1439 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 41.11 | | std | 22.93 | | cv | 0.558 | | sampleLengths | | 0 | 98 | | 1 | 45 | | 2 | 40 | | 3 | 109 | | 4 | 49 | | 5 | 24 | | 6 | 11 | | 7 | 40 | | 8 | 51 | | 9 | 25 | | 10 | 45 | | 11 | 15 | | 12 | 24 | | 13 | 65 | | 14 | 15 | | 15 | 26 | | 16 | 68 | | 17 | 15 | | 18 | 41 | | 19 | 11 | | 20 | 41 | | 21 | 69 | | 22 | 49 | | 23 | 67 | | 24 | 46 | | 25 | 30 | | 26 | 42 | | 27 | 14 | | 28 | 23 | | 29 | 64 | | 30 | 16 | | 31 | 27 | | 32 | 44 | | 33 | 41 | | 34 | 49 |
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| 97.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 91 | | matches | | 0 | "were bunched" | | 1 | "been revoked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 220 | | matches | | 0 | "was clutching" | | 1 | "were coming" | | 2 | "was not leaving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 91 | | ratio | 0.055 | | matches | | 0 | "Even under the bulky wool, his posture was wrong for a man just out for air at midnight—his shoulders were bunched, his head darting left and right at every alleyway and shuttered storefront." | | 1 | "Quinn’s instinct—the same instinct that kept her alive while her partner, DS Morris, died in a blood-soaked alley three years ago—told her the official report was a carefully constructed lie." | | 2 | "The air rushing up from the subterranean stairwell hit Quinn’s face—it was warm, thick, and utterly wrong." | | 3 | "Dozens—hundreds—of figures shuffled along the disused tracks." | | 4 | "As he passed under a string of amber lanterns, his coat sleeve pulled back, revealing a pale scar running from his wrist to his elbow—and beside it, a fresh, angry puncture mark leaking dark, shimmering fluid." |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1459 | | adjectiveStacks | 3 | | stackExamples | | 0 | "strange, double-jointed gait." | | 1 | "massive, broad-shouldered figure" | | 2 | "thick, grey-skinned fingers," |
| | adverbCount | 23 | | adverbRatio | 0.015764222069910898 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0054832076764907475 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 15.81 | | std | 9.6 | | cv | 0.607 | | sampleLengths | | 0 | 31 | | 1 | 28 | | 2 | 39 | | 3 | 12 | | 4 | 33 | | 5 | 13 | | 6 | 22 | | 7 | 5 | | 8 | 34 | | 9 | 33 | | 10 | 12 | | 11 | 30 | | 12 | 22 | | 13 | 7 | | 14 | 20 | | 15 | 18 | | 16 | 6 | | 17 | 11 | | 18 | 3 | | 19 | 27 | | 20 | 10 | | 21 | 38 | | 22 | 13 | | 23 | 7 | | 24 | 18 | | 25 | 10 | | 26 | 17 | | 27 | 18 | | 28 | 15 | | 29 | 10 | | 30 | 4 | | 31 | 4 | | 32 | 6 | | 33 | 31 | | 34 | 12 | | 35 | 22 | | 36 | 15 | | 37 | 4 | | 38 | 2 | | 39 | 20 | | 40 | 24 | | 41 | 18 | | 42 | 26 | | 43 | 15 | | 44 | 14 | | 45 | 27 | | 46 | 4 | | 47 | 7 | | 48 | 14 | | 49 | 20 |
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| 47.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3516483516483517 | | totalSentences | 91 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 85.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 89 | | matches | | 0 | "She kept thirty yards of" | | 1 | "Her leather watch strap, soft" | | 2 | "She had tracked Herrera from" | | 3 | "Her salt-and-pepper hair, clipped close" | | 4 | "He didn't stop." | | 5 | "He sprinted toward the dead" | | 6 | "She drew her service weapon," | | 7 | "It smelled of ozone, crushed" | | 8 | "She paused at the top" | | 9 | "*You call for backup." | | 10 | "You perimeter the exit." | | 11 | "You do it by the" | | 12 | "She smoothed her thumb down" | | 13 | "Her eyes adjusted to a" | | 14 | "She had spent eighteen years" | | 15 | "He was clutching his left" | | 16 | "She looked down at her" | | 17 | "Her sharp brown eyes tracked" | | 18 | "He tucked it aside and" | | 19 | "He held it out to" |
| | ratio | 0.337 | |
| 55.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 89 | | matches | | 0 | "The rain in Camden did" | | 1 | "Detective Harlow Quinn wiped a" | | 2 | "She kept thirty yards of" | | 3 | "Quinn adjusted her grip on" | | 4 | "Her leather watch strap, soft" | | 5 | "Midnight was twenty minutes away." | | 6 | "She had tracked Herrera from" | | 7 | "The official report cited severe" | | 8 | "Quinn’s instinct—the same instinct that" | | 9 | "Herrera turned sharply onto a" | | 10 | "Quinn closed the distance, her" | | 11 | "Her salt-and-pepper hair, clipped close" | | 12 | "Herrera had broken into a" | | 13 | "Quinn shouted, her voice cutting" | | 14 | "He didn't stop." | | 15 | "He sprinted toward the dead" | | 16 | "Quinn gave chase, her heart" | | 17 | "Herrera reached the end of" | | 18 | "The door yielded with a" | | 19 | "Quinn reached the doorway three" |
| | ratio | 0.809 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 89 | | matches | | 0 | "Even under the bulky wool," | | 1 | "If she pulled her trigger" |
| | ratio | 0.022 | |
| 52.15% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 8 | | matches | | 0 | "Quinn’s instinct—the same instinct that kept her alive while her partner, DS Morris, died in a blood-soaked alley three years ago—told her the official report w…" | | 1 | "Herrera was her first solid link to the shadow world that had swallowed her partner." | | 2 | "But as Quinn descended three, four, five levels deep, the silence gave way to a low, rhythmic vibration that shuddered through the soles of her boots." | | 3 | "On low wooden tables lay polished bone tools, jars of luminescent bile, herbs that writhed slowly in dry glass, and leather-bound ledgers that shimmered with fa…" | | 4 | "A woman in a velvet coat turned her head, and for a fleeting second, Quinn saw eyes that reflected light like a cat’s, liquid black and irisless." | | 5 | "Herrera reached into his neck, pulling out a Saint Christopher medallion that caught the amber light." | | 6 | "But behind him, near a stack of wooden crates dripping with brackish water, hung a heavy velvet curtain that draped over an unlit maintenance alcove." | | 7 | "She smoothed down her coat, adjusted her collar, and stepped out from behind the signal box, assuming the precise, confident posture that had carried her throug…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn shouted, her voice cutting clean through the rain" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |