| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.433 | | leniency | 0.867 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1314 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 80.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1314 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "pulse" | | 2 | "lilt" | | 3 | "flicker" |
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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 | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1314 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 75.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1009 | | uniqueNames | 15 | | maxNameDensity | 1.49 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Herrera | 15 | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Brewer | 1 | | Street | 1 | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Seville | 1 | | English | 1 | | Henderson | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Henderson" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Brewer" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Seville" |
| | globalScore | 0.757 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1314 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 38.65 | | std | 23.61 | | cv | 0.611 | | sampleLengths | | 0 | 71 | | 1 | 87 | | 2 | 28 | | 3 | 54 | | 4 | 24 | | 5 | 45 | | 6 | 16 | | 7 | 67 | | 8 | 44 | | 9 | 40 | | 10 | 18 | | 11 | 41 | | 12 | 5 | | 13 | 36 | | 14 | 13 | | 15 | 60 | | 16 | 2 | | 17 | 44 | | 18 | 74 | | 19 | 12 | | 20 | 7 | | 21 | 28 | | 22 | 35 | | 23 | 15 | | 24 | 52 | | 25 | 74 | | 26 | 37 | | 27 | 89 | | 28 | 7 | | 29 | 29 | | 30 | 63 | | 31 | 41 | | 32 | 32 | | 33 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 79 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 179 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1016 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.014763779527559055 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005905511811023622 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 13.69 | | std | 10.2 | | cv | 0.745 | | sampleLengths | | 0 | 12 | | 1 | 21 | | 2 | 38 | | 3 | 7 | | 4 | 23 | | 5 | 3 | | 6 | 4 | | 7 | 9 | | 8 | 41 | | 9 | 4 | | 10 | 10 | | 11 | 14 | | 12 | 24 | | 13 | 13 | | 14 | 6 | | 15 | 3 | | 16 | 8 | | 17 | 14 | | 18 | 6 | | 19 | 1 | | 20 | 3 | | 21 | 20 | | 22 | 8 | | 23 | 4 | | 24 | 13 | | 25 | 16 | | 26 | 12 | | 27 | 10 | | 28 | 23 | | 29 | 22 | | 30 | 14 | | 31 | 7 | | 32 | 4 | | 33 | 19 | | 34 | 29 | | 35 | 11 | | 36 | 8 | | 37 | 3 | | 38 | 7 | | 39 | 27 | | 40 | 14 | | 41 | 5 | | 42 | 11 | | 43 | 25 | | 44 | 6 | | 45 | 4 | | 46 | 3 | | 47 | 33 | | 48 | 27 | | 49 | 2 |
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| 53.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3645833333333333 | | totalSentences | 96 | | uniqueOpeners | 35 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Then the moment passed, and" |
| | ratio | 0.014 | |
| 63.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 74 | | matches | | 0 | "She'd been tracking him for" | | 1 | "She cared that Herrera had" | | 2 | "He led her through Soho," | | 3 | "She kept her distance, using" | | 4 | "Her phone buzzed in her" | | 5 | "She ignored it." | | 6 | "She stepped inside." | | 7 | "She checked her service weapon," | | 8 | "He didn't look up when" | | 9 | "He didn't need to." | | 10 | "She'd heard his footsteps on" | | 11 | "His voice carried the soft" | | 12 | "He finally met her eyes," | | 13 | "He laughed, a short, broken" | | 14 | "She hadn't touched it." | | 15 | "He nodded toward the pale-eyed" | | 16 | "She'd read the file on" | | 17 | "She'd never had cause to" | | 18 | "She stood, and he rose" | | 19 | "He moved toward the back" |
| | ratio | 0.392 | |
| 20.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 74 | | matches | | 0 | "The rain fell in sheets" | | 1 | "Detective Harlow Quinn pulled her" | | 2 | "Tomás Herrera moved with the" | | 3 | "She'd been tracking him for" | | 4 | "The tip came from a" | | 5 | "That was the phrase." | | 6 | "Quinn didn't care about the" | | 7 | "She cared that Herrera had" | | 8 | "The traffic light changed." | | 9 | "Herrera crossed against it, not" | | 10 | "Quinn followed, her shoes slapping" | | 11 | "He led her through Soho," | | 12 | "She kept her distance, using" | | 13 | "Her phone buzzed in her" | | 14 | "She ignored it." | | 15 | "Rules of pursuit: eyes on" | | 16 | "Herrera ducked into an alley" | | 17 | "Quinn reached the entrance, slowed," | | 18 | "She stepped inside." | | 19 | "The alley opened into a" |
| | ratio | 0.878 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "Tomás Herrera moved with the easy stride of a man who'd spent years running from things, his dark coat absorbing the darkness, the Saint Christopher medallion c…" | | 1 | "Quinn followed, her shoes slapping through puddles that swallowed the sound of her footsteps." | | 2 | "The back room was small, windowless, lit by a single bulb that swung slightly in a draft Quinn couldn't feel." | | 3 | "It was cold, colder than it should have been, and it left a faint grey mark on her palm that faded as she watched." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "people spoke (speak)" |
| | dialogueSentences | 30 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.4 | | effectiveRatio | 0.133 | |