| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1224 | | 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) | |
| 55.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1224 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "glistening" | | 1 | "footsteps" | | 2 | "familiar" | | 3 | "echoed" | | 4 | "silence" | | 5 | "vibrated" | | 6 | "tracing" | | 7 | "weight" |
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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 | 1 | | narrationSentences | 73 | | matches | | |
| 64.58% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1220 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 91.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1187 | | uniqueNames | 19 | | maxNameDensity | 1.18 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 14 | | Raven | 1 | | Nest | 1 | | Tomás | 1 | | Herrera | 12 | | Saint | 1 | | Christopher | 1 | | Soho | 1 | | Glock | 2 | | London | 1 | | St | 1 | | Camden | 3 | | Tube | 1 | | Veil | 3 | | Market | 2 | | Morris | 4 | | Rotherhithe | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Market" | | 9 | "Morris" |
| | places | | 0 | "Detective" | | 1 | "Soho" | | 2 | "London" | | 3 | "St" | | 4 | "Veil" | | 5 | "Rotherhithe" |
| | globalScore | 0.91 | | windowScore | 1 | |
| 70.63% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed thicker than the night should have been, the kind of dark that swallowed light before it had a chance to touch it" | | 1 | "looked like they'd seen something that ha" |
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| 36.07% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.639 | | wordCount | 1220 | | matches | | 0 | "not yet, but her fingers brushed against the familiar grip, a habit worn" | | 1 | "not enough, but enough to know" |
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| 75.34% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 73 | | matches | | 0 | "saw that the" | | 1 | "know that Morris" |
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| 54.95% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 50.83 | | std | 17.4 | | cv | 0.342 | | sampleLengths | | 0 | 81 | | 1 | 71 | | 2 | 60 | | 3 | 48 | | 4 | 56 | | 5 | 16 | | 6 | 29 | | 7 | 53 | | 8 | 65 | | 9 | 44 | | 10 | 58 | | 11 | 50 | | 12 | 69 | | 13 | 54 | | 14 | 61 | | 15 | 8 | | 16 | 57 | | 17 | 58 | | 18 | 74 | | 19 | 43 | | 20 | 50 | | 21 | 51 | | 22 | 23 | | 23 | 41 |
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| 81.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 73 | | matches | | 0 | "been sealed" | | 1 | "been closed" | | 2 | "were faded" | | 3 | "been touched" | | 4 | "was forbidden" |
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| 56.63% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 186 | | matches | | 0 | "was going" | | 1 | "was already moving" | | 2 | "wasn't coming" | | 3 | "was leaving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 73 | | ratio | 0.055 | | matches | | 0 | "Quinn saw his eyes sweep the street once, twice—a medical training that had taught him to assess a scene before entering it." | | 1 | "He glanced back, and she saw the calculation in his eyes—the paramedic assessing the situation, looking for a way to de-escalate, to escape." | | 2 | "She could hear something else, too—a low hum that vibrated through the ground, through the soles of her shoes, through her teeth." | | 3 | "She understood now—a little, not enough, but enough to know that Morris hadn't died by accident." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 50 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.02 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.02 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 16.71 | | std | 9.74 | | cv | 0.583 | | sampleLengths | | 0 | 28 | | 1 | 53 | | 2 | 16 | | 3 | 25 | | 4 | 22 | | 5 | 5 | | 6 | 3 | | 7 | 32 | | 8 | 28 | | 9 | 32 | | 10 | 16 | | 11 | 26 | | 12 | 15 | | 13 | 15 | | 14 | 2 | | 15 | 8 | | 16 | 6 | | 17 | 11 | | 18 | 18 | | 19 | 25 | | 20 | 28 | | 21 | 16 | | 22 | 20 | | 23 | 29 | | 24 | 15 | | 25 | 29 | | 26 | 22 | | 27 | 23 | | 28 | 13 | | 29 | 18 | | 30 | 8 | | 31 | 24 | | 32 | 8 | | 33 | 14 | | 34 | 19 | | 35 | 17 | | 36 | 11 | | 37 | 3 | | 38 | 22 | | 39 | 9 | | 40 | 7 | | 41 | 13 | | 42 | 16 | | 43 | 25 | | 44 | 20 | | 45 | 8 | | 46 | 9 | | 47 | 17 | | 48 | 31 | | 49 | 15 |
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| 50.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3698630136986301 | | totalSentences | 73 | | uniqueOpeners | 27 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Then she stepped onto the" |
| | ratio | 0.014 | |
| 81.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 72 | | matches | | 0 | "He never did." | | 1 | "She let him get half" | | 2 | "He turned down a narrow" | | 3 | "She was already moving, her" | | 4 | "He ducked through a gap" | | 5 | "She landed in a crouch," | | 6 | "She didn't draw it, not" | | 7 | "He glanced back, and she" | | 8 | "She was faster, stronger, and" | | 9 | "They ran past the red" | | 10 | "She'd heard rumors." | | 11 | "She could still hear Herrera's" | | 12 | "She could hear something else," | | 13 | "It wasn't a machine." | | 14 | "It was too organic, too" | | 15 | "Her hand went to her" | | 16 | "She didn't know then." | | 17 | "She understood now—a little, not" | | 18 | "He'd died because he'd found" | | 19 | "He treated the people who" |
| | ratio | 0.347 | |
| 36.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 72 | | matches | | 0 | "Rain needled the back of" | | 1 | "The green neon of The" | | 2 | "Tomás Herrera emerged at 11:47" | | 3 | "The Saint Christopher medallion caught" | | 4 | "Quinn saw his eyes sweep" | | 5 | "He never did." | | 6 | "She let him get half" | | 7 | "The leather watch on her" | | 8 | "Herrera walked with purpose, shoulders" | | 9 | "Quinn followed, her breath a" | | 10 | "He turned down a narrow" | | 11 | "The sound echoed between the" | | 12 | "Herrera's head snapped around, and" | | 13 | "The medallion swung as he" | | 14 | "Quinn shouted, her voice a" | | 15 | "She was already moving, her" | | 16 | "Herrera was fast, but he" | | 17 | "He ducked through a gap" | | 18 | "The alley opened into a" | | 19 | "Herrera vaulted them with surprising" |
| | ratio | 0.847 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "If she went down those" |
| | ratio | 0.014 | |
| 69.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn followed, her breath a white plume in the cold that tasted of diesel and rain." | | 1 | "She was already moving, her body launching into the chase before her mind had finished processing the command." | | 2 | "Herrera was fast, but he moved with the awkward gait of someone who had learned to sprint in crowded hospital corridors, not on rain-slicked streets." | | 3 | "Quinn pushed harder, her lungs burning with the cold air, her legs eating up the distance between them." | | 4 | "The official report said misadventure, a tragic accident, but Quinn had seen the state of his body, the expression on his face, the way his eyes had looked like…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn shouted, her voice a blade through the rain" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |