| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.367 | | leniency | 0.733 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 76.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 841 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "hesitantly" | | 1 | "nervously" | | 2 | "quickly" | | 3 | "slightly" |
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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) | |
| 34.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 841 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "chaotic" | | 1 | "familiar" | | 2 | "eyebrow" | | 3 | "furrowed" | | 4 | "cataloged" | | 5 | "tracing" | | 6 | "glinting" | | 7 | "navigate" | | 8 | "resolve" |
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| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 6 | | maxInWindow | 6 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 5 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 54 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 844 | | ratio | 0 | | matches | (empty) | |
| 89.29% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 1 | | matches | | 0 | "Somehow, she doubted this was a mere mortal killing." |
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| 11.11% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 576 | | uniqueNames | 8 | | maxNameDensity | 2.78 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 16 | | Quinn | 2 | | Tube | 1 | | Veil | 3 | | Market | 3 | | London | 1 | | Kowalski | 1 | | Eva | 9 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" |
| | places | | | globalScore | 0.111 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 844 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 30.14 | | std | 16.28 | | cv | 0.54 | | sampleLengths | | 0 | 46 | | 1 | 58 | | 2 | 15 | | 3 | 33 | | 4 | 24 | | 5 | 17 | | 6 | 33 | | 7 | 14 | | 8 | 26 | | 9 | 69 | | 10 | 9 | | 11 | 40 | | 12 | 14 | | 13 | 28 | | 14 | 18 | | 15 | 31 | | 16 | 33 | | 17 | 58 | | 18 | 21 | | 19 | 22 | | 20 | 39 | | 21 | 7 | | 22 | 46 | | 23 | 9 | | 24 | 48 | | 25 | 20 | | 26 | 17 | | 27 | 49 |
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| 85.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 54 | | matches | | 0 | "been slashed" | | 1 | "were untouched" | | 2 | "was cauterized" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 87 | | matches | (empty) | |
| 23.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 72 | | ratio | 0.042 | | matches | | 0 | "Harlow's eyes narrowed as she surveyed the bustling scene — booths and stalls ringed the cavernous underground space, their proprietors hawking enchanted wares, occult trinkets, and surreptitious information." | | 1 | "The wound was clean, surgical — the work of a skilled hand." | | 2 | "If there was a supernatural killer stalking these shadows, she would find them — no matter what it took." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 577 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.019064124783362217 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.01386481802426343 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 11.72 | | std | 6.66 | | cv | 0.568 | | sampleLengths | | 0 | 28 | | 1 | 18 | | 2 | 28 | | 3 | 13 | | 4 | 17 | | 5 | 15 | | 6 | 24 | | 7 | 9 | | 8 | 5 | | 9 | 19 | | 10 | 4 | | 11 | 13 | | 12 | 23 | | 13 | 10 | | 14 | 8 | | 15 | 6 | | 16 | 15 | | 17 | 11 | | 18 | 24 | | 19 | 17 | | 20 | 10 | | 21 | 5 | | 22 | 13 | | 23 | 9 | | 24 | 5 | | 25 | 24 | | 26 | 11 | | 27 | 3 | | 28 | 11 | | 29 | 8 | | 30 | 20 | | 31 | 11 | | 32 | 7 | | 33 | 4 | | 34 | 27 | | 35 | 11 | | 36 | 22 | | 37 | 15 | | 38 | 12 | | 39 | 3 | | 40 | 3 | | 41 | 16 | | 42 | 9 | | 43 | 14 | | 44 | 7 | | 45 | 14 | | 46 | 8 | | 47 | 3 | | 48 | 19 | | 49 | 9 |
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| 70.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4305555555555556 | | totalSentences | 72 | | uniqueOpeners | 31 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Somehow, she doubted this was" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 54 | | matches | | 0 | "It was a far cry" | | 1 | "His throat had been slashed," | | 2 | "She frowned, a niggling suspicion" | | 3 | "she asked, glancing up at" | | 4 | "She worried at the strap" | | 5 | "She rose to her feet," | | 6 | "she murmured, half to herself" | | 7 | "She crouched back down, her" | | 8 | "Her brow furrowed." | | 9 | "She shook her head slightly." | | 10 | "She cast a sidelong glance" | | 11 | "She turned and strode deeper" |
| | ratio | 0.222 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 54 | | matches | | 0 | "Detective Harlow Quinn's steel-toed boots" | | 1 | "The air was thick with" | | 2 | "Harlow's eyes narrowed as she" | | 3 | "It was a far cry" | | 4 | "This was the domain of" | | 5 | "Harlow called out, her voice" | | 6 | "A familiar figure in a" | | 7 | "Harlow arched one salt-and-pepper eyebrow." | | 8 | "Eva Kowalski's brow furrowed." | | 9 | "Harlow gestured towards the center" | | 10 | "Eva's green eyes widened behind" | | 11 | "Harlow strode towards the commotion," | | 12 | "The crowd parted before her," | | 13 | "Harlow knelt beside him, her" | | 14 | "His throat had been slashed," | | 15 | "She frowned, a niggling suspicion" | | 16 | "she asked, glancing up at" | | 17 | "The young woman nodded hesitantly." | | 18 | "She worried at the strap" | | 19 | "Harlow's eyes narrowed." |
| | ratio | 0.889 | |
| 92.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 54 | | matches | | 0 | "If there was a supernatural" |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 6 | | matches | | 0 | "Harlow called out, her voice cutting through the chaotic din" | | 1 | "She worried, her gaze darting around nervously" | | 2 | "Harlow interrupted, a spark of interest in her eyes" | | 3 | "she murmured, half to herself" | | 4 | "Eva supplied, her fingers tightening on the strap of her satchel" | | 5 | "She crouched, her fingers tracing the edges of the fatal wound" |
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| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow called out (call out)" | | 1 | "Harlow interrupted (interrupt)" | | 2 | "she murmured (murmur)" |
| | dialogueSentences | 30 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0.75 | | effectiveRatio | 0.2 | |