| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.75 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1150 | | 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) | |
| 52.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1150 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "glint" | | 2 | "traced" | | 3 | "familiar" | | 4 | "weight" | | 5 | "measured" | | 6 | "standard" | | 7 | "scanned" | | 8 | "flickered" |
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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 | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 119 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1150 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1123 | | uniqueNames | 14 | | maxNameDensity | 2.05 | | worstName | "Tomás" | | maxWindowNameDensity | 4 | | worstWindowName | "Tomás" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 21 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 23 | | Morris | 1 | | Camden | 1 | | Rain | 3 | | Water | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" | | 7 | "Morris" | | 8 | "Rain" | | 9 | "Water" |
| | places | | | globalScore | 0.476 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 26.09% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.739 | | wordCount | 1150 | | matches | | 0 | "Neither Quinn nor" | | 1 | "Neither man nor" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 59.30% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 57.5 | | std | 20.56 | | cv | 0.358 | | sampleLengths | | 0 | 83 | | 1 | 55 | | 2 | 32 | | 3 | 46 | | 4 | 79 | | 5 | 21 | | 6 | 61 | | 7 | 73 | | 8 | 59 | | 9 | 60 | | 10 | 55 | | 11 | 57 | | 12 | 17 | | 13 | 59 | | 14 | 55 | | 15 | 81 | | 16 | 57 | | 17 | 90 | | 18 | 84 | | 19 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 186 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 120 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1125 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 21 | | adverbRatio | 0.018666666666666668 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0035555555555555557 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 9.58 | | std | 3.92 | | cv | 0.409 | | sampleLengths | | 0 | 21 | | 1 | 15 | | 2 | 15 | | 3 | 10 | | 4 | 6 | | 5 | 16 | | 6 | 9 | | 7 | 12 | | 8 | 12 | | 9 | 11 | | 10 | 11 | | 11 | 16 | | 12 | 16 | | 13 | 4 | | 14 | 5 | | 15 | 12 | | 16 | 9 | | 17 | 10 | | 18 | 6 | | 19 | 14 | | 20 | 4 | | 21 | 14 | | 22 | 8 | | 23 | 9 | | 24 | 16 | | 25 | 14 | | 26 | 13 | | 27 | 8 | | 28 | 4 | | 29 | 7 | | 30 | 15 | | 31 | 3 | | 32 | 9 | | 33 | 23 | | 34 | 5 | | 35 | 17 | | 36 | 9 | | 37 | 8 | | 38 | 10 | | 39 | 9 | | 40 | 5 | | 41 | 10 | | 42 | 6 | | 43 | 12 | | 44 | 7 | | 45 | 9 | | 46 | 10 | | 47 | 8 | | 48 | 7 | | 49 | 8 |
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| 50.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.325 | | totalSentences | 120 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 119 | | matches | | 0 | "His short curly dark brown" | | 1 | "Her five foot nine frame" | | 2 | "She closed the gap by" | | 3 | "His voice echoed against brick" | | 4 | "She sidestepped a group of" | | 5 | "She kept the distance to" | | 6 | "His warm brown eyes held" | | 7 | "Her own brown eyes marked" | | 8 | "She avoided a stack of" | | 9 | "She gained three steps on" | | 10 | "She recalled the last time" | | 11 | "Her left wrist bore no" | | 12 | "She measured the angle of" | | 13 | "She scanned the visible portion" | | 14 | "She placed one boot on" | | 15 | "Her right hand stayed free" | | 16 | "She descended one more level" |
| | ratio | 0.143 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 113 | | totalSentences | 119 | | matches | | 0 | "Water soaked her coat and" | | 1 | "Brown eyes tracked the man" | | 2 | "His short curly dark brown" | | 3 | "Olive skin gleamed under stray" | | 4 | "Tomás Herrera bolted left at" | | 5 | "Her five foot nine frame" | | 6 | "The leather watch on her" | | 7 | "She closed the gap by" | | 8 | "Tomás pressed a hand to" | | 9 | "The Saint Christopher medallion bounced" | | 10 | "Tomás shouted the words over" | | 11 | "His voice echoed against brick" | | 12 | "Quinn lengthened her stride." | | 13 | "Pavement slicked beneath her boots." | | 14 | "She sidestepped a group of" | | 15 | "Water sprayed from their steps" | | 16 | "A taxi horn blared as" | | 17 | "Tires carved arcs through standing" | | 18 | "Tomás reached the end of" | | 19 | "Quinn followed without pause." |
| | ratio | 0.95 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 2 | | matches | | 0 | "Any path after him meant crossing vendors who sold goods beyond standard criminal records." | | 1 | "The murmurs below carried an edge that echoed the unanswered questions around her former partner's death." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Tomás shouted (shout)" | | 1 | "Tomás spoke (speak)" |
| | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |