| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1128 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "carefully" | | 1 | "quickly" | | 2 | "very" |
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| 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) | |
| 37.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1128 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "fleeting" | | 1 | "scanned" | | 2 | "glint" | | 3 | "throb" | | 4 | "hulking" | | 5 | "could feel" | | 6 | "stomach" | | 7 | "familiar" | | 8 | "echo" | | 9 | "vibrated" | | 10 | "echoing" | | 11 | "pulsed" | | 12 | "shimmered" | | 13 | "navigating" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 128 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 128 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1020 | | uniqueNames | 12 | | maxNameDensity | 0.98 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Detective | 2 | | Harlow | 2 | | Quinn | 10 | | Herrera | 9 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Silas | 8 | | Frustration | 1 | | Morris | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Silas" | | 6 | "Frustration" | | 7 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 1 | | matches | | 0 | "the back wall, seemingly crammed with antiqu" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1127 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 136 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 28.18 | | std | 16.03 | | cv | 0.569 | | sampleLengths | | 0 | 72 | | 1 | 46 | | 2 | 35 | | 3 | 5 | | 4 | 35 | | 5 | 8 | | 6 | 46 | | 7 | 37 | | 8 | 36 | | 9 | 11 | | 10 | 19 | | 11 | 27 | | 12 | 26 | | 13 | 20 | | 14 | 13 | | 15 | 43 | | 16 | 19 | | 17 | 45 | | 18 | 3 | | 19 | 28 | | 20 | 15 | | 21 | 9 | | 22 | 10 | | 23 | 12 | | 24 | 14 | | 25 | 41 | | 26 | 33 | | 27 | 37 | | 28 | 32 | | 29 | 46 | | 30 | 39 | | 31 | 23 | | 32 | 5 | | 33 | 16 | | 34 | 45 | | 35 | 33 | | 36 | 13 | | 37 | 24 | | 38 | 60 | | 39 | 46 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 5.07% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 171 | | matches | | 0 | "wasn’t exaggerating" | | 1 | "was talking" | | 2 | "was walking" | | 3 | "was doing" | | 4 | "was selling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 136 | | ratio | 0.015 | | matches | | 0 | "She scanned the alley – a claustrophobic space choked with overflowing bins and ghostly shadows." | | 1 | "She ran a hand along the spines, noting the titles – ancient geographies, forgotten folklore." |
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| 88.24% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 131 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.05343511450381679 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.015267175572519083 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 8.29 | | std | 5.43 | | cv | 0.656 | | sampleLengths | | 0 | 12 | | 1 | 6 | | 2 | 20 | | 3 | 5 | | 4 | 9 | | 5 | 1 | | 6 | 7 | | 7 | 12 | | 8 | 14 | | 9 | 10 | | 10 | 12 | | 11 | 1 | | 12 | 7 | | 13 | 2 | | 14 | 16 | | 15 | 7 | | 16 | 12 | | 17 | 5 | | 18 | 5 | | 19 | 15 | | 20 | 1 | | 21 | 1 | | 22 | 4 | | 23 | 1 | | 24 | 1 | | 25 | 7 | | 26 | 4 | | 27 | 4 | | 28 | 3 | | 29 | 2 | | 30 | 13 | | 31 | 9 | | 32 | 5 | | 33 | 14 | | 34 | 17 | | 35 | 5 | | 36 | 11 | | 37 | 4 | | 38 | 15 | | 39 | 5 | | 40 | 16 | | 41 | 9 | | 42 | 2 | | 43 | 5 | | 44 | 14 | | 45 | 12 | | 46 | 15 | | 47 | 14 | | 48 | 12 | | 49 | 20 |
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| 53.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.39705882352941174 | | totalSentences | 136 | | uniqueOpeners | 54 | |
| 91.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 109 | | matches | | 0 | "Then she saw it." | | 1 | "Mostly found smoke and liars." | | 2 | "Then, she found it." |
| | ratio | 0.028 | |
| 80.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 109 | | matches | | 0 | "She tasted grit and exhaust" | | 1 | "She’d lost him twice already." | | 2 | "Her soaked jacket clung to" | | 3 | "He’d ditched the cab a" | | 4 | "She rounded a corner, nearly" | | 5 | "she barked, not bothering with" | | 6 | "She scanned the alley –" | | 7 | "She checked her radio." | | 8 | "She’d paid it a visit" | | 9 | "She pushed open the door," | | 10 | "She moved towards the bar," | | 11 | "she said, her voice cutting" | | 12 | "His tone was flat, devoid" | | 13 | "She let the threat hang" | | 14 | "He returned to polishing glasses" | | 15 | "She’d reached a dead end." | | 16 | "She started to turn away," | | 17 | "She walked toward the bookshelf," | | 18 | "She ran a hand along" | | 19 | "She pressed it." |
| | ratio | 0.349 | |
| 65.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 109 | | matches | | 0 | "Rain lashed against Detective Harlow" | | 1 | "She tasted grit and exhaust" | | 2 | "She’d lost him twice already." | | 3 | "A former paramedic, conveniently unlicensed," | | 4 | "That’s how the tip had" | | 5 | "Quinn lengthened her stride, the" | | 6 | "Her soaked jacket clung to" | | 7 | "He’d ditched the cab a" | | 8 | "She rounded a corner, nearly" | | 9 | "she barked, not bothering with" | | 10 | "The driver offered a mumbled" | | 11 | "Herrera was nowhere in sight." | | 12 | "Quinn cursed under her breath." | | 13 | "She scanned the alley –" | | 14 | "She checked her radio." | | 15 | "The comms had been patchy" | | 16 | "A glint of green." | | 17 | "The Raven’s Nest." | | 18 | "A low-life haven, nestled between" | | 19 | "She’d paid it a visit" |
| | ratio | 0.789 | |
| 91.74% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 109 | | matches | | 0 | "Before her lay the Veil" | | 1 | "What he was selling." |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 3 | | matches | | 0 | "she barked, not bothering with politeness" | | 1 | "she said, her voice cutting through the noise" | | 2 | "she asked, her voice low" |
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| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 12 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.333 | | effectiveRatio | 0.167 | |