| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.625 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 895 | | 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) | |
| 72.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 895 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "measured" | | 1 | "glinting" | | 2 | "standard" | | 3 | "unraveling" | | 4 | "familiar" |
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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 | 60 | | matches | (empty) | |
| 23.81% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 60 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" | | 2 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 63 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 902 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 830 | | uniqueNames | 17 | | maxNameDensity | 1.69 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | Detective | 1 | | Quinn | 14 | | London | 2 | | Camden | 1 | | High | 1 | | Street | 1 | | Herrera | 9 | | Underground | 1 | | Morris | 3 | | Veil | 2 | | Market | 2 | | Transport | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Spanish | 1 |
| | persons | | 0 | "Detective" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Spanish" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Raven" |
| | globalScore | 0.657 | | windowScore | 0.667 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 4 | | matches | | 0 | "humming that seemed to emanate from the tiles themselves" | | 1 | "looked like an ordinary bookshelf wedged" | | 2 | "something like pity in his expression" | | 3 | "quite people" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.217 | | wordCount | 902 | | matches | | 0 | "not the harsh fluorescents of Transport for London, but something softer, more organic" | | 1 | "not a maintenance tunnel, but a wide corridor" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 89.07% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 42.95 | | std | 19.83 | | cv | 0.462 | | sampleLengths | | 0 | 55 | | 1 | 59 | | 2 | 10 | | 3 | 48 | | 4 | 73 | | 5 | 50 | | 6 | 56 | | 7 | 57 | | 8 | 56 | | 9 | 6 | | 10 | 51 | | 11 | 64 | | 12 | 62 | | 13 | 30 | | 14 | 8 | | 15 | 59 | | 16 | 22 | | 17 | 26 | | 18 | 51 | | 19 | 40 | | 20 | 19 |
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| 87.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 60 | | matches | | 0 | "been closed" | | 1 | "were lined" | | 2 | "been answered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 149 | | matches | | 0 | "was swinging" | | 1 | "was unraveling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 63 | | ratio | 0.095 | | matches | | 0 | "Her quarry was quick – younger, probably fitter – but Quinn had been chasing suspects through London's streets for nearly two decades." | | 1 | "Her worn leather watch caught the streetlight as she checked the time – 23:47." | | 2 | "But there was light coming from somewhere deeper in the station – not the harsh fluorescents of Transport for London, but something softer, more organic." | | 3 | "A movement caught her eye – Herrera, ducking through a maintenance door." | | 4 | "This was the missing piece – the thread that could finally explain what had happened to Morris." | | 5 | "Quinn's training felt inadequate here – what good was standard procedure in a place that shouldn't exist?" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 825 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.03272727272727273 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012121212121212121 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 63 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 63 | | mean | 14.32 | | std | 6.43 | | cv | 0.449 | | sampleLengths | | 0 | 24 | | 1 | 22 | | 2 | 9 | | 3 | 11 | | 4 | 16 | | 5 | 17 | | 6 | 15 | | 7 | 2 | | 8 | 8 | | 9 | 17 | | 10 | 14 | | 11 | 17 | | 12 | 12 | | 13 | 16 | | 14 | 12 | | 15 | 15 | | 16 | 18 | | 17 | 12 | | 18 | 20 | | 19 | 18 | | 20 | 10 | | 21 | 25 | | 22 | 21 | | 23 | 12 | | 24 | 10 | | 25 | 22 | | 26 | 13 | | 27 | 16 | | 28 | 20 | | 29 | 20 | | 30 | 3 | | 31 | 3 | | 32 | 21 | | 33 | 12 | | 34 | 18 | | 35 | 13 | | 36 | 16 | | 37 | 17 | | 38 | 14 | | 39 | 4 | | 40 | 9 | | 41 | 17 | | 42 | 18 | | 43 | 18 | | 44 | 12 | | 45 | 10 | | 46 | 8 | | 47 | 4 | | 48 | 4 | | 49 | 10 |
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| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.42857142857142855 | | totalSentences | 63 | | uniqueOpeners | 27 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 59 | | matches | | 0 | "Instead of the expected keypad" | | 1 | "All connected to Herrera." |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 59 | | matches | | 0 | "Her quarry was quick –" | | 1 | "She knew these alleys, knew" | | 2 | "she shouted, more for the" | | 3 | "Her worn leather watch caught" | | 4 | "she muttered, drawing her weapon" | | 5 | "Her flashlight beam caught the" | | 6 | "She reached the door just" | | 7 | "It was real." | | 8 | "She pushed forward, trying to" | | 9 | "He'd been investigating a series" | | 10 | "It reminded her of the" | | 11 | "He glanced back, meeting Quinn's" | | 12 | "he said, his Spanish accent" | | 13 | "He stepped through the doorway" | | 14 | "She thought of Morris, of" | | 15 | "She thought of the way" |
| | ratio | 0.271 | |
| 36.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 59 | | matches | | 0 | "Her quarry was quick –" | | 1 | "She knew these alleys, knew" | | 2 | "The figure ahead darted left" | | 3 | "Quinn's hand instinctively moved to" | | 4 | "The passage opened onto Camden" | | 5 | "Quinn caught a glimpse of" | | 6 | "The unlicensed medic she'd been" | | 7 | "she shouted, more for the" | | 8 | "Her worn leather watch caught" | | 9 | "Something about that detail nagged" | | 10 | "Herrera veered suddenly, disappearing down" | | 11 | "Quinn pulled up short at" | | 12 | "The station had been closed" | | 13 | "The Veil Market, some called" | | 14 | "she muttered, drawing her weapon" | | 15 | "The rain grew distant as" | | 16 | "Her flashlight beam caught the" | | 17 | "The ticket hall was empty," | | 18 | "Quinn moved carefully, checking corners," | | 19 | "A movement caught her eye" |
| | ratio | 0.847 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 79.37% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 4 | | matches | | 0 | "Rain hammered against Detective Quinn's face as she sprinted down the slick pavement, her boots splashing through puddles that reflected the neon signs above." | | 1 | "The rain grew distant as she descended, replaced by an odd humming that seemed to emanate from the tiles themselves." | | 2 | "Beyond lay not a maintenance tunnel, but a wide corridor that should have been physically impossible given the station's layout." | | 3 | "Quinn's training felt inadequate here – what good was standard procedure in a place that shouldn't exist?" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she muttered (mutter)" |
| | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.667 | | effectiveRatio | 0.5 | |