| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 76.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1084 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "cautiously" | | 2 | "tightly" |
| |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1084 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "echoed" | | 1 | "silence" | | 2 | "scanned" | | 3 | "shattered" | | 4 | "gloom" | | 5 | "scanning" | | 6 | "furrowed" | | 7 | "traced" | | 8 | "flicked" | | 9 | "racing" | | 10 | "etched" | | 11 | "otherworldly" | | 12 | "trembled" | | 13 | "chill" | | 14 | "footsteps" | | 15 | "echoing" | | 16 | "unreadable" | | 17 | "resolve" | | 18 | "unravel" | | 19 | "pulsed" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "clenched into fists" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 74 | | matches | (empty) | |
| 84.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1077 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 29.59% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 789 | | uniqueNames | 11 | | maxNameDensity | 2.41 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 19 | | Veil | 3 | | Market | 1 | | Eva | 17 | | Kowalski | 1 | | Morris | 2 | | Ahead | 1 | | Compass | 2 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.296 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed poised to unravel" |
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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 | 1077 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 35.9 | | std | 22.64 | | cv | 0.631 | | sampleLengths | | 0 | 104 | | 1 | 65 | | 2 | 19 | | 3 | 61 | | 4 | 13 | | 5 | 34 | | 6 | 38 | | 7 | 13 | | 8 | 9 | | 9 | 51 | | 10 | 74 | | 11 | 9 | | 12 | 49 | | 13 | 29 | | 14 | 25 | | 15 | 11 | | 16 | 59 | | 17 | 8 | | 18 | 33 | | 19 | 22 | | 20 | 34 | | 21 | 35 | | 22 | 16 | | 23 | 40 | | 24 | 11 | | 25 | 24 | | 26 | 33 | | 27 | 62 | | 28 | 39 | | 29 | 57 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 145 | | matches | | |
| 14.45% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 89 | | ratio | 0.045 | | matches | | 0 | "The Veil Market lay in shambles—torn fabric awnings hung limp, shattered glass crunched underfoot, and overturned stalls spilled their contents onto the cracked platform." | | 1 | "The shattered stalls, the residue, the compass—each piece fit into a puzzle she couldn’t yet see." | | 2 | "Her fingers trembled as she found the page she was looking for—a sketch of symbols identical to those etched into the Compass." | | 3 | "She couldn’t let Quinn face this alone—not when the Veil itself seemed poised to unravel." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 794 | | adjectiveStacks | 1 | | stackExamples | | 0 | "worn leather-bound journal" |
| | adverbCount | 21 | | adverbRatio | 0.02644836272040302 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.017632241813602016 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 12.1 | | std | 7.17 | | cv | 0.593 | | sampleLengths | | 0 | 21 | | 1 | 16 | | 2 | 12 | | 3 | 14 | | 4 | 24 | | 5 | 17 | | 6 | 11 | | 7 | 21 | | 8 | 21 | | 9 | 12 | | 10 | 12 | | 11 | 7 | | 12 | 13 | | 13 | 31 | | 14 | 17 | | 15 | 11 | | 16 | 2 | | 17 | 14 | | 18 | 20 | | 19 | 3 | | 20 | 9 | | 21 | 15 | | 22 | 11 | | 23 | 3 | | 24 | 10 | | 25 | 5 | | 26 | 4 | | 27 | 14 | | 28 | 37 | | 29 | 7 | | 30 | 9 | | 31 | 5 | | 32 | 15 | | 33 | 1 | | 34 | 37 | | 35 | 9 | | 36 | 11 | | 37 | 13 | | 38 | 15 | | 39 | 10 | | 40 | 14 | | 41 | 15 | | 42 | 12 | | 43 | 13 | | 44 | 3 | | 45 | 8 | | 46 | 6 | | 47 | 8 | | 48 | 16 | | 49 | 9 |
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| 53.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3258426966292135 | | totalSentences | 89 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 97.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 72 | | matches | | 0 | "She adjusted her coat and" | | 1 | "She pushed her round glasses" | | 2 | "Her worn leather satchel hung" | | 3 | "Her brown eyes narrowed, scanning" | | 4 | "She gestured to a splintered" | | 5 | "She straightened and turned her" | | 6 | "Her eyes traced the faint" | | 7 | "She turned back to the" | | 8 | "She spotted a small brass" | | 9 | "Her military precision honed the" | | 10 | "She moved deeper into the" | | 11 | "Her breath fogged in the" | | 12 | "She stepped off the platform," | | 13 | "She trailed off, her eyes" | | 14 | "She stepped forward, the shadows" | | 15 | "She watched as Quinn disappeared" | | 16 | "She pulled a worn leather-bound" | | 17 | "Her fingers trembled as she" | | 18 | "She closed the journal, her" | | 19 | "She couldn’t let Quinn face" |
| | ratio | 0.306 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 72 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped off" | | 2 | "The worn leather watch on" | | 3 | "She adjusted her coat and" | | 4 | "The Veil Market lay in" | | 5 | "A faint, acrid scent lingered" | | 6 | "A voice called from the" | | 7 | "Eva Kowalski stood near a" | | 8 | "She pushed her round glasses" | | 9 | "Her worn leather satchel hung" | | 10 | "Quinn’s voice cut through the" | | 11 | "Her brown eyes narrowed, scanning" | | 12 | "Eva tucked a loose curl" | | 13 | "She gestured to a splintered" | | 14 | "Quinn knelt beside the crate," | | 15 | "Eva’s voice faltered" | | 16 | "Quinn’s brow furrowed." | | 17 | "She straightened and turned her" | | 18 | "Her eyes traced the faint" | | 19 | "Quinn’s gaze flicked to Eva." |
| | ratio | 0.958 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 7 | | matches | | 0 | "Quinn said, her voice firm" | | 1 | "Eva crouched, her fingers brushing the edge cautiously" | | 2 | "She stepped, her boots crunching on gravel and debris" | | 3 | "Quinn said, her voice low," | | 4 | "She trailed, her eyes narrowing" | | 5 | "She stepped, the shadows closing around her" | | 6 | "She closed, her resolve hardening" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Eva’s voice faltered (falter)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 30 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.4 | | effectiveRatio | 0.133 | |