| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said softly [softly]" |
| | dialogueSentences | 48 | | tagDensity | 0.542 | | leniency | 1 | | rawRatio | 0.038 | | effectiveRatio | 0.038 | |
| 90.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1526 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "slightly" | | 2 | "softly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 60.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1526 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "gloom" | | 1 | "perfect" | | 2 | "scanned" | | 3 | "stark" | | 4 | "potential" | | 5 | "trembled" | | 6 | "etched" | | 7 | "could feel" | | 8 | "racing" | | 9 | "pulse" | | 10 | "standard" | | 11 | "pulsed" |
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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 | 125 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 125 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1526 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 78 | | wordCount | 1131 | | uniqueNames | 18 | | maxNameDensity | 2.74 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Tube | 1 | | Detective | 2 | | Harlow | 2 | | Quinn | 31 | | London | 1 | | Morris | 4 | | Sergeant | 1 | | Evans | 9 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Veil | 2 | | Compass | 1 | | Lock | 1 | | North | 1 | | Eva | 16 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Sergeant" | | 4 | "Evans" | | 5 | "Kowalski" | | 6 | "Compass" | | 7 | "Eva" | | 8 | "Market" |
| | places | | 0 | "Camden" | | 1 | "Detective" | | 2 | "London" |
| | globalScore | 0.13 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 68.94% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.311 | | wordCount | 1526 | | matches | | 0 | "not cardiac,\" Eva said, her voice quiet but steady" | | 1 | "not in the evidence left behind, but in the space" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 147 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 33.17 | | std | 20.92 | | cv | 0.631 | | sampleLengths | | 0 | 56 | | 1 | 90 | | 2 | 23 | | 3 | 13 | | 4 | 35 | | 5 | 36 | | 6 | 38 | | 7 | 47 | | 8 | 80 | | 9 | 36 | | 10 | 23 | | 11 | 22 | | 12 | 43 | | 13 | 22 | | 14 | 10 | | 15 | 18 | | 16 | 79 | | 17 | 28 | | 18 | 12 | | 19 | 6 | | 20 | 40 | | 21 | 5 | | 22 | 48 | | 23 | 48 | | 24 | 39 | | 25 | 3 | | 26 | 60 | | 27 | 12 | | 28 | 27 | | 29 | 57 | | 30 | 45 | | 31 | 17 | | 32 | 34 | | 33 | 22 | | 34 | 32 | | 35 | 58 | | 36 | 14 | | 37 | 44 | | 38 | 6 | | 39 | 30 | | 40 | 10 | | 41 | 22 | | 42 | 31 | | 43 | 26 | | 44 | 10 | | 45 | 69 |
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| 94.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 125 | | matches | | 0 | "been thrown" | | 1 | "was etched" | | 2 | "was locked" | | 3 | "been lost" |
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| 13.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 215 | | matches | | 0 | "wasn't spinning" | | 1 | "wasn't pointing" | | 2 | "was pointing" | | 3 | "was standing" | | 4 | "was looking" | | 5 | "was getting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 147 | | ratio | 0.007 | | matches | | 0 | "The needle wasn't spinning; it was locked, vibrating with a low hum she could feel in her teeth." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1133 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.02294792586054722 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009708737864077669 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 10.38 | | std | 7.15 | | cv | 0.689 | | sampleLengths | | 0 | 26 | | 1 | 17 | | 2 | 1 | | 3 | 8 | | 4 | 4 | | 5 | 17 | | 6 | 29 | | 7 | 8 | | 8 | 36 | | 9 | 2 | | 10 | 2 | | 11 | 19 | | 12 | 7 | | 13 | 4 | | 14 | 2 | | 15 | 8 | | 16 | 14 | | 17 | 13 | | 18 | 18 | | 19 | 18 | | 20 | 15 | | 21 | 23 | | 22 | 7 | | 23 | 4 | | 24 | 16 | | 25 | 20 | | 26 | 10 | | 27 | 22 | | 28 | 15 | | 29 | 18 | | 30 | 15 | | 31 | 10 | | 32 | 22 | | 33 | 4 | | 34 | 2 | | 35 | 21 | | 36 | 11 | | 37 | 11 | | 38 | 8 | | 39 | 9 | | 40 | 4 | | 41 | 22 | | 42 | 11 | | 43 | 11 | | 44 | 4 | | 45 | 6 | | 46 | 18 | | 47 | 7 | | 48 | 5 | | 49 | 7 |
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| 62.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4 | | totalSentences | 145 | | uniqueOpeners | 58 | |
| 63.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 105 | | matches | | 0 | "Too late for commuters, too" | | 1 | "Just a man in a" |
| | ratio | 0.019 | |
| 90.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 105 | | matches | | 0 | "She checked the worn leather" | | 1 | "She was forty-one, and the" | | 2 | "You're overthinking it, Harlow, she" | | 3 | "It's a heart attack." | | 4 | "It was the same feeling" | | 5 | "He looked up as Quinn" | | 6 | "She scanned the perimeter." | | 7 | "She turned as a figure" | | 8 | "She clutched a worn leather" | | 9 | "She stopped beside Quinn, tucking" | | 10 | "She ran a gloved finger" | | 11 | "It smeared like charcoal." | | 12 | "She didn't believe in magic." | | 13 | "She believed in motives, means," | | 14 | "She reached into her pocket" | | 15 | "She'd confiscated it from a" | | 16 | "She pulled it out now." | | 17 | "She held the compass flat." | | 18 | "It wasn't pointing at the" | | 19 | "It was pointing at the" |
| | ratio | 0.324 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 105 | | matches | | 0 | "The damp air of the" | | 1 | "She checked the worn leather" | | 2 | "Quinn stepped over the yellow" | | 3 | "She was forty-one, and the" | | 4 | "The crime scene was unusual," | | 5 | "Bodies didn't usually turn up" | | 6 | "You're overthinking it, Harlow, she" | | 7 | "It's a heart attack." | | 8 | "It was the same feeling" | | 9 | "The air here felt thin," | | 10 | "Detective Sergeant Evans was already" | | 11 | "He looked up as Quinn" | | 12 | "Evans said, standing and brushing" | | 13 | "Quinn didn't look at the" | | 14 | "She scanned the perimeter." | | 15 | "The shadows seemed to pool" | | 16 | "She turned as a figure" | | 17 | "Eva Kowalski adjusted the round" | | 18 | "She clutched a worn leather" | | 19 | "Quinn had pulled strings to" |
| | ratio | 0.857 | |
| 47.62% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 105 | | matches | | 0 | "Now the impossible was standing" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "The damp air of the abandoned Camden Tube station clung to Detective Harlow Quinn's coat, a cold sweat of condensation that smelled of rust and ozone." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 7 | | matches | | 0 | "Eva said, her voice quiet but steady" | | 1 | "Eva corrected, her green eyes flashing behind the lenses" | | 2 | "Eva countered, though her hand trembled slightly as she adjusted her satchel strap" | | 3 | "Quinn snapped, though her heart hammered against her ribs" | | 4 | "Quinn said, the deduction forming in real-time" | | 5 | "Quinn said, her voice dropping" | | 6 | "Quinn said, her voice hard" |
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| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 23 | | fancyCount | 4 | | fancyTags | | 0 | "Eva corrected (correct)" | | 1 | "Eva whispered (whisper)" | | 2 | "Quinn snapped (snap)" | | 3 | "Quinn corrected (correct)" |
| | dialogueSentences | 48 | | tagDensity | 0.479 | | leniency | 0.958 | | rawRatio | 0.174 | | effectiveRatio | 0.167 | |