| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1252 | | totalAiIsmAdverbs | 1 | | 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) | |
| 64.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1252 | | totalAiIsms | 9 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "down her spine" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | |
| | highlights | | 0 | "pulse" | | 1 | "quickened" | | 2 | "scanning" | | 3 | "down her spine" | | 4 | "measured" | | 5 | "raced" | | 6 | "electric" | | 7 | "could feel" |
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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 | 98 | | matches | (empty) | |
| 99.13% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1243 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.64% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1243 | | uniqueNames | 20 | | maxNameDensity | 1.29 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 2 | | Quinn | 16 | | Old | 1 | | Compton | 1 | | Street | 3 | | Herrera | 8 | | Raven | 1 | | Nest | 1 | | Morris | 3 | | Greek | 1 | | Camden | 5 | | Metropolitan | 1 | | Police | 1 | | Road | 1 | | High | 1 | | Veil | 1 | | Market | 2 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "Greek" | | 5 | "Camden" | | 6 | "Road" | | 7 | "High" |
| | globalScore | 0.856 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.805 | | wordCount | 1243 | | matches | | 0 | "not electric, but flickering, like torches" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | |
| 87.38% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 38.84 | | std | 17.71 | | cv | 0.456 | | sampleLengths | | 0 | 49 | | 1 | 39 | | 2 | 72 | | 3 | 23 | | 4 | 61 | | 5 | 27 | | 6 | 61 | | 7 | 37 | | 8 | 30 | | 9 | 39 | | 10 | 43 | | 11 | 38 | | 12 | 34 | | 13 | 39 | | 14 | 11 | | 15 | 65 | | 16 | 31 | | 17 | 62 | | 18 | 15 | | 19 | 34 | | 20 | 26 | | 21 | 53 | | 22 | 22 | | 23 | 83 | | 24 | 30 | | 25 | 63 | | 26 | 36 | | 27 | 31 | | 28 | 36 | | 29 | 18 | | 30 | 18 | | 31 | 17 |
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| 90.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 98 | | matches | | 0 | "was connected" | | 1 | "were shuttered" | | 2 | "been transformed" | | 3 | "was pulled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 212 | | matches | | 0 | "was booking" | | 1 | "was going" | | 2 | "wasn't calling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 98 | | ratio | 0.061 | | matches | | 0 | "The clique of criminals—or worse—that she'd been tracking since that night three years ago." | | 1 | "The beam of her torch caught something on the ground—footprints in the accumulated grime, fresh, leading deeper." | | 2 | "The voices below grew momentarily louder, and she caught a glimpse of light—not electric, but flickering, like torches." | | 3 | "The voices had gone quiet—had they heard the door?" | | 4 | "People moved between the stalls—dozens of them—examining goods laid out on tables." | | 5 | "The medallion around his neck—Saint Christopher, she'd noted from her surveillance photos—caught the torchlight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1259 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.024622716441620333 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007148530579825258 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 12.68 | | std | 7.33 | | cv | 0.578 | | sampleLengths | | 0 | 22 | | 1 | 27 | | 2 | 16 | | 3 | 23 | | 4 | 14 | | 5 | 16 | | 6 | 28 | | 7 | 14 | | 8 | 5 | | 9 | 14 | | 10 | 4 | | 11 | 8 | | 12 | 20 | | 13 | 13 | | 14 | 1 | | 15 | 19 | | 16 | 6 | | 17 | 3 | | 18 | 6 | | 19 | 12 | | 20 | 24 | | 21 | 16 | | 22 | 21 | | 23 | 10 | | 24 | 12 | | 25 | 15 | | 26 | 6 | | 27 | 9 | | 28 | 9 | | 29 | 6 | | 30 | 6 | | 31 | 15 | | 32 | 18 | | 33 | 11 | | 34 | 10 | | 35 | 22 | | 36 | 10 | | 37 | 11 | | 38 | 17 | | 39 | 12 | | 40 | 19 | | 41 | 3 | | 42 | 5 | | 43 | 13 | | 44 | 21 | | 45 | 6 | | 46 | 5 | | 47 | 11 | | 48 | 5 | | 49 | 40 |
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| 63.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3979591836734694 | | totalSentences | 98 | | uniqueOpeners | 39 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 96 | | matches | | 0 | "Then she pulled the metal" | | 1 | "Too tall, or with eyes" | | 2 | "Then another looked up." |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 96 | | matches | | 0 | "His olive skin caught the" | | 1 | "She'd been watching The Raven's" | | 2 | "He was connected to them." | | 3 | "She burst out of the" | | 4 | "She ignored it." | | 5 | "She wasn't calling it in" | | 6 | "She kept her breathing measured," | | 7 | "Her mind raced through the" | | 8 | "They emerged onto Camden High" | | 9 | "She slowed, approaching the gap" | | 10 | "Her hand moved to the" | | 11 | "It was ajar." | | 12 | "He'd led her somewhere specific." | | 13 | "She pulled out her radio," | | 14 | "She killed her own torch" | | 15 | "She'd documented impossible things: people" | | 16 | "She checked her baton, her" | | 17 | "She could see perhaps ten" | | 18 | "She took the first step" | | 19 | "Her sharp jaw tightened." |
| | ratio | 0.271 | |
| 69.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 96 | | matches | | 0 | "The rain hammered down in" | | 1 | "Detective Harlow Quinn's shoes slapped" | | 2 | "Tomás Herrera glanced back over" | | 3 | "His olive skin caught the" | | 4 | "Quinn swore under her breath" | | 5 | "She'd been watching The Raven's" | | 6 | "He was connected to them." | | 7 | "The clique of criminals—or worse—that" | | 8 | "The night Morris died." | | 9 | "The alley stank of rotting" | | 10 | "Quinn's hand instinctively touched the" | | 11 | "She burst out of the" | | 12 | "Herrera was booking it north" | | 13 | "Quinn's radio crackled at her" | | 14 | "She ignored it." | | 15 | "This was her case, her" | | 16 | "She wasn't calling it in" | | 17 | "The chase continued through Soho's" | | 18 | "Rain drummed on Quinn's Metropolitan" | | 19 | "She kept her breathing measured," |
| | ratio | 0.781 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 96 | | matches | | 0 | "If he got on a" | | 1 | "By the time she reached" |
| | ratio | 0.021 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 6 | | matches | | 0 | "The rain hammered down in sheets, turning the gutters of Soho into rushing streams that reflected the garish neon of late-night establishments." | | 1 | "She'd been watching The Raven's Nest for three weeks now, documenting the unusual clientele that filtered in and out of that bar with its distinctive green neon…" | | 2 | "Herrera was booking it north toward Camden, weaving between late-night pedestrians who shouted curses as he shouldered past them." | | 3 | "Following leads that her superiors dismissed as the grief-stricken delusions of a detective who'd lost her partner." | | 4 | "She'd documented impossible things: people who vanished into thin air, wounds that healed overnight, evidence that disappeared from locked rooms." | | 5 | "A supernatural black market that moved locations, that dealt in things that shouldn't exist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |