| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 952 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 952 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "flickered" | | 1 | "flicked" | | 2 | "echoed" | | 3 | "glinting" | | 4 | "gloom" | | 5 | "footsteps" | | 6 | "measured" | | 7 | "shattered" | | 8 | "flicker" | | 9 | "dancing" | | 10 | "mosaic" | | 11 | "vibrated" | | 12 | "lurched" | | 13 | "pulsed" | | 14 | "velvet" | | 15 | "pulse" | | 16 | "throbbed" | | 17 | "etched" | | 18 | "whisper" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "the air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 19 | | totalWords | 947 | | ratio | 0.02 | | matches | | 0 | "The Raven’s Nest" | | 1 | "The tides." | | 2 | "it" | | 3 | "No more Morris’s ghosts. Just the job." | | 4 | "The tides turn." | | 5 | "Don’t panic, Quinn." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 42.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 838 | | uniqueNames | 13 | | maxNameDensity | 2.15 | | worstName | "Herrera" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Morris | 4 | | Herrera | 18 | | Saint | 1 | | Christopher | 1 | | Thames | 1 | | Gunfire | 1 | | Veil | 1 | | Market | 4 | | Tube | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Gunfire" | | 8 | "Market" |
| | places | | | globalScore | 0.426 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like forever, the air growing cold" |
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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 | 947 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 104 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 27.06 | | std | 16.96 | | cv | 0.627 | | sampleLengths | | 0 | 68 | | 1 | 15 | | 2 | 36 | | 3 | 21 | | 4 | 24 | | 5 | 43 | | 6 | 33 | | 7 | 38 | | 8 | 39 | | 9 | 24 | | 10 | 36 | | 11 | 32 | | 12 | 43 | | 13 | 2 | | 14 | 7 | | 15 | 1 | | 16 | 3 | | 17 | 37 | | 18 | 27 | | 19 | 2 | | 20 | 37 | | 21 | 13 | | 22 | 27 | | 23 | 70 | | 24 | 40 | | 25 | 37 | | 26 | 24 | | 27 | 9 | | 28 | 33 | | 29 | 32 | | 30 | 21 | | 31 | 11 | | 32 | 10 | | 33 | 46 | | 34 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 142 | | matches | | |
| 5.49% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 104 | | ratio | 0.048 | | matches | | 0 | "She flicked open her wristwatch—2:17 a.m.—then slammed it shut." | | 1 | "Herrera had gone left—toward the tracks." | | 2 | "And then the train came—screams of metal, a wind that shoved her back against the platform." | | 3 | "Herrera’s hand wasn’t on his weapon—it was on his medallion." | | 4 | "The weasel-faced seller lunged—a knife in her fingers, the blade etched with symbols." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 848 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.01650943396226415 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0011792452830188679 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 9.11 | | std | 5.98 | | cv | 0.656 | | sampleLengths | | 0 | 11 | | 1 | 18 | | 2 | 23 | | 3 | 9 | | 4 | 7 | | 5 | 6 | | 6 | 9 | | 7 | 23 | | 8 | 5 | | 9 | 3 | | 10 | 5 | | 11 | 10 | | 12 | 11 | | 13 | 7 | | 14 | 17 | | 15 | 23 | | 16 | 9 | | 17 | 11 | | 18 | 7 | | 19 | 18 | | 20 | 8 | | 21 | 9 | | 22 | 25 | | 23 | 4 | | 24 | 3 | | 25 | 5 | | 26 | 7 | | 27 | 12 | | 28 | 9 | | 29 | 3 | | 30 | 3 | | 31 | 21 | | 32 | 13 | | 33 | 10 | | 34 | 6 | | 35 | 4 | | 36 | 3 | | 37 | 13 | | 38 | 13 | | 39 | 6 | | 40 | 1 | | 41 | 1 | | 42 | 9 | | 43 | 16 | | 44 | 16 | | 45 | 2 | | 46 | 3 | | 47 | 4 | | 48 | 1 | | 49 | 3 |
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| 49.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3269230769230769 | | totalSentences | 104 | | uniqueOpeners | 34 | |
| 39.22% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 85 | | matches | | | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 85 | | matches | | 0 | "She flicked open her wristwatch—2:17" | | 1 | "She hit the corner and" | | 2 | "He’d known she’d be there." | | 3 | "He laughed, the sound short" | | 4 | "He gestured to the cracked" | | 5 | "She exhaled, sharp." | | 6 | "Her instincts tightened." | | 7 | "She found the service door" | | 8 | "Her fingers brushed the cold" | | 9 | "She could see the flicker" | | 10 | "She crept forward." | | 11 | "Her chest felt hollowed out." | | 12 | "She could turn back now," | | 13 | "Her hand went to her" | | 14 | "Her pulse throbbed in her" | | 15 | "He moved toward her like" | | 16 | "She gripped her gun tighter." | | 17 | "He glanced at the seller," | | 18 | "he hissed, eyes blazing" | | 19 | "Her jaw set." |
| | ratio | 0.259 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 85 | | matches | | 0 | "The rain had turned Soho" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The green neon sign of" | | 3 | "She flicked open her wristwatch—2:17" | | 4 | "A shout echoed down the" | | 5 | "She hit the corner and" | | 6 | "Tomás Herrera crouched halfway down" | | 7 | "The steps crumpled like paper." | | 8 | "Quinn’s jaw tightened." | | 9 | "He’d known she’d be there." | | 10 | "Herrera’s voice carried over the" | | 11 | "*The tides.* She’d heard that" | | 12 | "The medallion dangled from Herrera’s" | | 13 | "Quinn’s fingers closed around the" | | 14 | "He laughed, the sound short" | | 15 | "He gestured to the cracked" | | 16 | "Gunfire cracked over the sound" | | 17 | "Quinn lunged forward, but Herrera" | | 18 | "The flashlight beam vanished." | | 19 | "She exhaled, sharp." |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "he hissed, eyes blazing" |
| |
| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 17 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0.5 | | effectiveRatio | 0.118 | |