| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 4 | | adverbTags | | 0 | "Quinn approached slowly [slowly]" | | 1 | "Further plan tangled [Further]" | | 2 | "His annoyance hardened again [again]" | | 3 | "She stood abruptly [abruptly]" |
| | dialogueSentences | 30 | | tagDensity | 0.567 | | leniency | 1 | | rawRatio | 0.235 | | effectiveRatio | 0.235 | |
| 78.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 930 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "angrily" | | 2 | "carefully" | | 3 | "completely" |
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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) | |
| 24.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 930 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "echoed" | | 1 | "tension" | | 2 | "silence" | | 3 | "shimmered" | | 4 | "familiar" | | 5 | "standard" | | 6 | "pawn" | | 7 | "depths" | | 8 | "perfect" | | 9 | "unravel" |
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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 | 1 | | narrationSentences | 96 | | matches | | |
| 98.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 927 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 704 | | uniqueNames | 25 | | maxNameDensity | 1.42 | | worstName | "Davies" | | maxWindowNameDensity | 3 | | worstWindowName | "Davies" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 6 | | Tube | 1 | | Camden | 1 | | Davies | 10 | | Precise | 1 | | Irina | 1 | | Current | 1 | | Davyens | 1 | | Morris | 1 | | London | 2 | | Veil | 1 | | Market | 1 | | Tin | 1 | | Pan | 1 | | Alley | 1 | | Formal | 1 | | Backlog | 1 | | Crime | 1 | | Scene | 1 | | Investigation | 1 | | English | 1 | | Museum | 1 | | Eva | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Davies" | | 4 | "Irina" | | 5 | "Morris" | | 6 | "Backlog" | | 7 | "Eva" |
| | places | | 0 | "Precise" | | 1 | "London" | | 2 | "English" |
| | globalScore | 0.79 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 927 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 97.55% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 28.97 | | std | 14.24 | | cv | 0.491 | | sampleLengths | | 0 | 43 | | 1 | 44 | | 2 | 36 | | 3 | 18 | | 4 | 27 | | 5 | 18 | | 6 | 33 | | 7 | 43 | | 8 | 33 | | 9 | 18 | | 10 | 28 | | 11 | 10 | | 12 | 26 | | 13 | 15 | | 14 | 41 | | 15 | 42 | | 16 | 38 | | 17 | 28 | | 18 | 43 | | 19 | 21 | | 20 | 72 | | 21 | 10 | | 22 | 15 | | 23 | 13 | | 24 | 35 | | 25 | 1 | | 26 | 47 | | 27 | 13 | | 28 | 21 | | 29 | 27 | | 30 | 37 | | 31 | 31 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 96 | | matches | | |
| 38.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 124 | | matches | | 0 | "was shaking" | | 1 | "was moving" | | 2 | "was going" |
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| 61.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 106 | | ratio | 0.028 | | matches | | 0 | "Concrete steps echoed with determination—Detective Harlow Quinn's boots wore the patina of years on their lace-ups." | | 1 | "Ahead, Davies—his bulky silhouette broke the monochrome shadows—fussed over the ominously still corpse slouched against a rusted turnstile." | | 2 | "Film of paraffin wax everywhere, sticky-sweet smell — and red wrinkled tips." |
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| 84.34% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 716 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.051675977653631286 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.02653631284916201 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 8.74 | | std | 5.22 | | cv | 0.598 | | sampleLengths | | 0 | 5 | | 1 | 16 | | 2 | 18 | | 3 | 2 | | 4 | 1 | | 5 | 1 | | 6 | 9 | | 7 | 12 | | 8 | 10 | | 9 | 13 | | 10 | 18 | | 11 | 10 | | 12 | 8 | | 13 | 14 | | 14 | 1 | | 15 | 3 | | 16 | 14 | | 17 | 7 | | 18 | 6 | | 19 | 3 | | 20 | 15 | | 21 | 19 | | 22 | 10 | | 23 | 4 | | 24 | 19 | | 25 | 3 | | 26 | 9 | | 27 | 12 | | 28 | 2 | | 29 | 6 | | 30 | 18 | | 31 | 7 | | 32 | 6 | | 33 | 12 | | 34 | 23 | | 35 | 5 | | 36 | 6 | | 37 | 4 | | 38 | 7 | | 39 | 9 | | 40 | 10 | | 41 | 7 | | 42 | 8 | | 43 | 7 | | 44 | 7 | | 45 | 27 | | 46 | 13 | | 47 | 7 | | 48 | 7 | | 49 | 4 |
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| 97.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.6132075471698113 | | totalSentences | 106 | | uniqueOpeners | 65 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 87 | | matches | | 0 | "More was there than met" | | 1 | "Perhaps there would even be" | | 2 | "Usually someone with particularly swift" | | 3 | "Further plan tangled urgency" | | 4 | "Perhaps more than meets the" |
| | ratio | 0.057 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 87 | | matches | | 0 | "It wasn't the usual smells" | | 1 | "Her nose, finely tuned by" | | 2 | "He looked up as Quinn's" | | 3 | "His clean-shaven baby face was" | | 4 | "Her worn leather watch caught" | | 5 | "She crouched beside the still" | | 6 | "Her interest was drawn to" | | 7 | "Her foresight glimpsed matching patches" | | 8 | "She pointed to his boot-laces" | | 9 | "Her eyes narrowed" | | 10 | "She observed better the swollen" | | 11 | "It wasn’t any choker found" | | 12 | "It was personal, familiar." | | 13 | "Her own suspicions rose." | | 14 | "She knew he’d file complaints" | | 15 | "Her military precision-guided us to" | | 16 | "His team didn’t even see" | | 17 | "She knew better than most" | | 18 | "Her watch ticked privately." | | 19 | "Her partner Morris should be" |
| | ratio | 0.333 | |
| 97.93% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 87 | | matches | | 0 | "qutain clanked against stone walls." | | 1 | "The platform of the abandoned" | | 2 | "The beam of her torch" | | 3 | "It wasn't the usual smells" | | 4 | "Her nose, finely tuned by" | | 5 | "He looked up as Quinn's" | | 6 | "His clean-shaven baby face was" | | 7 | "That was Davies, always proper" | | 8 | "Quinn approached slowly" | | 9 | "Her worn leather watch caught" | | 10 | "Davies waved angrily." | | 11 | "She crouched beside the still" | | 12 | "A penlight from her pocket" | | 13 | "Something caught her eye." | | 14 | "Davies would start mentioning astrology" | | 15 | "Quinn ignored him." | | 16 | "Her interest was drawn to" | | 17 | "Film of paraffin wax everywhere," | | 18 | "The natural thought tilled her" | | 19 | "The patient had been wearing" |
| | ratio | 0.724 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "Her own conclusions barrelled forward with momentum stuffed from changing lanterns, her watch ticking away likely perfect timing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0 | | effectiveRatio | 0 | |