| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 25 | | tagDensity | 0.52 | | leniency | 1 | | rawRatio | 0.077 | | effectiveRatio | 0.077 | |
| 91.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1206 | | 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) | |
| 87.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1206 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "familiar" | | 1 | "perfect" | | 2 | "crystal" |
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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 | 55 | | matches | (empty) | |
| 90.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1215 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 895 | | uniqueNames | 12 | | maxNameDensity | 0.78 | | worstName | "Patterson" | | maxWindowNameDensity | 2 | | worstWindowName | "Patterson" | | discoveredNames | | Tube | 1 | | Camden | 2 | | Detective | 1 | | Harlow | 1 | | Quinn | 3 | | Eighteen | 2 | | Morris | 3 | | Christmas | 1 | | Patterson | 7 | | Crisp | 1 | | Deptford | 1 | | London | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Patterson" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | 0 | "her face, and apparently decided against it" |
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| 35.39% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.646 | | wordCount | 1215 | | matches | | 0 | "not *sprawled*, not *fallen*, but arranged" | | 1 | "not *fallen*, but arranged" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 66 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 55.23 | | std | 29.91 | | cv | 0.542 | | sampleLengths | | 0 | 67 | | 1 | 30 | | 2 | 84 | | 3 | 73 | | 4 | 46 | | 5 | 26 | | 6 | 39 | | 7 | 37 | | 8 | 64 | | 9 | 5 | | 10 | 8 | | 11 | 87 | | 12 | 33 | | 13 | 88 | | 14 | 19 | | 15 | 94 | | 16 | 76 | | 17 | 108 | | 18 | 47 | | 19 | 93 | | 20 | 22 | | 21 | 69 |
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| 92.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 55 | | matches | | 0 | "been rigged" | | 1 | "being corrected" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 153 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 66 | | ratio | 0.076 | | matches | | 0 | "The body lay in the center of the platform, arranged — that was the word that came to her immediately, not *sprawled*, not *fallen*, but arranged — in a rough circle of scorched tile." | | 1 | "His eyes were open, and they weren't burned — not clouded with heat cataracts, not weeping fluid the way eyes did around real fire." | | 2 | "She let her eyes travel the platform, along the tiled walls where faded advertisements still clung in patches — a 1987 poster for cigarettes, ghost-pale and peeling — and stopped at a series of marks near the tunnel mouth." | | 3 | "\"No scorch pattern consistent with an explosion at all, actually — explosions blow outward unevenly, follow the weakest structural points. This is a perfect circle. Almost geometric.\" She turned back to the body, crouched again, and this time noticed what she'd missed the first pass — a fine white substance dusted along the dead man's palms, like chalk, or ash, or bone ground fine." | | 4 | "Quinn stood, feeling every one of her forty-one years in her knees, and looked once more at the dead man's face — at the fixed, wide-open terror in eyes that hadn't burned, at whatever he'd seen coming for him in the dark beneath Camden that no gas leak could explain." |
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| 84.96% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 894 | | adjectiveStacks | 1 | | stackExamples | | 0 | "fixed, wide-open terror" |
| | adverbCount | 45 | | adverbRatio | 0.050335570469798654 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.0145413870246085 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 18.41 | | std | 14.65 | | cv | 0.796 | | sampleLengths | | 0 | 38 | | 1 | 29 | | 2 | 18 | | 3 | 7 | | 4 | 5 | | 5 | 31 | | 6 | 18 | | 7 | 9 | | 8 | 20 | | 9 | 5 | | 10 | 1 | | 11 | 34 | | 12 | 17 | | 13 | 22 | | 14 | 11 | | 15 | 35 | | 16 | 24 | | 17 | 2 | | 18 | 27 | | 19 | 12 | | 20 | 12 | | 21 | 16 | | 22 | 9 | | 23 | 7 | | 24 | 24 | | 25 | 4 | | 26 | 5 | | 27 | 24 | | 28 | 5 | | 29 | 8 | | 30 | 65 | | 31 | 1 | | 32 | 1 | | 33 | 1 | | 34 | 19 | | 35 | 27 | | 36 | 6 | | 37 | 18 | | 38 | 39 | | 39 | 1 | | 40 | 30 | | 41 | 7 | | 42 | 12 | | 43 | 22 | | 44 | 21 | | 45 | 17 | | 46 | 8 | | 47 | 26 | | 48 | 4 | | 49 | 41 |
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| 92.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.6212121212121212 | | totalSentences | 66 | | uniqueOpeners | 41 | |
| 68.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 49 | | matches | | 0 | "Somewhere above them, London went" |
| | ratio | 0.02 | |
| 24.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 49 | | matches | | 0 | "She ducked her head beneath" | | 1 | "Her leather watch, the one" | | 2 | "She didn't need it to" | | 3 | "She needed it because it" | | 4 | "She didn't do that anymore." | | 5 | "He didn't look up" | | 6 | "She let the flatness of" | | 7 | "She'd learned, over the years," | | 8 | "She looked at the dead" | | 9 | "His eyes were open, and" | | 10 | "They were simply wide." | | 11 | "His mouth was open too," | | 12 | "She reached into her coat" | | 13 | "She didn't believe it, and" | | 14 | "She let her eyes travel" | | 15 | "She crossed to it, ran" | | 16 | "It was smooth-edged, almost polished," | | 17 | "She didn't touch it with" | | 18 | "She thought of Morris." | | 19 | "She hadn't known what to" |
| | ratio | 0.49 | |
| 72.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 49 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "The forensics lights had been" | | 2 | "The uniformed constable at the" | | 3 | "She ducked her head beneath" | | 4 | "Her leather watch, the one" | | 5 | "She didn't need it to" | | 6 | "She needed it because it" | | 7 | "She didn't do that anymore." | | 8 | "The body lay in the" | | 9 | "A man in his thirties," | | 10 | "He didn't look up" | | 11 | "Quinn crouched on the opposite" | | 12 | "Patterson gestured with his pen" | | 13 | "She let the flatness of" | | 14 | "She'd learned, over the years," | | 15 | "She looked at the dead" | | 16 | "His eyes were open, and" | | 17 | "They were simply wide." | | 18 | "His mouth was open too," | | 19 | "She reached into her coat" |
| | ratio | 0.776 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 18.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 4 | | matches | | 0 | "DI Patterson crouched beside him, scribbling in his notebook with the particular smugness of a man who thought he'd already solved something." | | 1 | "Patterson's jaw tightened, the particular tell of a man who didn't like being corrected by a woman with more service years than him but the same rank." | | 2 | "Deep ones, gouged into old stone that should have been harder than that, four parallel lines like something enormous and taloned had dragged itself along the wa…" | | 3 | "It was smooth-edged, almost polished, as if whatever made it had passed through the stone rather than merely scratching its surface." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "the barrier straightened, the way they all did" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Patterson repeated (repeat)" |
| | dialogueSentences | 25 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0.25 | | effectiveRatio | 0.08 | |