| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.636 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1347 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "very" | | 2 | "really" |
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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) | |
| 85.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1347 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "silence" | | 1 | "wavering" | | 2 | "flicker" | | 3 | "tension" |
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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 | 117 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 117 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1355 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1271 | | uniqueNames | 21 | | maxNameDensity | 0.55 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Street" | | discoveredNames | | Quinn | 7 | | Berwick | 1 | | Street | 4 | | Oxfords | 1 | | London | 3 | | Morris | 3 | | Wardour | 1 | | Soho | 2 | | Nest | 3 | | Raven | 1 | | Met | 1 | | Fleet | 1 | | Brewer | 1 | | Lexington | 1 | | Regent | 1 | | Park | 1 | | Camden | 1 | | Tube | 1 | | Yard | 1 | | Tommy | 1 | | Herrera | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Raven" | | 3 | "Met" | | 4 | "Lexington" | | 5 | "Tommy" | | 6 | "Herrera" |
| | places | | 0 | "Berwick" | | 1 | "Street" | | 2 | "London" | | 3 | "Wardour" | | 4 | "Soho" | | 5 | "Fleet" | | 6 | "Brewer" | | 7 | "Regent" | | 8 | "Camden" | | 9 | "Yard" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a Tube platform, but the tile" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.738 | | wordCount | 1355 | | matches | | 0 | "Not the panicked zig-zag of a cornered man, but a pattern" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 36.62 | | std | 24.88 | | cv | 0.679 | | sampleLengths | | 0 | 19 | | 1 | 106 | | 2 | 18 | | 3 | 33 | | 4 | 81 | | 5 | 70 | | 6 | 67 | | 7 | 2 | | 8 | 48 | | 9 | 9 | | 10 | 41 | | 11 | 63 | | 12 | 48 | | 13 | 66 | | 14 | 38 | | 15 | 39 | | 16 | 2 | | 17 | 64 | | 18 | 55 | | 19 | 16 | | 20 | 13 | | 21 | 42 | | 22 | 15 | | 23 | 9 | | 24 | 28 | | 25 | 43 | | 26 | 27 | | 27 | 47 | | 28 | 74 | | 29 | 39 | | 30 | 13 | | 31 | 41 | | 32 | 25 | | 33 | 13 | | 34 | 5 | | 35 | 32 | | 36 | 4 |
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| 99.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 117 | | matches | | 0 | "been, given" | | 1 | "been sealed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 9 | | totalVerbs | 197 | | matches | | 0 | "was going" | | 1 | "was leading" | | 2 | "was already swinging" | | 3 | "was already telling" | | 4 | "was waiting" | | 5 | "was standing" | | 6 | "was holding" | | 7 | "was looking" | | 8 | "was waiting" |
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| 1.18% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 121 | | ratio | 0.05 | | matches | | 0 | "The suspect — a wiry man in a dark anorak, face obscured by a hood — was fast." | | 1 | "The place had been clean on paper — no licenses pulled, no noise complaints, nothing the Met wanted to touch." | | 2 | "Not stopping — that would have been too obvious — but easing back into a fast walk, the way a man does when he's sure of his footing." | | 3 | "She almost missed the entrance — a rusted door behind a dumpster, half-hidden by graffiti that might have been a sigil or might have been a tag." | | 4 | "Instead, the stairwell simply ended in a low, arched opening, and beyond it —" | | 5 | "She thought she could see movement in the dark — a shifting, a rearranging." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1268 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.02365930599369085 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007097791798107256 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 11.2 | | std | 9.15 | | cv | 0.817 | | sampleLengths | | 0 | 10 | | 1 | 9 | | 2 | 18 | | 3 | 23 | | 4 | 23 | | 5 | 42 | | 6 | 10 | | 7 | 8 | | 8 | 25 | | 9 | 3 | | 10 | 5 | | 11 | 3 | | 12 | 20 | | 13 | 1 | | 14 | 2 | | 15 | 10 | | 16 | 7 | | 17 | 38 | | 18 | 8 | | 19 | 2 | | 20 | 16 | | 21 | 20 | | 22 | 24 | | 23 | 15 | | 24 | 9 | | 25 | 12 | | 26 | 31 | | 27 | 2 | | 28 | 13 | | 29 | 6 | | 30 | 11 | | 31 | 5 | | 32 | 13 | | 33 | 6 | | 34 | 3 | | 35 | 31 | | 36 | 4 | | 37 | 4 | | 38 | 2 | | 39 | 2 | | 40 | 28 | | 41 | 20 | | 42 | 13 | | 43 | 6 | | 44 | 27 | | 45 | 6 | | 46 | 9 | | 47 | 9 | | 48 | 2 | | 49 | 21 |
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| 48.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.36363636363636365 | | totalSentences | 121 | | uniqueOpeners | 44 | |
| 34.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 97 | | matches | | 0 | "Instead, the stairwell simply ended" |
| | ratio | 0.01 | |
| 75.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 97 | | matches | | 0 | "She lowered her shoulder into" | | 1 | "He cut left on Berwick" | | 2 | "She hadn't taken it off" | | 3 | "she shouted, not because she" | | 4 | "Her lungs burned." | | 5 | "He didn't stop." | | 6 | "He darted through a gap" | | 7 | "She'd been watching the Nest" | | 8 | "She'd found his body in" | | 9 | "She thought of a cardiogram," | | 10 | "Her radio crackled at her" | | 11 | "She ignored it." | | 12 | "He was leading her." | | 13 | "They were in Camden now," | | 14 | "He turned down a service" | | 15 | "She almost missed the entrance" | | 16 | "She caught it with her" | | 17 | "Her skin prickled." | | 18 | "She drew her baton." | | 19 | "She didn't know why, exactly," |
| | ratio | 0.361 | |
| 52.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 97 | | matches | | 0 | "She lowered her shoulder into" | | 1 | "The suspect — a wiry" | | 2 | "He cut left on Berwick" | | 3 | "The worn leather strap had" | | 4 | "She hadn't taken it off" | | 5 | "she shouted, not because she" | | 6 | "Her lungs burned." | | 7 | "The rain came down harder." | | 8 | "He didn't stop." | | 9 | "He darted through a gap" | | 10 | "That was the part that" | | 11 | "The part that had worried" | | 12 | "She'd been watching the Nest" | | 13 | "The Raven's Nest, with its" | | 14 | "The place had been clean" | | 15 | "A place that smelled of" | | 16 | "A place where the rules" | | 17 | "She'd found his body in" | | 18 | "The suspect turned sharply onto" | | 19 | "The route was deliberate, she" |
| | ratio | 0.814 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 97 | | matches | | 0 | "Whoever this man was, he" | | 1 | "By the time they reached" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "Faster than a man his size should have been, given the slick of standing water that turned the pavement into a grey mirror." | | 1 | "Right now, there was only this: a low archway, a hollow in a brick pillar, a young man holding a bone, and a darkness that was waiting to see what she would do." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |