| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said simply [simply]" |
| | dialogueSentences | 33 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.111 | | effectiveRatio | 0.061 | |
| 74.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1391 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "carefully" | | 1 | "slightly" | | 2 | "slowly" |
| |
| 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) | |
| 24.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1391 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "practiced ease" | | count | 1 |
| | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "marble" | | 1 | "fluttered" | | 2 | "standard" | | 3 | "silence" | | 4 | "tension" | | 5 | "scanning" | | 6 | "practiced ease" | | 7 | "stomach" | | 8 | "implication" | | 9 | "furrowing" | | 10 | "synthetic" | | 11 | "pulse" | | 12 | "quickened" | | 13 | "gloom" | | 14 | "jaw clenched" | | 15 | "eyebrow" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "clenched her fist" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 156 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 156 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 179 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 7 | | markdownWords | 11 | | totalWords | 1379 | | ratio | 0.008 | | matches | | 0 | "fixed" | | 1 | "actual" | | 2 | "warded" | | 3 | "time" | | 4 | "memories" | | 5 | "identities" | | 6 | "Come back when you’re ready." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1085 | | uniqueNames | 16 | | maxNameDensity | 2.21 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Harlow | 1 | | Quinn | 24 | | Vogel | 3 | | Soho | 2 | | Natasha | 13 | | Inspector | 1 | | Daniel | 1 | | Reeves | 13 | | Mark | 1 | | Lane | 1 | | Veil | 2 | | Market | 2 | | Eva | 3 | | Kowalski | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Vogel" | | 3 | "Natasha" | | 4 | "Inspector" | | 5 | "Daniel" | | 6 | "Reeves" | | 7 | "Mark" | | 8 | "Lane" | | 9 | "Eva" | | 10 | "Kowalski" | | 11 | "Morris" |
| | places | | | globalScore | 0.394 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 2.175 | | wordCount | 1379 | | matches | | 0 | "not empty, not lifeless, but *fixed*, as if locked onto something no one else could see" | | 1 | "not lifeless, but *fixed*, as if locked onto something no one else could see" | | 2 | "not just drugs and weapons changed hands, but *time*, *memories*, *identities*" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 179 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 19.7 | | std | 18.3 | | cv | 0.929 | | sampleLengths | | 0 | 101 | | 1 | 71 | | 2 | 67 | | 3 | 7 | | 4 | 25 | | 5 | 57 | | 6 | 16 | | 7 | 15 | | 8 | 33 | | 9 | 10 | | 10 | 37 | | 11 | 25 | | 12 | 34 | | 13 | 5 | | 14 | 1 | | 15 | 30 | | 16 | 40 | | 17 | 3 | | 18 | 5 | | 19 | 44 | | 20 | 11 | | 21 | 30 | | 22 | 31 | | 23 | 38 | | 24 | 5 | | 25 | 14 | | 26 | 14 | | 27 | 14 | | 28 | 30 | | 29 | 37 | | 30 | 3 | | 31 | 15 | | 32 | 30 | | 33 | 20 | | 34 | 19 | | 35 | 16 | | 36 | 18 | | 37 | 13 | | 38 | 44 | | 39 | 17 | | 40 | 28 | | 41 | 1 | | 42 | 4 | | 43 | 24 | | 44 | 11 | | 45 | 11 | | 46 | 7 | | 47 | 32 | | 48 | 11 | | 49 | 5 |
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| 87.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 156 | | matches | | 0 | "was supposed" | | 1 | "been dropped" | | 2 | "been logged" | | 3 | "been dropped" | | 4 | "were dilated" | | 5 | "been taken" | | 6 | "been killed" | | 7 | "been closed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 193 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 179 | | ratio | 0.05 | | matches | | 0 | "The Tube station’s old marble tiles were slick with condensation, the air thick with the scent of damp stone and something else—copper, maybe, or the ghost of old blood." | | 1 | "But there was something wrong with the way her eyes stared upward—not empty, not lifeless, but *fixed*, as if locked onto something no one else could see." | | 2 | "She knew that tone—dry, precise, the kind of politeness that edges into sarcasm without quite crossing the line." | | 3 | "Natasha’s belongings—phone, purse, keys—should have been logged, secured." | | 4 | "The lividity—gravity pooling in the lowest points—was fixed too soon." | | 5 | "The air from below smelled of rust and something older—wet earth, like an underground river." | | 6 | "And the fact that this station—Mark Lane—had been closed for over forty years." | | 7 | "It was a key to somewhere she’d only heard of in whispers—the Veil Market, a black market beneath the city where not just drugs and weapons changed hands, but *time*, *memories*, *identities*." | | 8 | "But Eva had been there during the last one—the case that had left Morris dead and Quinn haunted." |
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| 96.21% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1015 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.04433497536945813 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.015763546798029555 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 179 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 179 | | mean | 7.7 | | std | 6.95 | | cv | 0.902 | | sampleLengths | | 0 | 29 | | 1 | 18 | | 2 | 20 | | 3 | 15 | | 4 | 11 | | 5 | 8 | | 6 | 3 | | 7 | 10 | | 8 | 13 | | 9 | 11 | | 10 | 5 | | 11 | 29 | | 12 | 5 | | 13 | 18 | | 14 | 7 | | 15 | 3 | | 16 | 3 | | 17 | 4 | | 18 | 27 | | 19 | 7 | | 20 | 3 | | 21 | 4 | | 22 | 18 | | 23 | 9 | | 24 | 36 | | 25 | 5 | | 26 | 5 | | 27 | 2 | | 28 | 4 | | 29 | 12 | | 30 | 8 | | 31 | 7 | | 32 | 22 | | 33 | 2 | | 34 | 2 | | 35 | 5 | | 36 | 1 | | 37 | 1 | | 38 | 5 | | 39 | 5 | | 40 | 7 | | 41 | 30 | | 42 | 2 | | 43 | 2 | | 44 | 6 | | 45 | 5 | | 46 | 4 | | 47 | 6 | | 48 | 19 | | 49 | 6 |
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| 46.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3240223463687151 | | totalSentences | 179 | | uniqueOpeners | 58 | |
| 78.13% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 128 | | matches | | 0 | "Then she lifted Natasha’s left" | | 1 | "Just smooth, unmarked skin." | | 2 | "Then a voice crackled back." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 128 | | matches | | 0 | "Her leather watch, worn smooth" | | 1 | "She’d vanished in a place" | | 2 | "She crouched beside the body." | | 3 | "Her skin was pale, lips" | | 4 | "She didn’t need to." | | 5 | "She knew that tone—dry, precise," | | 6 | "She rose slowly, brushing dust" | | 7 | "she said simply" | | 8 | "She ignored the jab and" | | 9 | "She reached into her coat" | | 10 | "She leaned closer, studying Natasha’s" | | 11 | "He didn’t have to." | | 12 | "She’d been killed where she" | | 13 | "She reached for her penlight" | | 14 | "He didn’t need to." | | 15 | "They both knew what this" | | 16 | "It was metal." | | 17 | "She moved toward it, stepping" | | 18 | "She knelt and pulled the" | | 19 | "She pulled out her phone," |
| | ratio | 0.25 | |
| 88.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 128 | | matches | | 0 | "The Tube station’s old marble" | | 1 | "Detective Harlow Quinn stepped carefully" | | 2 | "The abandoned platform stretched ahead" | | 3 | "Her leather watch, worn smooth" | | 4 | "Quinn’s jaw tightened." | | 5 | "This was supposed to be" | | 6 | "Natasha Vogel, 24, last seen" | | 7 | "She’d vanished in a place" | | 8 | "She crouched beside the body." | | 9 | "Natasha lay sprawled on her" | | 10 | "Her skin was pale, lips" | | 11 | "Quinn didn’t turn." | | 12 | "She didn’t need to." | | 13 | "She knew that tone—dry, precise," | | 14 | "She rose slowly, brushing dust" | | 15 | "Detective Inspector Daniel Reeves stood" | | 16 | "A tension in his posture." | | 17 | "she said simply" | | 18 | "Reeves exhaled through his nose," | | 19 | "She ignored the jab and" |
| | ratio | 0.742 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "But there was something wrong with the way her eyes stared upward—not empty, not lifeless, but *fixed*, as if locked onto something no one else could see." | | 1 | "She knew that tone—dry, precise, the kind of politeness that edges into sarcasm without quite crossing the line." | | 2 | "Near the far end of the platform, half-hidden in shadow, was a patch of floor that wasn’t tile." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "Reeves said, voice flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn murmured (murmur)" |
| | dialogueSentences | 33 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.111 | | effectiveRatio | 0.061 | |