| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1181 | | 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) | |
| 74.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1181 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "comfortable" | | 1 | "sense of" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "measured" | | 5 | "resolve" |
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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 | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | 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 | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1181 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 998 | | uniqueNames | 23 | | maxNameDensity | 1.5 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Brewer | 1 | | Street | 2 | | Soho | 1 | | Harlow | 1 | | Quinn | 15 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Dean | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Morris | 4 | | Whitechapel | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Tube | 1 | | Herrera | 4 | | Met | 1 | | London | 1 | | Crown | 1 | | Prosecution | 1 | | Service | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Morris" | | 5 | "Tube" | | 6 | "Herrera" | | 7 | "Met" | | 8 | "Service" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Dean" | | 4 | "Shaftesbury" | | 5 | "Avenue" | | 6 | "Whitechapel" | | 7 | "Tottenham" | | 8 | "Court" | | 9 | "Road" | | 10 | "London" |
| | globalScore | 0.748 | | windowScore | 0.833 | |
| 76.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 2 | | matches | | 0 | "quite catch" | | 1 | "looked like a knuckle of bone, polished s" |
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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 | 1181 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 43.74 | | std | 30.66 | | cv | 0.701 | | sampleLengths | | 0 | 83 | | 1 | 77 | | 2 | 11 | | 3 | 64 | | 4 | 57 | | 5 | 9 | | 6 | 73 | | 7 | 53 | | 8 | 19 | | 9 | 96 | | 10 | 39 | | 11 | 93 | | 12 | 63 | | 13 | 15 | | 14 | 3 | | 15 | 72 | | 16 | 23 | | 17 | 28 | | 18 | 5 | | 19 | 29 | | 20 | 28 | | 21 | 2 | | 22 | 79 | | 23 | 15 | | 24 | 81 | | 25 | 58 | | 26 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1003 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 29 | | adverbRatio | 0.028913260219341975 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004985044865403789 | |
| 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 | 11.14 | | std | 8.87 | | cv | 0.796 | | sampleLengths | | 0 | 19 | | 1 | 23 | | 2 | 24 | | 3 | 4 | | 4 | 13 | | 5 | 15 | | 6 | 9 | | 7 | 10 | | 8 | 21 | | 9 | 14 | | 10 | 8 | | 11 | 11 | | 12 | 5 | | 13 | 5 | | 14 | 15 | | 15 | 20 | | 16 | 11 | | 17 | 8 | | 18 | 5 | | 19 | 3 | | 20 | 5 | | 21 | 20 | | 22 | 7 | | 23 | 17 | | 24 | 7 | | 25 | 2 | | 26 | 7 | | 27 | 4 | | 28 | 3 | | 29 | 15 | | 30 | 5 | | 31 | 4 | | 32 | 35 | | 33 | 6 | | 34 | 6 | | 35 | 27 | | 36 | 14 | | 37 | 5 | | 38 | 14 | | 39 | 8 | | 40 | 10 | | 41 | 19 | | 42 | 6 | | 43 | 8 | | 44 | 1 | | 45 | 18 | | 46 | 26 | | 47 | 3 | | 48 | 4 | | 49 | 30 |
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| 53.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.39622641509433965 | | totalSentences | 106 | | uniqueOpeners | 42 | |
| 38.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 87 | | matches | | 0 | "Then the suspect produced something" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 87 | | matches | | 0 | "She counted her breaths." | | 1 | "She'd seen the shadows moving" | | 2 | "She pushed off the wall." | | 3 | "Her shoes slapped wet stone." | | 4 | "She swore under her breath" | | 5 | "He crossed Shaftesbury Avenue against" | | 6 | "She liked that." | | 7 | "He didn't head toward the" | | 8 | "He cut down a narrow" | | 9 | "She hesitated at the mouth" | | 10 | "She drew her torch, clicked" | | 11 | "She followed the sound down" | | 12 | "He stepped aside, and the" | | 13 | "He leaned against the doorframe," | | 14 | "Their eyes met." | | 15 | "She'd been building this case" | | 16 | "His voice carried a faint" | | 17 | "Her reflection ghosted across a" | | 18 | "She took a measured step" | | 19 | "He paused, studied her" |
| | ratio | 0.253 | |
| 28.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 87 | | matches | | 0 | "Detective Harlow Quinn pressed her" | | 1 | "Water streamed down her closely" | | 2 | "She counted her breaths." | | 3 | "The suspect emerged from the" | | 4 | "Quinn had been watching the" | | 5 | "The clique that met inside" | | 6 | "The bar's owner, a pale" | | 7 | "The back room, the one" | | 8 | "She'd seen the shadows moving" | | 9 | "Tonight, someone had slipped out" | | 10 | "She pushed off the wall." | | 11 | "Her shoes slapped wet stone." | | 12 | "The suspect cut left onto" | | 13 | "Quinn kept her distance, forty" | | 14 | "A black cab swerved through" | | 15 | "She swore under her breath" | | 16 | "The suspect didn't look back." | | 17 | "That bothered her." | | 18 | "This one moved with purpose," | | 19 | "He crossed Shaftesbury Avenue against" |
| | ratio | 0.862 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 4 | | matches | | 0 | "Rain hammered the cobblestones of Brewer Street, turning the gutters into rivers that mirrored the neon blur of Soho." | | 1 | "The clique that met inside those walls thought themselves invisible." | | 2 | "He stepped aside, and the suspect passed through the doorway into a low-roofed chamber that stretched beyond the reach of Quinn's vision." | | 3 | "She'd been building this case for eighteen months, and everything she'd gathered pointed to a black market that shouldn't exist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "the cabbie shouted (shout)" |
| | dialogueSentences | 17 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0.333 | | effectiveRatio | 0.118 | |