| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said abruptly [abruptly]" |
| | dialogueSentences | 61 | | tagDensity | 0.361 | | leniency | 0.721 | | rawRatio | 0.045 | | effectiveRatio | 0.033 | |
| 81.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1579 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "perfectly" | | 1 | "really" | | 2 | "very" | | 3 | "slightly" | | 4 | "slowly" | | 5 | "barely above a whisper" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 58.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1579 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "efficient" | | 1 | "chill" | | 2 | "scanned" | | 3 | "flicker" | | 4 | "measured" | | 5 | "warmth" | | 6 | "silence" | | 7 | "footsteps" | | 8 | "echoed" | | 9 | "eyebrow" | | 10 | "whisper" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "let out a breath" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "let out a breath" | | 1 | "the air was thick with" | | 2 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 102 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1572 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 11 | | wordCount | 1059 | | uniqueNames | 6 | | maxNameDensity | 0.47 | | worstName | "Evan" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Carter | 1 | | Evan | 5 | | Silas | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Evan" | | 4 | "Silas" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 85.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a man who wore a suit like ar" | | 1 | "as if trying to find the girl he’d known buried beneath the woman she’d become" |
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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 | 1572 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 139 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 25.77 | | std | 20.78 | | cv | 0.806 | | sampleLengths | | 0 | 92 | | 1 | 62 | | 2 | 9 | | 3 | 73 | | 4 | 40 | | 5 | 1 | | 6 | 10 | | 7 | 32 | | 8 | 52 | | 9 | 13 | | 10 | 30 | | 11 | 3 | | 12 | 10 | | 13 | 5 | | 14 | 4 | | 15 | 46 | | 16 | 19 | | 17 | 13 | | 18 | 2 | | 19 | 13 | | 20 | 28 | | 21 | 6 | | 22 | 22 | | 23 | 16 | | 24 | 18 | | 25 | 33 | | 26 | 28 | | 27 | 5 | | 28 | 37 | | 29 | 11 | | 30 | 47 | | 31 | 44 | | 32 | 18 | | 33 | 23 | | 34 | 86 | | 35 | 11 | | 36 | 9 | | 37 | 43 | | 38 | 21 | | 39 | 21 | | 40 | 10 | | 41 | 13 | | 42 | 36 | | 43 | 13 | | 44 | 11 | | 45 | 12 | | 46 | 19 | | 47 | 1 | | 48 | 30 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 180 | | matches | (empty) | |
| 81.19% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 139 | | ratio | 0.022 | | matches | | 0 | "A flicker of something—shock, recognition, a quick calculation—crossed his face before it settled into a careful, neutral mask." | | 1 | "“Right.” His eyes travelled over her—the simple black t-shirt, the apron tied at her waist, the way she stood behind the bar as if she owned it." | | 2 | "“Not like that. Not without a word.” He leaned forward, his forearms on the bar, and she caught a scent of his cologne—spicy, unfamiliar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1066 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.029080675422138838 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.01125703564727955 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 11.31 | | std | 8.57 | | cv | 0.758 | | sampleLengths | | 0 | 17 | | 1 | 25 | | 2 | 13 | | 3 | 14 | | 4 | 6 | | 5 | 17 | | 6 | 19 | | 7 | 10 | | 8 | 11 | | 9 | 22 | | 10 | 9 | | 11 | 7 | | 12 | 1 | | 13 | 3 | | 14 | 12 | | 15 | 21 | | 16 | 18 | | 17 | 11 | | 18 | 3 | | 19 | 18 | | 20 | 19 | | 21 | 1 | | 22 | 5 | | 23 | 5 | | 24 | 11 | | 25 | 15 | | 26 | 6 | | 27 | 14 | | 28 | 7 | | 29 | 4 | | 30 | 11 | | 31 | 16 | | 32 | 13 | | 33 | 27 | | 34 | 3 | | 35 | 3 | | 36 | 7 | | 37 | 3 | | 38 | 5 | | 39 | 4 | | 40 | 15 | | 41 | 17 | | 42 | 14 | | 43 | 11 | | 44 | 8 | | 45 | 11 | | 46 | 2 | | 47 | 2 | | 48 | 7 | | 49 | 6 |
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| 37.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.302158273381295 | | totalSentences | 139 | | uniqueOpeners | 42 | |
| 33.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 99 | | matches | | 0 | "Then she picked up his" |
| | ratio | 0.01 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 65 | | totalSentences | 99 | | matches | | 0 | "He was tall, broad-shouldered, his" | | 1 | "He scanned the room, his" | | 2 | "His name formed on her" | | 3 | "His hair, once a floppy" | | 4 | "He looked like a man" | | 5 | "He saw her." | | 6 | "He walked toward the bar," | | 7 | "His voice was lower, rougher." | | 8 | "It didn’t sound like him." | | 9 | "She made herself move, dropping" | | 10 | "She mirrored his tone, flat," | | 11 | "He shrugged out of his" | | 12 | "His suit was expensive, a" | | 13 | "It fit him perfectly." | | 14 | "She stated it plainly, a" | | 15 | "His eyes travelled over her—the" | | 16 | "She turned to the shelves," | | 17 | "Her hands were steady as" | | 18 | "She poured two fingers of" | | 19 | "He took it, his fingers" |
| | ratio | 0.657 | |
| 0.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 99 | | matches | | 0 | "The green neon sign of" | | 1 | "Aurora Carter wiped down the" | | 2 | "Silas was in the back" | | 3 | "The soft clink of glass" | | 4 | "The door opened, letting in" | | 5 | "A man stepped in, shaking" | | 6 | "He was tall, broad-shouldered, his" | | 7 | "He scanned the room, his" | | 8 | "Rory froze, the damp cloth" | | 9 | "His name formed on her" | | 10 | "The ghost of him, maybe," | | 11 | "The boyish softness she remembered" | | 12 | "His hair, once a floppy" | | 13 | "He looked like a man" | | 14 | "He saw her." | | 15 | "A flicker of something—shock, recognition," | | 16 | "He walked toward the bar," | | 17 | "His voice was lower, rougher." | | 18 | "It didn’t sound like him." | | 19 | "She made herself move, dropping" |
| | ratio | 0.919 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "He scanned the room, his gaze passing over the maps on the walls, the photographs of forgotten faces, before landing on her." | | 1 | "He looked at her one last time, his eyes searching her face as if trying to find the girl he’d known buried beneath the woman she’d become." |
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| 56.82% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 3 | | matches | | 0 | "Evan said, his voice quiet now, stripped of its corporate veneer" | | 1 | "She kept, cool" | | 2 | "he asked, his voice barely above a whisper" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 61 | | tagDensity | 0.082 | | leniency | 0.164 | | rawRatio | 0.2 | | effectiveRatio | 0.033 | |