| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1292 | | 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) | |
| 49.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1292 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "measured" | | 1 | "chill" | | 2 | "pulse" | | 3 | "flickered" | | 4 | "weight" | | 5 | "gloom" | | 6 | "echoed" | | 7 | "pulsed" | | 8 | "stomach" | | 9 | "vibrated" | | 10 | "scanning" | | 11 | "wavering" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 76 | | matches | (empty) | |
| 86.47% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1289 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.23% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1137 | | uniqueNames | 16 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 1 | | Quinn | 12 | | Met | 1 | | Tomás | 1 | | Herrera | 10 | | Victorian | 1 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Morris | 3 | | Tube | 1 | | Seville | 1 | | London | 3 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Met" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Raven" | | 7 | "Silas" | | 8 | "Morris" | | 9 | "Saint" | | 10 | "Christopher" |
| | places | | | globalScore | 0.972 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.776 | | wordCount | 1289 | | matches | | 0 | "not by fluorescent tubes, but by glass jars hanging from the tiled walls" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 36.83 | | std | 26.38 | | cv | 0.716 | | sampleLengths | | 0 | 77 | | 1 | 43 | | 2 | 9 | | 3 | 40 | | 4 | 16 | | 5 | 2 | | 6 | 114 | | 7 | 61 | | 8 | 10 | | 9 | 3 | | 10 | 53 | | 11 | 39 | | 12 | 16 | | 13 | 47 | | 14 | 32 | | 15 | 32 | | 16 | 14 | | 17 | 33 | | 18 | 13 | | 19 | 87 | | 20 | 38 | | 21 | 13 | | 22 | 22 | | 23 | 17 | | 24 | 51 | | 25 | 10 | | 26 | 76 | | 27 | 7 | | 28 | 25 | | 29 | 56 | | 30 | 36 | | 31 | 82 | | 32 | 36 | | 33 | 52 | | 34 | 27 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 86.36% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 176 | | matches | | 0 | "were swallowing" | | 1 | "was spinning" | | 2 | "was weighing" |
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| 75.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 85 | | ratio | 0.024 | | matches | | 0 | "Whatever that little clique of his was moving through Silas’s bar—narcotics, stolen pharmaceuticals, illegal weapons—Herrera was the mule tonight." | | 1 | "From below rose a low, thrumming murmur—hundreds of voices bartering in languages that scraped against Quinn's ears like gravel." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.0278503046127067 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009573542210617928 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 15.16 | | std | 8.07 | | cv | 0.532 | | sampleLengths | | 0 | 16 | | 1 | 24 | | 2 | 11 | | 3 | 4 | | 4 | 22 | | 5 | 10 | | 6 | 14 | | 7 | 19 | | 8 | 9 | | 9 | 40 | | 10 | 16 | | 11 | 2 | | 12 | 3 | | 13 | 11 | | 14 | 14 | | 15 | 10 | | 16 | 25 | | 17 | 19 | | 18 | 32 | | 19 | 12 | | 20 | 8 | | 21 | 18 | | 22 | 23 | | 23 | 10 | | 24 | 3 | | 25 | 26 | | 26 | 16 | | 27 | 11 | | 28 | 9 | | 29 | 7 | | 30 | 23 | | 31 | 16 | | 32 | 21 | | 33 | 18 | | 34 | 8 | | 35 | 32 | | 36 | 20 | | 37 | 12 | | 38 | 14 | | 39 | 3 | | 40 | 5 | | 41 | 13 | | 42 | 12 | | 43 | 13 | | 44 | 3 | | 45 | 22 | | 46 | 15 | | 47 | 9 | | 48 | 26 | | 49 | 12 |
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| 93.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5764705882352941 | | totalSentences | 85 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 76 | | matches | | 0 | "More white noise." | | 1 | "Instead, a sound like a" | | 2 | "Then she saw Morris’s empty" |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 76 | | matches | | 0 | "She ignored the chill." | | 1 | "He clutched a leather medical" | | 2 | "She released the button and" | | 3 | "She didn't need the cavalry" | | 4 | "She had clocked Herrera slipping" | | 5 | "His jacket sleeve snagged on" | | 6 | "He didn't slow down to" | | 7 | "His warm brown eyes went" | | 8 | "He kept his weight on" | | 9 | "He fumbled in his pocket" | | 10 | "He pulled out a flat," | | 11 | "Her breath caught in her" | | 12 | "He threw himself backwards through" | | 13 | "he shouted as the darkness" | | 14 | "Her stomach dropped, cold sweat" | | 15 | "She tumbled onto slick, dry" | | 16 | "She slid the baton back" |
| | ratio | 0.224 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 76 | | matches | | 0 | "Water sheeted off the corrugated" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "She ignored the chill." | | 3 | "The metal rattled violently before" | | 4 | "He clutched a leather medical" | | 5 | "Quinn thumbed the transmit button" | | 6 | "Static hissed back in her" | | 7 | "The storm and the Victorian" | | 8 | "She released the button and" | | 9 | "She didn't need the cavalry" | | 10 | "She had clocked Herrera slipping" | | 11 | "Whatever that little clique of" | | 12 | "His jacket sleeve snagged on" | | 13 | "The fabric tore, exposing olive" | | 14 | "He didn't slow down to" | | 15 | "Quinn vaulted a fallen wooden" | | 16 | "The alley was a dead" | | 17 | "Herrera backed against the iron" | | 18 | "Rain dripped from his nose" | | 19 | "His warm brown eyes went" |
| | ratio | 0.711 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | 0 | "Even in the gloom, Quinn" |
| | ratio | 0.013 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn kept her stride long and measured, her boots smashing through puddles that reflected the sickly orange glow of the streetlamps above." | | 1 | "From below rose a low, thrumming murmur—hundreds of voices bartering in languages that scraped against Quinn's ears like gravel." | | 2 | "At the nearest stall, a vendor with eyes that reflected the green firelight like a cat's was weighing out silver powder on brass scales, while a customer counte…" | | 3 | "Behind her, the iron gate was solid once more, an unbroken wall of rust blocking the way back to the rain and the world that made sense." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 12 | | tagDensity | 0.083 | | leniency | 0.167 | | rawRatio | 1 | | effectiveRatio | 0.167 | |