| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Her voice cracked slightly [slightly]" |
| | dialogueSentences | 58 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0.063 | | effectiveRatio | 0.034 | |
| 86.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1085 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "slightly" | | 2 | "gently" |
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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) | |
| 63.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1085 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "chill" | | 1 | "weight" | | 2 | "silence" | | 3 | "familiar" | | 4 | "tension" | | 5 | "eyebrow" | | 6 | "charged" |
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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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 21 | | totalWords | 1068 | | ratio | 0.02 | | matches | | 0 | "urgent" | | 1 | "Ley Lines of the British Isles" | | 2 | "Ceremonial Sigils of the Avarosian Court" | | 3 | "arrested" | | 4 | "your" | | 5 | "we" | | 6 | "told" | | 7 | "you" | | 8 | "Wait for me." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 590 | | uniqueNames | 15 | | maxNameDensity | 1.02 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 6 | | Moreau | 1 | | Parisian | 1 | | Rory | 6 | | Lines | 1 | | British | 1 | | Sigils | 1 | | Avarosian | 1 | | Silence | 1 | | Three | 2 | | Covent | 1 | | Garden | 1 | | Brick | 1 | | Lane | 1 | | Ptolemy | 4 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Ptolemy" |
| | places | | 0 | "Parisian" | | 1 | "Covent" | | 2 | "Garden" | | 3 | "Brick" | | 4 | "Lane" |
| | globalScore | 0.992 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.936 | | wordCount | 1068 | | matches | | 0 | "not from calculation, but from something raw" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 14.24 | | std | 15.49 | | cv | 1.088 | | sampleLengths | | 0 | 60 | | 1 | 5 | | 2 | 4 | | 3 | 6 | | 4 | 59 | | 5 | 6 | | 6 | 28 | | 7 | 25 | | 8 | 6 | | 9 | 7 | | 10 | 7 | | 11 | 25 | | 12 | 10 | | 13 | 11 | | 14 | 34 | | 15 | 3 | | 16 | 65 | | 17 | 3 | | 18 | 7 | | 19 | 4 | | 20 | 29 | | 21 | 32 | | 22 | 5 | | 23 | 4 | | 24 | 2 | | 25 | 1 | | 26 | 57 | | 27 | 9 | | 28 | 18 | | 29 | 1 | | 30 | 7 | | 31 | 39 | | 32 | 6 | | 33 | 5 | | 34 | 16 | | 35 | 6 | | 36 | 39 | | 37 | 48 | | 38 | 2 | | 39 | 5 | | 40 | 22 | | 41 | 2 | | 42 | 28 | | 43 | 7 | | 44 | 23 | | 45 | 18 | | 46 | 7 | | 47 | 5 | | 48 | 14 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 109 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 129 | | ratio | 0.062 | | matches | | 0 | "But something in his stance—tighter, guarded—made her pause." | | 1 | "Shorter than the last time he’d seen her—shoulder-length now, not past her collar." | | 2 | "Books sprawled across the coffee table—*Ley Lines of the British Isles*, *Ceremonial Sigils of the Avarosian Court*—stacked beside empty takeout containers." | | 3 | "The amber eye caught the light—warm, almost human." | | 4 | "The scar on her left wrist—crescent-shaped, pale—tingled in the cold air." | | 5 | "Rory saw it then—the shadow behind the mask." | | 6 | "His eyes—mismatched, foreign, familiar—held hers." | | 7 | "She searched his face—the sharp angles, the controlled tension in his mouth, the faint scar above his left eyebrow she’d stitched with trembling fingers after a fight in Covent Garden." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 604 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same razor-sharp jawline," |
| | adverbCount | 18 | | adverbRatio | 0.029801324503311258 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009933774834437087 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 8.28 | | std | 7.73 | | cv | 0.934 | | sampleLengths | | 0 | 6 | | 1 | 5 | | 2 | 20 | | 3 | 2 | | 4 | 14 | | 5 | 13 | | 6 | 5 | | 7 | 4 | | 8 | 6 | | 9 | 3 | | 10 | 2 | | 11 | 31 | | 12 | 8 | | 13 | 10 | | 14 | 2 | | 15 | 2 | | 16 | 1 | | 17 | 6 | | 18 | 10 | | 19 | 13 | | 20 | 5 | | 21 | 3 | | 22 | 13 | | 23 | 9 | | 24 | 6 | | 25 | 3 | | 26 | 4 | | 27 | 7 | | 28 | 6 | | 29 | 19 | | 30 | 5 | | 31 | 5 | | 32 | 11 | | 33 | 10 | | 34 | 11 | | 35 | 13 | | 36 | 3 | | 37 | 20 | | 38 | 15 | | 39 | 21 | | 40 | 9 | | 41 | 3 | | 42 | 7 | | 43 | 4 | | 44 | 23 | | 45 | 6 | | 46 | 11 | | 47 | 21 | | 48 | 3 | | 49 | 2 |
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| 48.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3178294573643411 | | totalSentences | 129 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 37.65% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 68 | | matches | | 0 | "She stood barefoot on the" | | 1 | "She’d installed the third herself," | | 2 | "She undid the chain." | | 3 | "He hadn’t changed." | | 4 | "His heterochromatic eyes, one amber," | | 5 | "She didn’t move." | | 6 | "She let the word linger" | | 7 | "He shifted his weight" | | 8 | "She stepped aside." | | 9 | "He entered with the grace" | | 10 | "Her laptop glowed faintly, paused" | | 11 | "His voice dropped" | | 12 | "Her voice cracked slightly" | | 13 | "His jaw tightened." | | 14 | "He turned to face her" | | 15 | "His fingers flexed at his" | | 16 | "she said, forcing a laugh" | | 17 | "His voice was flat" | | 18 | "He took a step forward" | | 19 | "She crossed her arms." |
| | ratio | 0.456 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 68 | | matches | | 0 | "The chain rattled like a" | | 1 | "Rory didn’t answer right away." | | 2 | "She stood barefoot on the" | | 3 | "She’d installed the third herself," | | 4 | "Ptolemy sat three feet away," | | 5 | "A shadow darkened the fisheye." | | 6 | "She undid the chain." | | 7 | "The door opened to Lucien" | | 8 | "He hadn’t changed." | | 9 | "His heterochromatic eyes, one amber," | | 10 | "Rory’s fingers instinctively brushed the" | | 11 | "A beat passed." | | 12 | "The hum of the curry" | | 13 | "Ptolemy crept forward, sniffing the" | | 14 | "She didn’t move." | | 15 | "She let the word linger" | | 16 | "Lucien exhaled through his nose." | | 17 | "He shifted his weight" | | 18 | "The ivory handle of his" | | 19 | "She stepped aside." |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 3 | | matches | | 0 | "Still the same razor-sharp jawline, the way his charcoal suit hugged his frame like a second skin, the platinum hair slicked back as if he’d just stepped from a…" | | 1 | "He entered with the grace of someone who’d never needed permission, folding his cane and leaning it against the wall." | | 2 | "Close enough that she could smell the faint ozone beneath his cologne, the subtle otherness that clung to half-demons." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 2 | | fancyTags | | 0 | "he observed (observe)" | | 1 | "he agreed (agree)" |
| | dialogueSentences | 58 | | tagDensity | 0.155 | | leniency | 0.31 | | rawRatio | 0.222 | | effectiveRatio | 0.069 | |