| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 139 | | tagDensity | 0.151 | | leniency | 0.302 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2325 | | 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) | |
| 78.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2325 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flickered" | | 1 | "pulse" | | 2 | "charm" | | 3 | "comforting" | | 4 | "implication" | | 5 | "glint" | | 6 | "flicked" | | 7 | "trembled" |
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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 | 187 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 187 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 305 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2325 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1431 | | uniqueNames | 6 | | maxNameDensity | 3.35 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 48 | | Camden | 1 | | Tube | 1 | | Kowalski | 1 | | Eva | 21 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 121 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.43 | | wordCount | 2325 | | matches | | 0 | "not with light but with a thin, wet gleam" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 305 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 172 | | mean | 13.52 | | std | 12.62 | | cv | 0.934 | | sampleLengths | | 0 | 17 | | 1 | 58 | | 2 | 22 | | 3 | 3 | | 4 | 23 | | 5 | 29 | | 6 | 49 | | 7 | 4 | | 8 | 10 | | 9 | 44 | | 10 | 8 | | 11 | 19 | | 12 | 29 | | 13 | 1 | | 14 | 32 | | 15 | 4 | | 16 | 9 | | 17 | 1 | | 18 | 5 | | 19 | 51 | | 20 | 10 | | 21 | 4 | | 22 | 4 | | 23 | 4 | | 24 | 33 | | 25 | 6 | | 26 | 10 | | 27 | 4 | | 28 | 4 | | 29 | 9 | | 30 | 6 | | 31 | 16 | | 32 | 15 | | 33 | 7 | | 34 | 17 | | 35 | 11 | | 36 | 18 | | 37 | 1 | | 38 | 34 | | 39 | 3 | | 40 | 2 | | 41 | 3 | | 42 | 1 | | 43 | 4 | | 44 | 9 | | 45 | 5 | | 46 | 5 | | 47 | 29 | | 48 | 2 | | 49 | 5 |
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| 92.13% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 187 | | matches | | 0 | "been chained" | | 1 | "been dragged" | | 2 | "been fastened" | | 3 | "been removed" | | 4 | "was clenched" | | 5 | "been made" | | 6 | "been made" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 239 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 305 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1433 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.013258897418004187 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006978367062107466 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 305 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 305 | | mean | 7.62 | | std | 5.16 | | cv | 0.677 | | sampleLengths | | 0 | 17 | | 1 | 12 | | 2 | 17 | | 3 | 14 | | 4 | 15 | | 5 | 7 | | 6 | 15 | | 7 | 3 | | 8 | 4 | | 9 | 9 | | 10 | 10 | | 11 | 15 | | 12 | 14 | | 13 | 12 | | 14 | 12 | | 15 | 14 | | 16 | 11 | | 17 | 4 | | 18 | 10 | | 19 | 14 | | 20 | 9 | | 21 | 21 | | 22 | 8 | | 23 | 15 | | 24 | 4 | | 25 | 6 | | 26 | 23 | | 27 | 1 | | 28 | 9 | | 29 | 23 | | 30 | 4 | | 31 | 4 | | 32 | 5 | | 33 | 1 | | 34 | 5 | | 35 | 5 | | 36 | 16 | | 37 | 2 | | 38 | 9 | | 39 | 13 | | 40 | 6 | | 41 | 5 | | 42 | 5 | | 43 | 4 | | 44 | 4 | | 45 | 4 | | 46 | 6 | | 47 | 12 | | 48 | 10 | | 49 | 5 |
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| 46.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.23934426229508196 | | totalSentences | 305 | | uniqueOpeners | 73 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 171 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 171 | | matches | | 0 | "Its tiled walls sweated beneath" | | 1 | "He held a paper cup" | | 2 | "He nodded at the body" | | 3 | "Her curly red hair had" | | 4 | "His right hand rested palm-up," | | 5 | "She studied the man’s hands," | | 6 | "He pointed to the folding" | | 7 | "He said nothing." | | 8 | "It looked like soot." | | 9 | "She did not touch them." | | 10 | "She turned back to the" | | 11 | "His left heel carried no" | | 12 | "His jacketless arms lay close" | | 13 | "She pointed at the floor" | | 14 | "She crouched by the victim’s" | | 15 | "She used the penlight from" | | 16 | "She eased the hand open" | | 17 | "Its tip aimed at the" | | 18 | "He had been walking toward" | | 19 | "His eyes flicked to the" |
| | ratio | 0.164 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 160 | | totalSentences | 171 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "The station had not appeared" | | 2 | "Its tiled walls sweated beneath" | | 3 | "A train track vanished into" | | 4 | "A uniformed constable waited beside" | | 5 | "He held a paper cup" | | 6 | "Quinn checked her watch." | | 7 | "The worn leather strap had" | | 8 | "He nodded at the body" | | 9 | "Quinn took in the platform" | | 10 | "A white chalk line circled" | | 11 | "A set of small brass" | | 12 | "The nearest lamp flickered, casting" | | 13 | "A woman stood by one" | | 14 | "Eva Kowalski wore round glasses" | | 15 | "Her curly red hair had" | | 16 | "the constable replied" | | 17 | "Quinn’s gaze shifted to the" | | 18 | "Someone had drawn a shape" | | 19 | "Eva leaned in, then stopped" |
| | ratio | 0.936 | |
| 29.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 171 | | matches | | 0 | "Whoever had opened the circle" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "His jacketless arms lay close to his sides, as if someone had placed them there." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 18 | | fancyCount | 2 | | fancyTags | | 0 | "Eva warned (warn)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 139 | | tagDensity | 0.129 | | leniency | 0.259 | | rawRatio | 0.111 | | effectiveRatio | 0.029 | |