| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 72.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1432 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "very" | | 1 | "slowly" | | 2 | "carefully" | | 3 | "gently" | | 4 | "lazily" | | 5 | "suddenly" | | 6 | "slightly" |
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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) | |
| 86.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1432 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "etched" | | 1 | "unwavering" | | 2 | "playfully" | | 3 | "flickered" |
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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 | 103 | | matches | (empty) | |
| 87.38% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 23 | | totalWords | 1433 | | ratio | 0.016 | | matches | | 0 | "Found something. The prefix you asked about. I think I should come down." | | 1 | "How did you know I'd ask you down." | | 2 | "You didn't." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1099 | | uniqueNames | 12 | | maxNameDensity | 1.09 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 2 | | Quinn | 12 | | London | 2 | | Camden | 1 | | Ferris | 2 | | Londoners | 1 | | Piccadilly | 1 | | Italian | 1 | | Eva | 12 | | Kowalski | 2 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Ferris" | | 2 | "Londoners" | | 3 | "Eva" | | 4 | "Kowalski" |
| | places | | | globalScore | 0.954 | | windowScore | 0.333 | |
| 20.69% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 3 | | matches | | 0 | "quite explain and didn't want to" | | 1 | "looked like black sand" | | 2 | "looked like a schoolteacher who had wande" |
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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 | 1433 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 29.24 | | std | 26.9 | | cv | 0.92 | | sampleLengths | | 0 | 14 | | 1 | 76 | | 2 | 69 | | 3 | 43 | | 4 | 4 | | 5 | 45 | | 6 | 9 | | 7 | 2 | | 8 | 70 | | 9 | 11 | | 10 | 14 | | 11 | 3 | | 12 | 47 | | 13 | 8 | | 14 | 91 | | 15 | 96 | | 16 | 32 | | 17 | 13 | | 18 | 11 | | 19 | 6 | | 20 | 6 | | 21 | 64 | | 22 | 8 | | 23 | 4 | | 24 | 51 | | 25 | 16 | | 26 | 48 | | 27 | 26 | | 28 | 5 | | 29 | 39 | | 30 | 5 | | 31 | 29 | | 32 | 24 | | 33 | 22 | | 34 | 8 | | 35 | 65 | | 36 | 90 | | 37 | 32 | | 38 | 17 | | 39 | 1 | | 40 | 68 | | 41 | 15 | | 42 | 14 | | 43 | 1 | | 44 | 12 | | 45 | 50 | | 46 | 36 | | 47 | 3 | | 48 | 10 |
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| 81.42% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 103 | | matches | | 0 | "been called" | | 1 | "been called" | | 2 | "been told" | | 3 | "was etched" | | 4 | "were stained" | | 5 | "been lit" | | 6 | "is told" |
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| 91.89% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 185 | | matches | | 0 | "was beginning" | | 1 | "was beginning" | | 2 | "was beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 120 | | ratio | 0.008 | | matches | | 0 | "A gold watch on the left wrist — wrong side, but then nothing here was right." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1102 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.032667876588021776 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.011796733212341199 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 11.94 | | std | 11.04 | | cv | 0.924 | | sampleLengths | | 0 | 6 | | 1 | 8 | | 2 | 41 | | 3 | 7 | | 4 | 4 | | 5 | 24 | | 6 | 27 | | 7 | 6 | | 8 | 13 | | 9 | 23 | | 10 | 32 | | 11 | 11 | | 12 | 4 | | 13 | 18 | | 14 | 27 | | 15 | 5 | | 16 | 4 | | 17 | 2 | | 18 | 12 | | 19 | 21 | | 20 | 4 | | 21 | 2 | | 22 | 3 | | 23 | 15 | | 24 | 13 | | 25 | 11 | | 26 | 14 | | 27 | 3 | | 28 | 32 | | 29 | 7 | | 30 | 2 | | 31 | 1 | | 32 | 5 | | 33 | 8 | | 34 | 2 | | 35 | 1 | | 36 | 17 | | 37 | 3 | | 38 | 2 | | 39 | 11 | | 40 | 14 | | 41 | 12 | | 42 | 10 | | 43 | 19 | | 44 | 14 | | 45 | 2 | | 46 | 2 | | 47 | 25 | | 48 | 16 | | 49 | 3 |
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| 44.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3416666666666667 | | totalSentences | 120 | | uniqueOpeners | 41 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 86 | | matches | | 0 | "Then she saw it." | | 1 | "Then she crouched, setting her" |
| | ratio | 0.023 | |
| 89.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 86 | | matches | | 0 | "Her worn leather watch read" | | 1 | "It wasn't listed in the" | | 2 | "He paused, glancing back toward" | | 3 | "It wasn't grout." | | 4 | "It was a darker substance," | | 5 | "She crouched, keeping her gloves" | | 6 | "She went further." | | 7 | "They were the color of" | | 8 | "They were the color of" | | 9 | "They were the color of" | | 10 | "Her phone buzzed." | | 11 | "I think I should come" | | 12 | "She pocketed the phone." | | 13 | "She looked like a schoolteacher" | | 14 | "She also looked, Quinn thought," | | 15 | "It pointed, unwavering, at the" | | 16 | "She lifted the compass higher." | | 17 | "She lowered it toward the" | | 18 | "She stood, brushing her coat" | | 19 | "She looked at the body." |
| | ratio | 0.326 | |
| 53.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 86 | | matches | | 0 | "The Tube station had no" | | 1 | "That was the first thing" | | 2 | "Harlow Quinn stood at the" | | 3 | "Her worn leather watch read" | | 4 | "The time didn't matter." | | 5 | "Time never mattered in places" | | 6 | "The call had come from" | | 7 | "The station appeared on no" | | 8 | "It wasn't listed in the" | | 9 | "A young constable named Ferris" | | 10 | "He paused, glancing back toward" | | 11 | "Quinn pulled her collar up." | | 12 | "The night had teeth." | | 13 | "The corridor descended at a" | | 14 | "The tiles were the right" | | 15 | "It wasn't grout." | | 16 | "It was a darker substance," | | 17 | "She crouched, keeping her gloves" | | 18 | "Ferris hurried off, grateful for" | | 19 | "She went further." |
| | ratio | 0.814 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow Quinn stood at the entrance where the tiled wall met a ragged wound of brick and dark earth, her breath misting in air that smelled of copper and somethi…" | | 1 | "But there it squatted beneath the streets of Camden, its tiled corridors stretching back into a darkness that swallowed her torchlight with appetite." | | 2 | "The man's expression, in death, was one of mild surprise, as though he had been told a particularly clever joke at the precise moment his soul had vacated the p…" | | 3 | "She looked at the lamps and the corridor and the way the tiles fit together just slightly wrong, and she thought about the two uniformed officers who had walked…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.233 | | leniency | 0.467 | | rawRatio | 0 | | effectiveRatio | 0 | |