| 65.17% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 6 | | adverbTags | | 0 | "Marcus said finally [finally]" | | 1 | "He smiled sadly [sadly]" | | 2 | "The question tasted like [like]" | | 3 | "The name landed like [like]" | | 4 | "He stood too [too]" | | 5 | "she said again [again]" |
| | dialogueSentences | 89 | | tagDensity | 0.27 | | leniency | 0.539 | | rawRatio | 0.25 | | effectiveRatio | 0.135 | |
| 76.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2139 | | totalAiIsmAdverbs | 10 | | found | | | highlights | | 0 | "slowly" | | 1 | "really" | | 2 | "slightly" | | 3 | "carefully" | | 4 | "sadly" |
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| 60.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 78.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2139 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "coded" | | 3 | "silence" | | 4 | "dance" | | 5 | "perfect" | | 6 | "comfortable" | | 7 | "methodical" |
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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 | 184 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 184 | | filterMatches | (empty) | | hedgeMatches | | 0 | "started to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 248 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 74 | | wordCount | 1688 | | uniqueNames | 25 | | maxNameDensity | 0.89 | | worstName | "Marcus" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Marcus" | | discoveredNames | | Aurora | 8 | | Tuesday | 2 | | Cardiff | 2 | | Carter | 5 | | Silas | 5 | | Terry | 2 | | Evan | 5 | | Hungrier | 1 | | Raven | 1 | | Nest | 1 | | Unless | 3 | | Thames | 1 | | Chinese | 1 | | Marcus | 15 | | Upstairs | 1 | | Freedom | 1 | | Crisp | 1 | | Eva | 3 | | Yu-Fei | 2 | | Associates | 1 | | Army | 1 | | Rory | 3 | | London | 2 | | Since | 3 | | Would | 4 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Silas" | | 3 | "Terry" | | 4 | "Evan" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "Marcus" | | 8 | "Eva" | | 9 | "Rory" | | 10 | "Would" |
| | places | | 0 | "Cardiff" | | 1 | "Thames" | | 2 | "Yu-Fei" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 126 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like cooking oil and other people'" | | 1 | "felt like when it came with closed fist" | | 2 | "looked like a map of somewhere better" |
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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 | 2127 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 248 | | matches | | 0 | "learned that all" | | 1 | "up that night" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 118 | | mean | 18.03 | | std | 15.71 | | cv | 0.871 | | sampleLengths | | 0 | 10 | | 1 | 10 | | 2 | 27 | | 3 | 2 | | 4 | 36 | | 5 | 19 | | 6 | 2 | | 7 | 13 | | 8 | 30 | | 9 | 1 | | 10 | 14 | | 11 | 44 | | 12 | 2 | | 13 | 19 | | 14 | 15 | | 15 | 2 | | 16 | 6 | | 17 | 30 | | 18 | 2 | | 19 | 14 | | 20 | 4 | | 21 | 3 | | 22 | 3 | | 23 | 8 | | 24 | 12 | | 25 | 3 | | 26 | 32 | | 27 | 10 | | 28 | 2 | | 29 | 2 | | 30 | 2 | | 31 | 15 | | 32 | 47 | | 33 | 3 | | 34 | 15 | | 35 | 55 | | 36 | 8 | | 37 | 40 | | 38 | 31 | | 39 | 3 | | 40 | 3 | | 41 | 7 | | 42 | 11 | | 43 | 14 | | 44 | 1 | | 45 | 10 | | 46 | 34 | | 47 | 12 | | 48 | 2 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 184 | | matches | | 0 | "was supposed" | | 1 | "were earned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 307 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 248 | | ratio | 0 | | matches | (empty) | |
| 89.86% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1699 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Crisp white, professional font." |
| | adverbCount | 76 | | adverbRatio | 0.04473219540906415 | | lyAdverbCount | 28 | | lyAdverbRatio | 0.0164802825191289 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 248 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 248 | | mean | 8.58 | | std | 6.41 | | cv | 0.747 | | sampleLengths | | 0 | 10 | | 1 | 10 | | 2 | 6 | | 3 | 13 | | 4 | 8 | | 5 | 2 | | 6 | 5 | | 7 | 23 | | 8 | 8 | | 9 | 13 | | 10 | 6 | | 11 | 2 | | 12 | 7 | | 13 | 6 | | 14 | 7 | | 15 | 4 | | 16 | 1 | | 17 | 1 | | 18 | 17 | | 19 | 1 | | 20 | 11 | | 21 | 3 | | 22 | 4 | | 23 | 6 | | 24 | 34 | | 25 | 2 | | 26 | 8 | | 27 | 4 | | 28 | 7 | | 29 | 12 | | 30 | 3 | | 31 | 2 | | 32 | 4 | | 33 | 2 | | 34 | 4 | | 35 | 2 | | 36 | 24 | | 37 | 2 | | 38 | 11 | | 39 | 3 | | 40 | 4 | | 41 | 3 | | 42 | 3 | | 43 | 7 | | 44 | 1 | | 45 | 4 | | 46 | 1 | | 47 | 7 | | 48 | 3 | | 49 | 4 |
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| 70.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.4798387096774194 | | totalSentences | 248 | | uniqueOpeners | 119 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 11 | | totalSentences | 168 | | matches | | 0 | "Just Terry, who wouldn't ask" | | 1 | "Of course she did." | | 2 | "Probably spun a story about" | | 3 | "Almost explained about the first" | | 4 | "Instead, she said," | | 5 | "Gloriously, defiantly alive." | | 6 | "Maybe Marcus would understand." | | 7 | "Maybe he'd think her cruel." | | 8 | "Maybe the truth was simpler:" | | 9 | "Just Yu-Fei's schedule and the" | | 10 | "Just offered her the flat" |
| | ratio | 0.065 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 168 | | matches | | 0 | "She turned slowly, already knowing" | | 1 | "He looked exactly the same." | | 2 | "He moved closer, stopping just" | | 3 | "His laugh came out forced" | | 4 | "He gestured at the empty" | | 5 | "She should say yes." | | 6 | "His eyebrows rose slightly." | | 7 | "He accepted his drink, studying" | | 8 | "He paused, choosing words carefully" | | 9 | "They drank in silence." | | 10 | "He smiled sadly" | | 11 | "She drained her whisky, welcoming" | | 12 | "Her voice came out sharper" | | 13 | "She stood abruptly, fumbling for" | | 14 | "His fingers brushed the scar" | | 15 | "She jerked away, throwing bills" | | 16 | "He stood too, desperation creeping" | | 17 | "She made it three steps" | | 18 | "Her hands had started shaking." | | 19 | "His reflection caught in the" |
| | ratio | 0.226 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 168 | | matches | | 0 | "The whisky burned less than" | | 1 | "The voice came from behind," | | 2 | "Aurora's fingers tightened around her" | | 3 | "She turned slowly, already knowing" | | 4 | "He looked exactly the same." | | 5 | "The familiarity hurt more than" | | 6 | "He moved closer, stopping just" | | 7 | "His laugh came out forced" | | 8 | "The question hung between them" | | 9 | "He gestured at the empty" | | 10 | "She should say yes." | | 11 | "Marcus settled onto the stool," | | 12 | "Marcus ordered, then glanced at" | | 13 | "His eyebrows rose slightly." | | 14 | "He accepted his drink, studying" | | 15 | "He paused, choosing words carefully" | | 16 | "They drank in silence." | | 17 | "Suits loosening ties, artists arguing" | | 18 | "Marcus said finally" | | 19 | "He smiled sadly" |
| | ratio | 0.577 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 168 | | matches | | 0 | "Because Evan knew exactly how" | | 1 | "Because Aurora Carter believed in" | | 2 | "In case what?" | | 3 | "In case she decided to" | | 4 | "In case she wanted to" |
| | ratio | 0.03 | |
| 76.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 6 | | matches | | 0 | "Should protect the careful life she'd built above this bar, away from everyone who knew Aurora Carter the almost-lawyer, the straight-A student, the girl who ha…" | | 1 | "Leaving out the parts about locked doors and apologies that came with flowers and promises that meant nothing when the bruises were still fresh." | | 2 | "The stairs to her flat stretched above, each step a small victory over the girl who would have stayed to explain, to apologize, to make everyone comfortable wit…" | | 3 | "The kind of place that did background checks and expected explanations for three-year gaps." | | 4 | "Maybe the truth was simpler: some bridges burned themselves, and the woman who'd risen from those ashes had no interest in rebuilding." | | 5 | "Silas would be closing up, wiping down surfaces with the methodical care of someone who understood that clean slates were earned, not given." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 2 | | matches | | 0 | "He stood too, desperation creeping into his voice" | | 1 | "Marcus moved, voice soft" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Marcus ordered (order)" | | 1 | "He pressed (press)" |
| | dialogueSentences | 89 | | tagDensity | 0.067 | | leniency | 0.135 | | rawRatio | 0.333 | | effectiveRatio | 0.045 | |