| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 80.64% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1033 | | totalAiIsmAdverbs | 4 | | 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) | |
| 46.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1033 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "flickered" | | 1 | "echoed" | | 2 | "could feel" | | 3 | "pulse" | | 4 | "flicker" | | 5 | "echoing" | | 6 | "pulsed" | | 7 | "whisper" | | 8 | "glinting" | | 9 | "reminder" | | 10 | "flicked" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 21 | | totalWords | 1020 | | ratio | 0.021 | | matches | | 0 | "The Raven’s Nest" | | 1 | "\"For the dreams you don’t remember.\"" | | 2 | "\"For the one who hunts you,\"" | | 3 | "\"It will show you their fear.\"" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 955 | | uniqueNames | 14 | | maxNameDensity | 1.47 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Harlow | 1 | | Quinn | 14 | | London | 2 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Met | 1 | | Morris | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Camden" | | 4 | "Market" | | 5 | "Morris" | | 6 | "Herrera" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Tomás" |
| | places | | | globalScore | 0.767 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | 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 | 1020 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 28.33 | | std | 22.09 | | cv | 0.78 | | sampleLengths | | 0 | 78 | | 1 | 12 | | 2 | 60 | | 3 | 43 | | 4 | 72 | | 5 | 57 | | 6 | 33 | | 7 | 26 | | 8 | 5 | | 9 | 63 | | 10 | 35 | | 11 | 7 | | 12 | 73 | | 13 | 19 | | 14 | 56 | | 15 | 8 | | 16 | 32 | | 17 | 40 | | 18 | 2 | | 19 | 38 | | 20 | 14 | | 21 | 8 | | 22 | 5 | | 23 | 52 | | 24 | 16 | | 25 | 12 | | 26 | 12 | | 27 | 30 | | 28 | 19 | | 29 | 18 | | 30 | 10 | | 31 | 27 | | 32 | 12 | | 33 | 9 | | 34 | 13 | | 35 | 4 |
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| 90.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 95 | | matches | | 0 | "were gone" | | 1 | "been transformed" | | 2 | "were raised" | | 3 | "were locked" |
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| 69.28% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 153 | | matches | | 0 | "was heading" | | 1 | "was running" | | 2 | "wasn’t leaving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 105 | | ratio | 0.057 | | matches | | 0 | "The alley reeked of stale beer and something fouler—rotting meat, maybe, or the kind of damp that clung to places where light never reached." | | 1 | "The suspect’s coat was unmistakable—dark, threadbare, the kind of thing you’d wear if you didn’t want to be remembered." | | 2 | "She’d heard the whispers, of course—every cop in the Met had." | | 3 | "Then, a sound—voices, murmuring, the clink of glass." | | 4 | "A stall blocked her path, its vendor—a gaunt man with fingers like spider legs—holding out a silver locket." | | 5 | "But the suspect—no, Tomás—was here." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 968 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.023760330578512397 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007231404958677686 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 9.71 | | std | 5.79 | | cv | 0.596 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 18 | | 3 | 19 | | 4 | 4 | | 5 | 2 | | 6 | 6 | | 7 | 24 | | 8 | 10 | | 9 | 2 | | 10 | 8 | | 11 | 16 | | 12 | 6 | | 13 | 10 | | 14 | 16 | | 15 | 7 | | 16 | 4 | | 17 | 22 | | 18 | 16 | | 19 | 15 | | 20 | 19 | | 21 | 19 | | 22 | 3 | | 23 | 3 | | 24 | 11 | | 25 | 8 | | 26 | 4 | | 27 | 9 | | 28 | 15 | | 29 | 14 | | 30 | 4 | | 31 | 15 | | 32 | 11 | | 33 | 5 | | 34 | 21 | | 35 | 17 | | 36 | 14 | | 37 | 8 | | 38 | 3 | | 39 | 6 | | 40 | 8 | | 41 | 11 | | 42 | 8 | | 43 | 2 | | 44 | 7 | | 45 | 6 | | 46 | 14 | | 47 | 15 | | 48 | 14 | | 49 | 24 |
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| 45.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3047619047619048 | | totalSentences | 105 | | uniqueOpeners | 32 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 89 | | matches | | 0 | "Then they were gone, swallowed" | | 1 | "Then, a sound—voices, murmuring, the" | | 2 | "Then, ahead, the suspect ducked" | | 3 | "Then, slowly, they turned." | | 4 | "Then he reached into his" | | 5 | "Then, slowly, he pulled out" | | 6 | "Then the curtain parted." |
| | ratio | 0.079 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 89 | | matches | | 0 | "Her breath came in sharp," | | 1 | "She didn’t call out." | | 2 | "She could feel it." | | 3 | "She’d heard the whispers, of" | | 4 | "She hesitated for half a" | | 5 | "He’d never come back." | | 6 | "Her boots clanged against the" | | 7 | "She turned, her breath steady" | | 8 | "She rounded a corner and" | | 9 | "She wasn’t in London anymore." | | 10 | "She ignored them." | | 11 | "she said, her voice cutting" | | 12 | "It wasn’t who she expected." | | 13 | "His hands were raised, but" | | 14 | "he said, his voice steady" | | 15 | "She stepped forward, her baton" | | 16 | "she said, her voice low" | | 17 | "His eyes were locked on" |
| | ratio | 0.202 | |
| 61.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 89 | | matches | | 0 | "The neon sign above *The" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "Her breath came in sharp," | | 3 | "She didn’t call out." | | 4 | "The suspect knew she was" | | 5 | "The alley reeked of stale" | | 6 | "Quinn’s fingers twitched near her" | | 7 | "The suspect was fast, but" | | 8 | "A crash echoed from the" | | 9 | "A bin toppled, its contents" | | 10 | "Quinn vaulted over a pile" | | 11 | "The suspect was heading for" | | 12 | "She could feel it." | | 13 | "The alley spilled into a" | | 14 | "The suspect cut left, weaving" | | 15 | "Quinn shouldered past them, her" | | 16 | "The suspect’s coat was unmistakable—dark," | | 17 | "Quinn’s pulse spiked." | | 18 | "The Veil Market." | | 19 | "She’d heard the whispers, of" |
| | ratio | 0.798 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 2 | | matches | | 0 | "Bottles of swirling liquid lined the shelves, their contents shifting colors as she watched." | | 1 | "Quinn pushed forward, her shoulders brushing against strangers who turned to stare." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, her voice cutting through the hum of the market" | | 1 | "he said, his voice steady despite the chase" | | 2 | "she said, her voice low" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.25 | | effectiveRatio | 0.143 | |