| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.172 | | leniency | 0.344 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 72.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1800 | | totalAiIsmAdverbs | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "gently" | | 3 | "perfectly" | | 4 | "barely above a whisper" |
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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) | |
| 33.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1800 | | totalAiIsms | 24 | | found | | | highlights | | 0 | "gleaming" | | 1 | "flickered" | | 2 | "eyebrow" | | 3 | "pulse" | | 4 | "whisper" | | 5 | "silence" | | 6 | "flicked" | | 7 | "could feel" | | 8 | "facade" | | 9 | "velvet" | | 10 | "weight" | | 11 | "stomach" | | 12 | "vibrated" | | 13 | "shattered" | | 14 | "sentinel" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "stomach dropped" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 185 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 185 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 238 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 12 | | markdownWords | 22 | | totalWords | 1788 | | ratio | 0.012 | | matches | | 0 | "Not him." | | 1 | "Please don’t open it." | | 2 | "Mrrow" | | 3 | "Je ne peux pas te laisser partir." | | 4 | "Valuable." | | 5 | "smell" | | 6 | "law" | | 7 | "logic" | | 8 | "survival" | | 9 | "everything" | | 10 | "my" | | 11 | "glorious" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 95 | | wordCount | 1493 | | uniqueNames | 15 | | maxNameDensity | 1.88 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 26 | | Brick | 1 | | Lane | 1 | | Celtic | 1 | | Mythology | 1 | | Rory | 28 | | Moreau | 1 | | Marseille | 2 | | God | 1 | | Lucien | 22 | | Saint-Pierre | 1 | | Like | 1 | | Marseillais | 1 | | Venice | 1 | | Ptolemy | 7 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Moreau" | | 3 | "Lucien" | | 4 | "Saint-Pierre" | | 5 | "Like" | | 6 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Marseillais" | | 4 | "Venice" |
| | globalScore | 0.562 | | windowScore | 0.167 | |
| 73.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like it had been grown on him" | | 1 | "something like burnt sugar" | | 2 | "as if remembering the shape of her waist, the curve of her neck" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.559 | | wordCount | 1788 | | matches | | 0 | "not roads and rivers, but veins of human suffering hidden beneath cities" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 238 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 119 | | mean | 15.03 | | std | 14.42 | | cv | 0.96 | | sampleLengths | | 0 | 71 | | 1 | 2 | | 2 | 43 | | 3 | 8 | | 4 | 46 | | 5 | 22 | | 6 | 2 | | 7 | 15 | | 8 | 15 | | 9 | 4 | | 10 | 12 | | 11 | 37 | | 12 | 5 | | 13 | 7 | | 14 | 36 | | 15 | 6 | | 16 | 34 | | 17 | 4 | | 18 | 10 | | 19 | 4 | | 20 | 2 | | 21 | 54 | | 22 | 39 | | 23 | 8 | | 24 | 1 | | 25 | 34 | | 26 | 11 | | 27 | 11 | | 28 | 8 | | 29 | 28 | | 30 | 9 | | 31 | 11 | | 32 | 6 | | 33 | 8 | | 34 | 31 | | 35 | 11 | | 36 | 2 | | 37 | 3 | | 38 | 8 | | 39 | 34 | | 40 | 13 | | 41 | 9 | | 42 | 22 | | 43 | 9 | | 44 | 8 | | 45 | 5 | | 46 | 8 | | 47 | 19 | | 48 | 45 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 185 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 264 | | matches | | |
| 10.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 238 | | ratio | 0.046 | | matches | | 0 | "Eva was still in the shower—humming off-key and water drumming against the thin bathroom tiles." | | 1 | "The hallway light bent slightly through the fisheye lens—no shadow, no hunch, just a man’s shoes on Eva’s welcome mat." | | 2 | "Ptolemy let out a low, rumbling *Mrrow*—more warning than greeting." | | 3 | "His heterochromatic gaze—amber and black—locked onto hers with the precision of a sniper." | | 4 | "The scent of cardamom and curry from the restaurant below curled up through the floorboards, clashing with Lucien’s expensive cologne—something with bergamot and cashmere." | | 5 | "Not that Eva needed protection—she could hold her own—but something primal in Rory wanted to shield her from whatever storm Lucien carried in." | | 6 | "She could smell him—tea and ozone and something like burnt sugar." | | 7 | "His eyes burned—one amber, one black—like twin suns, pulling her in." | | 8 | "At the scar barely visible on his jawline—she’d given him that, in a fit of rage, months ago." | | 9 | "Lucien watched the cat, then her, and for the first time, something flickered across his face—vulnerability?" | | 10 | "But as she held his gaze, she knew—this wasn’t just about the map." |
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| 95.62% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1511 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 68 | | adverbRatio | 0.04500330906684315 | | lyAdverbCount | 22 | | lyAdverbRatio | 0.01455989410986102 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 238 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 238 | | mean | 7.51 | | std | 6.08 | | cv | 0.809 | | sampleLengths | | 0 | 34 | | 1 | 12 | | 2 | 25 | | 3 | 2 | | 4 | 15 | | 5 | 17 | | 6 | 5 | | 7 | 4 | | 8 | 2 | | 9 | 8 | | 10 | 19 | | 11 | 22 | | 12 | 5 | | 13 | 11 | | 14 | 4 | | 15 | 7 | | 16 | 2 | | 17 | 9 | | 18 | 6 | | 19 | 8 | | 20 | 7 | | 21 | 4 | | 22 | 12 | | 23 | 14 | | 24 | 9 | | 25 | 14 | | 26 | 5 | | 27 | 6 | | 28 | 1 | | 29 | 2 | | 30 | 4 | | 31 | 7 | | 32 | 20 | | 33 | 3 | | 34 | 3 | | 35 | 3 | | 36 | 11 | | 37 | 4 | | 38 | 16 | | 39 | 3 | | 40 | 4 | | 41 | 10 | | 42 | 4 | | 43 | 2 | | 44 | 8 | | 45 | 17 | | 46 | 16 | | 47 | 13 | | 48 | 10 | | 49 | 4 |
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| 42.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2857142857142857 | | totalSentences | 238 | | uniqueOpeners | 68 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 160 | | matches | | 0 | "Then, slowly, recognition dawned." | | 1 | "Just for a second." | | 2 | "Then it was gone." | | 3 | "Then, gently, he lifted a" | | 4 | "Just for a second." |
| | ratio | 0.031 | |
| 95.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 160 | | matches | | 0 | "She stared at the door." | | 1 | "She set the tea down" | | 2 | "He rose in one fluid" | | 3 | "His gaze never left Rory." | | 4 | "She swallowed, fingers curling into" | | 5 | "She kept her eyes on" | | 6 | "Her dark curls dripped onto" | | 7 | "Her brown eyes flickered to" | | 8 | "She could barely breathe." | | 9 | "His heterochromatic gaze—amber and black—locked" | | 10 | "She’d seen him use it" | | 11 | "she said, voice barely above" | | 12 | "He tilted his head infinitesimally." | | 13 | "She met his gaze, jaw" | | 14 | "She could feel Eva’s curiosity" | | 15 | "She wanted to slam the" | | 16 | "She wanted to scream." | | 17 | "She wanted to pull him" | | 18 | "She folded her arms." | | 19 | "he replied, voice like velvet" |
| | ratio | 0.313 | |
| 63.13% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 160 | | matches | | 0 | "The peal of the doorbell" | | 1 | "Rory froze, the steaming mug" | | 2 | "Ptolemy lifted his head from" | | 3 | "Eva was still in the" | | 4 | "Rory’s thumb hovered over the" | | 5 | "She stared at the door." | | 6 | "She set the tea down" | | 7 | "Ptolemy’s eyes narrowed to slits," | | 8 | "He rose in one fluid" | | 9 | "His gaze never left Rory." | | 10 | "She swallowed, fingers curling into" | | 11 | "Eva’s voice, bright and unaware," | | 12 | "Rory said, her voice light" | | 13 | "She kept her eyes on" | | 14 | "Rory vaulted off the sofa," | | 15 | "Eva yelped, nearly dropping the" | | 16 | "Her dark curls dripped onto" | | 17 | "Rory kept her voice low," | | 18 | "Her brown eyes flickered to" | | 19 | "The hallway light bent slightly" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 160 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 2 | | matches | | 0 | "Not since the night he’d walked into a safe house she’d been hiding in, his coat spattered with rain and something darker, his cane tapping the cobbles like a m…" | | 1 | "At the way his fingers flexed at his sides, as if remembering the shape of her waist, the curve of her neck." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, voice barely above a whisper" |
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| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 4 | | fancyTags | | 0 | "Eva hissed (hiss)" | | 1 | "she mouthed (mouth)" | | 2 | "she whispered (whisper)" | | 3 | "she repeated (repeat)" |
| | dialogueSentences | 64 | | tagDensity | 0.156 | | leniency | 0.313 | | rawRatio | 0.4 | | effectiveRatio | 0.125 | |