| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1459 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 86.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1459 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "footsteps" | | 1 | "glistening" | | 2 | "weight" | | 3 | "flicked" |
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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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1469 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1400 | | uniqueNames | 26 | | maxNameDensity | 0.36 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Nest" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Quinn | 3 | | Vauxhall | 1 | | Market | 1 | | Herrera | 5 | | Whitechapel | 1 | | London | 1 | | Andalusian | 1 | | Soho | 1 | | Frith | 1 | | Street | 2 | | Center | 1 | | Point | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Mornington | 1 | | Crescent | 1 | | Camden | 1 | | High | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Morris | 2 | | Harlow | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Harlow" |
| | places | | 0 | "Vauxhall" | | 1 | "Whitechapel" | | 2 | "London" | | 3 | "Andalusian" | | 4 | "Soho" | | 5 | "Frith" | | 6 | "Street" | | 7 | "Center" | | 8 | "Point" | | 9 | "Tottenham" | | 10 | "Court" | | 11 | "Road" | | 12 | "Mornington" | | 13 | "Crescent" | | 14 | "Camden" | | 15 | "High" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like antifreeze" |
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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 | 1469 | | matches | (empty) | |
| 91.76% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 89 | | matches | | 0 | "watching that sign" | | 1 | "hoarding that she'd" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 47.39 | | std | 36.89 | | cv | 0.778 | | sampleLengths | | 0 | 99 | | 1 | 85 | | 2 | 43 | | 3 | 120 | | 4 | 58 | | 5 | 90 | | 6 | 89 | | 7 | 2 | | 8 | 2 | | 9 | 113 | | 10 | 7 | | 11 | 81 | | 12 | 90 | | 13 | 92 | | 14 | 40 | | 15 | 45 | | 16 | 21 | | 17 | 26 | | 18 | 88 | | 19 | 73 | | 20 | 23 | | 21 | 7 | | 22 | 7 | | 23 | 23 | | 24 | 6 | | 25 | 13 | | 26 | 7 | | 27 | 35 | | 28 | 43 | | 29 | 25 | | 30 | 16 |
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| 97.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 85 | | matches | | 0 | "been taught" | | 1 | "is meant" |
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| 82.53% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 227 | | matches | | 0 | "was starting" | | 1 | "was going" | | 2 | "was already reading" | | 3 | "was grinding" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 89 | | ratio | 0.09 | | matches | | 0 | "The kid who'd died in the Whitechapel stairwell two weeks ago — the girl with the chemical burns that no fire had made, the sutures in her arm that matched no hospital, no clinic, no legitimate practice in greater London — Herrera's sutures." | | 1 | "He paused at the wide black glass of a closed restaurant, and the green neon of a distant sign flashed in it, and in that flash her own silhouette moved — cropped hair, sharp jaw, the particular set of a woman walking with intent." | | 2 | "But she'd spent two decades learning how people ran — where they looked, when they decided, which way their weight would cheat them — and when he flicked a glance left at the green of Mornington Crescent she was already reading it." | | 3 | "Around a tiled arch, by the light of a lantern turned down to a coal-orange murmur, sat a booth of black iron grillework, and inside the booth sat a figure she couldn't describe afterward if she tried — a shape like a person the way a glove is like a hand." | | 4 | "Then the gate in the archway ground open, and from beyond it came light and noise — lantern-glow on tile, a murmur of a crowd, a hundred conversations in a space that should have held only rats and standing water." | | 5 | "Herrera kissed his thumb, which had touched a small silver medallion at his throat — Saint Christopher, patron of travelers, and she thought: he's afraid." | | 6 | "Secure the scene — except her radio had been losing bars since the second step down, and what exactly would she transmit?" | | 7 | "The gate shuddered, hesitated — and opened." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1397 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.018611309949892626 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002863278453829635 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 16.51 | | std | 13.02 | | cv | 0.789 | | sampleLengths | | 0 | 26 | | 1 | 32 | | 2 | 10 | | 3 | 1 | | 4 | 30 | | 5 | 6 | | 6 | 34 | | 7 | 23 | | 8 | 22 | | 9 | 20 | | 10 | 2 | | 11 | 6 | | 12 | 15 | | 13 | 15 | | 14 | 20 | | 15 | 2 | | 16 | 14 | | 17 | 4 | | 18 | 43 | | 19 | 22 | | 20 | 12 | | 21 | 1 | | 22 | 1 | | 23 | 4 | | 24 | 3 | | 25 | 8 | | 26 | 29 | | 27 | 6 | | 28 | 39 | | 29 | 30 | | 30 | 2 | | 31 | 2 | | 32 | 8 | | 33 | 3 | | 34 | 32 | | 35 | 44 | | 36 | 13 | | 37 | 2 | | 38 | 2 | | 39 | 35 | | 40 | 42 | | 41 | 36 | | 42 | 7 | | 43 | 23 | | 44 | 20 | | 45 | 28 | | 46 | 3 | | 47 | 7 | | 48 | 5 | | 49 | 42 |
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| 68.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4772727272727273 | | totalSentences | 88 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 74 | | matches | | 0 | "Then the gate in the" | | 1 | "Then he looked back and" | | 2 | "Then he was through, and" |
| | ratio | 0.041 | |
| 90.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 74 | | matches | | 0 | "She checked her watch out" | | 1 | "She'd thought he was a" | | 2 | "She'd assumed it was fence" | | 3 | "She was starting to suspect" | | 4 | "He was her thread." | | 5 | "He stood under the sign's" | | 6 | "She sank lower in the" | | 7 | "It drove everyone off the" | | 8 | "He went north on foot," | | 9 | "She kept the distance loose" | | 10 | "He paused at the wide" | | 11 | "He was twenty-nine and quick" | | 12 | "He cut off Camden High" | | 13 | "She stopped at the edge" | | 14 | "She went through the gap" | | 15 | "He pressed something small and" | | 16 | "She could hear the desk" | | 17 | "She was off the books" | | 18 | "She'd told herself she carried" | | 19 | "She took it out now," |
| | ratio | 0.324 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 74 | | matches | | 0 | "The green neon sign above" | | 1 | "Harlow Quinn had been watching" | | 2 | "She checked her watch out" | | 3 | "The worn leather strap had" | | 4 | "The bar had closed at" | | 5 | "The last punters had spilled" | | 6 | "She'd thought he was a" | | 7 | "She'd assumed it was fence" | | 8 | "She was starting to suspect" | | 9 | "The kitchen door opened at" | | 10 | "The man who came out" | | 11 | "He was her thread." | | 12 | "The kid who'd died in" | | 13 | "Quinn had pulled the pattern" | | 14 | "He stood under the sign's" | | 15 | "That was tradecraft." | | 16 | "Paramedics didn't learn that in" | | 17 | "She sank lower in the" | | 18 | "The rain worked in her" | | 19 | "It drove everyone off the" |
| | ratio | 0.689 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 6 | | matches | | 0 | "The green neon sign above the Raven's Nest bled its color into the rain, turning the puddles outside the bar into something that looked like antifreeze." | | 1 | "The kid who'd died in the Whitechapel stairwell two weeks ago — the girl with the chemical burns that no fire had made, the sutures in her arm that matched no h…" | | 2 | "He went north on foot, through Soho's glistening canyon of shut shops, doubling once through a mews off Frith Street in a way that told her he'd done this befor…" | | 3 | "Then the gate in the archway ground open, and from beyond it came light and noise — lantern-glow on tile, a murmur of a crowd, a hundred conversations in a spac…" | | 4 | "Herrera kissed his thumb, which had touched a small silver medallion at his throat — Saint Christopher, patron of travelers, and she thought: he's afraid." | | 5 | "She thought of a dead girl's arm and the neat, careful stitches in it, done by a man who'd said please." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |