| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 44 | | tagDensity | 0.114 | | leniency | 0.227 | | rawRatio | 0.2 | | effectiveRatio | 0.045 | |
| 94.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 960 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 37.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 960 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "calculated" | | 1 | "silk" | | 2 | "pulse" | | 3 | "scanned" | | 4 | "eyebrow" | | 5 | "pulsed" | | 6 | "silence" | | 7 | "familiar" | | 8 | "velvet" | | 9 | "desire" | | 10 | "flicked" |
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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 | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 73 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 955 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 579 | | uniqueNames | 5 | | maxNameDensity | 1.21 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 2 | | Tinder | 1 | | Ptolemy | 3 | | Spice | 1 | | Lucien | 7 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Lucien" |
| | places | (empty) | | globalScore | 0.896 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 95.29% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.047 | | wordCount | 955 | | matches | | 0 | "Not inviting, but interesting" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 17.05 | | std | 10.7 | | cv | 0.628 | | sampleLengths | | 0 | 48 | | 1 | 40 | | 2 | 11 | | 3 | 19 | | 4 | 45 | | 5 | 21 | | 6 | 14 | | 7 | 12 | | 8 | 36 | | 9 | 4 | | 10 | 14 | | 11 | 15 | | 12 | 16 | | 13 | 10 | | 14 | 32 | | 15 | 7 | | 16 | 23 | | 17 | 14 | | 18 | 12 | | 19 | 16 | | 20 | 7 | | 21 | 26 | | 22 | 9 | | 23 | 17 | | 24 | 24 | | 25 | 12 | | 26 | 45 | | 27 | 9 | | 28 | 4 | | 29 | 6 | | 30 | 21 | | 31 | 36 | | 32 | 7 | | 33 | 19 | | 34 | 9 | | 35 | 28 | | 36 | 12 | | 37 | 5 | | 38 | 24 | | 39 | 14 | | 40 | 5 | | 41 | 14 | | 42 | 18 | | 43 | 22 | | 44 | 4 | | 45 | 10 | | 46 | 19 | | 47 | 15 | | 48 | 13 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 106 | | matches | (empty) | |
| 14.16% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 111 | | ratio | 0.045 | | matches | | 0 | "Three deadbolts, muscle memory—slide, click, twist." | | 1 | "He scanned the mess—books teetering on every surface, maps stuffed in coffee mugs, strings of garlic and chillies clipped to the kitchen lamp." | | 2 | "\"For leverage. You know the city’s currents; my father’s interests, however, require more... local expertise.\"" | | 3 | "His eyebrow arched—a silent dare." | | 4 | "The door juddered in its frame as someone hammered—no knuckle rapping, no patience left." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 583 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.030874785591766724 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0017152658662092624 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 8.6 | | std | 6.01 | | cv | 0.698 | | sampleLengths | | 0 | 23 | | 1 | 6 | | 2 | 9 | | 3 | 10 | | 4 | 20 | | 5 | 20 | | 6 | 7 | | 7 | 4 | | 8 | 11 | | 9 | 8 | | 10 | 39 | | 11 | 6 | | 12 | 11 | | 13 | 10 | | 14 | 9 | | 15 | 5 | | 16 | 3 | | 17 | 9 | | 18 | 5 | | 19 | 10 | | 20 | 21 | | 21 | 4 | | 22 | 12 | | 23 | 2 | | 24 | 15 | | 25 | 7 | | 26 | 9 | | 27 | 10 | | 28 | 23 | | 29 | 9 | | 30 | 7 | | 31 | 8 | | 32 | 15 | | 33 | 14 | | 34 | 2 | | 35 | 5 | | 36 | 5 | | 37 | 9 | | 38 | 7 | | 39 | 7 | | 40 | 2 | | 41 | 18 | | 42 | 6 | | 43 | 5 | | 44 | 4 | | 45 | 17 | | 46 | 13 | | 47 | 11 | | 48 | 2 | | 49 | 5 |
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| 74.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4594594594594595 | | totalSentences | 111 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 1.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 66 | | matches | | 0 | "He pressed the ivory head" | | 1 | "I let the door hang" | | 2 | "He stepped inside, gaze flicking" | | 3 | "I braced a hip against" | | 4 | "His mouth curled." | | 5 | "I watched the way his" | | 6 | "His eye darkened, the trick" | | 7 | "He shrugged, the motion civilised" | | 8 | "He scanned the mess—books teetering" | | 9 | "He rolled his sleeves, revealing" | | 10 | "It sounded soft and bitter." | | 11 | "My voice cracked, betraying old" | | 12 | "His eyebrow arched—a silent dare." | | 13 | "He inspected my wrist, where" | | 14 | "My back found the wall." | | 15 | "His gaze softened, a flickering" | | 16 | "He leaned closer, breath washing" | | 17 | "It hung there, heavier than" | | 18 | "His lips quirked." | | 19 | "I jerked away." |
| | ratio | 0.545 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 66 | | matches | | 0 | "Something thudded against the door," | | 1 | "Knuckles white on brass, I" | | 2 | "A single amber eye peered" | | 3 | "Lucien’s silhouette filled the frame," | | 4 | "He pressed the ivory head" | | 5 | "The accent, all silk and" | | 6 | "I let the door hang" | | 7 | "He stepped inside, gaze flicking" | | 8 | "Ptolemy always sided with the" | | 9 | "Lucien stooped, ruffled the cat’s" | | 10 | "I braced a hip against" | | 11 | "His mouth curled." | | 12 | "I watched the way his" | | 13 | "His eye darkened, the trick" | | 14 | "He shrugged, the motion civilised" | | 15 | "He scanned the mess—books teetering" | | 16 | "He rolled his sleeves, revealing" | | 17 | "It sounded soft and bitter." | | 18 | "Ptolemy leapt onto the table" | | 19 | "Lucien eyed the cat like" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |