| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.233 | | leniency | 0.467 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1768 | | 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) | |
| 71.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1768 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "pulse" | | 1 | "measured" | | 2 | "churned" | | 3 | "footsteps" | | 4 | "flickered" | | 5 | "flicked" | | 6 | "weight" | | 7 | "depths" | | 8 | "familiar" |
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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 | 176 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 176 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 199 | | 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 | 1766 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 55.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1641 | | uniqueNames | 9 | | maxNameDensity | 1.89 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 31 | | Raven | 3 | | Nest | 3 | | Underground | 1 | | Morris | 4 | | Camden | 1 | | Don | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.555 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 127 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.566 | | wordCount | 1766 | | matches | | 0 | "Not the case file or the unanswered questions, but the last ordinary thing he’d said to her: Don’t let them dec" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 199 | | matches | | 0 | "learned that time" | | 1 | "heard that sound" | | 2 | "show that they" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 91 | | mean | 19.41 | | std | 19.54 | | cv | 1.007 | | sampleLengths | | 0 | 11 | | 1 | 41 | | 2 | 1 | | 3 | 52 | | 4 | 8 | | 5 | 70 | | 6 | 56 | | 7 | 13 | | 8 | 6 | | 9 | 20 | | 10 | 3 | | 11 | 41 | | 12 | 1 | | 13 | 52 | | 14 | 39 | | 15 | 40 | | 16 | 37 | | 17 | 17 | | 18 | 7 | | 19 | 31 | | 20 | 6 | | 21 | 7 | | 22 | 7 | | 23 | 43 | | 24 | 8 | | 25 | 32 | | 26 | 8 | | 27 | 11 | | 28 | 57 | | 29 | 12 | | 30 | 13 | | 31 | 3 | | 32 | 91 | | 33 | 6 | | 34 | 55 | | 35 | 4 | | 36 | 35 | | 37 | 6 | | 38 | 6 | | 39 | 4 | | 40 | 2 | | 41 | 12 | | 42 | 37 | | 43 | 6 | | 44 | 18 | | 45 | 1 | | 46 | 3 | | 47 | 54 | | 48 | 52 | | 49 | 30 |
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| 91.31% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 176 | | matches | | 0 | "was measured" | | 1 | "been found" | | 2 | "been abandoned" | | 3 | "been scraped" | | 4 | "was hidden" | | 5 | "allowed" | | 6 | "were supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 300 | | matches | | 0 | "was being" | | 1 | "was limping" | | 2 | "was reaching" | | 3 | "wasn’t watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 199 | | ratio | 0.01 | | matches | | 0 | "Voices murmured—many of them, gathered close together." | | 1 | "The crowd drew back—not enough to make a path, just enough to show that they had noticed." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1649 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.022437841115827774 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0030321406913280777 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 199 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 199 | | mean | 8.87 | | std | 5.87 | | cv | 0.662 | | sampleLengths | | 0 | 11 | | 1 | 32 | | 2 | 9 | | 3 | 1 | | 4 | 10 | | 5 | 11 | | 6 | 17 | | 7 | 14 | | 8 | 3 | | 9 | 5 | | 10 | 11 | | 11 | 15 | | 12 | 12 | | 13 | 14 | | 14 | 3 | | 15 | 15 | | 16 | 11 | | 17 | 16 | | 18 | 19 | | 19 | 5 | | 20 | 5 | | 21 | 13 | | 22 | 6 | | 23 | 6 | | 24 | 6 | | 25 | 5 | | 26 | 3 | | 27 | 3 | | 28 | 21 | | 29 | 13 | | 30 | 4 | | 31 | 3 | | 32 | 1 | | 33 | 4 | | 34 | 7 | | 35 | 17 | | 36 | 24 | | 37 | 11 | | 38 | 7 | | 39 | 21 | | 40 | 7 | | 41 | 15 | | 42 | 8 | | 43 | 2 | | 44 | 8 | | 45 | 7 | | 46 | 13 | | 47 | 17 | | 48 | 7 | | 49 | 10 |
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| 42.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.2562814070351759 | | totalSentences | 199 | | uniqueOpeners | 51 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 164 | | matches | | 0 | "Then he ran." | | 1 | "Somewhere below, a train clattered" | | 2 | "Somewhere in its depths, something" | | 3 | "Then she looked at the" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 164 | | matches | | 0 | "She pivoted on her heel" | | 1 | "Her shoes skidded." | | 2 | "She caught herself, pushed on." | | 3 | "He knew he was being" | | 4 | "He glanced back and saw" | | 5 | "She ran harder." | | 6 | "They tore through narrow streets" | | 7 | "His left leg buckled." | | 8 | "He looked back again." | | 9 | "His face was pale beneath" | | 10 | "He shoved through a gap" | | 11 | "She glanced at it without" | | 12 | "She yanked free and followed," | | 13 | "She caught the edge with" | | 14 | "She took the stairs two" | | 15 | "Her breath fogged in the" | | 16 | "She lowered her weapon a" | | 17 | "She’d heard that sound before," | | 18 | "She’d spent three years trying" | | 19 | "Her gun felt suddenly ridiculous" |
| | ratio | 0.238 | |
| 48.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 135 | | totalSentences | 164 | | matches | | 0 | "Detective Harlow Quinn caught a" | | 1 | "She pivoted on her heel" | | 2 | "The word vanished beneath the" | | 3 | "A bus shouldered through the" | | 4 | "The suspect darted between two" | | 5 | "Quinn followed, one hand braced" | | 6 | "Her shoes skidded." | | 7 | "She caught herself, pushed on." | | 8 | "The alley smelled of bins," | | 9 | "Quinn’s breath was steady, but" | | 10 | "This was neither." | | 11 | "This was the cold, precise" | | 12 | "The suspect had been at" | | 13 | "Quinn had watched him leave" | | 14 | "The bartender had offered nothing." | | 15 | "The customers had offered less." | | 16 | "He knew he was being" | | 17 | "Quinn hit the pavement behind" | | 18 | "He glanced back and saw" | | 19 | "She ran harder." |
| | ratio | 0.823 | |
| 91.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 164 | | matches | | 0 | "If he reached the tunnel," | | 1 | "If she lowered her weapon," | | 2 | "If she fired, the crowd" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 3 | | matches | | 0 | "Her training offered answers for rooms with doors, suspects with hands, witnesses who understood the law." | | 1 | "It had nothing to say about a station that shouldn’t exist, about a market beneath Camden where things in jars watched her pass, about the taste of old pennies …" | | 2 | "Morris’s handwriting, narrow and urgent, beside a list of places that were supposed to be myths." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "he whispered (whisper)" |
| | dialogueSentences | 30 | | tagDensity | 0.233 | | leniency | 0.467 | | rawRatio | 0.143 | | effectiveRatio | 0.067 | |