| 57.14% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "she called again [again]" |
| | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0.143 | | effectiveRatio | 0.143 | |
| 93.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 780 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 780 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "gloom" | | 1 | "flicker" | | 2 | "echoed" | | 3 | "etched" | | 4 | "echo" | | 5 | "pulse" | | 6 | "weight" | | 7 | "pulsed" | | 8 | "silk" | | 9 | "chill" | | 10 | "shattered" | | 11 | "unreadable" | | 12 | "depths" | | 13 | "sinister" |
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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 | 63 | | matches | (empty) | |
| 97.51% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 772 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 72.86% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 713 | | uniqueNames | 9 | | maxNameDensity | 1.54 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 11 | | Quinn | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 1 | | Soho | 1 | | Morris | 2 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Morris" |
| | places | | | globalScore | 0.729 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.591 | | wordCount | 772 | | matches | | 0 | "Not a shout, but a warning" | | 1 | "not the passage, but a different scene: a rooftop, rain pouring down" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 33.57 | | std | 21.54 | | cv | 0.642 | | sampleLengths | | 0 | 68 | | 1 | 61 | | 2 | 55 | | 3 | 16 | | 4 | 52 | | 5 | 78 | | 6 | 11 | | 7 | 5 | | 8 | 38 | | 9 | 58 | | 10 | 43 | | 11 | 14 | | 12 | 46 | | 13 | 5 | | 14 | 31 | | 15 | 33 | | 16 | 11 | | 17 | 29 | | 18 | 7 | | 19 | 48 | | 20 | 34 | | 21 | 21 | | 22 | 8 |
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| 99.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 63 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 123 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 68 | | ratio | 0.132 | | matches | | 0 | "She’d seen him slip inside, but the narrow passageway offered no clear path—just shadows and the distant hum of a subway train." | | 1 | "She’d learned to trust her instincts; they’d kept her alive during the case that took DS Morris." | | 2 | "The sound wasn’t wood settling—it was deliberate, like a footstep." | | 3 | "Then, a flicker of movement—a figure darting between two stacked crates." | | 4 | "The underground market pulsed with life—vendors hawking vials of liquid shadow, stalls draped in black silk, and the low murmur of transactions conducted in hushed tones." | | 5 | "She’d heard whispers about the Veil Market—how it traded in things that shouldn’t exist, how it was a haven for those who operated in the cracks of the law." | | 6 | "His eyes, however, were visible—pale, almost white, and unnervingly still." | | 7 | "The man’s hood fell away, revealing a face she recognized—too well." | | 8 | "“Someone who knows what’s waiting for you below.” She gestured to the market’s depths, where the glow of lanterns revealed more stalls—each one more sinister than the last." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 723 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.03319502074688797 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006915629322268326 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 11.35 | | std | 6.53 | | cv | 0.576 | | sampleLengths | | 0 | 18 | | 1 | 14 | | 2 | 14 | | 3 | 22 | | 4 | 7 | | 5 | 21 | | 6 | 16 | | 7 | 17 | | 8 | 9 | | 9 | 5 | | 10 | 10 | | 11 | 16 | | 12 | 15 | | 13 | 10 | | 14 | 6 | | 15 | 8 | | 16 | 1 | | 17 | 7 | | 18 | 11 | | 19 | 8 | | 20 | 17 | | 21 | 17 | | 22 | 19 | | 23 | 26 | | 24 | 16 | | 25 | 9 | | 26 | 2 | | 27 | 3 | | 28 | 2 | | 29 | 10 | | 30 | 21 | | 31 | 7 | | 32 | 5 | | 33 | 29 | | 34 | 5 | | 35 | 19 | | 36 | 9 | | 37 | 8 | | 38 | 16 | | 39 | 10 | | 40 | 14 | | 41 | 4 | | 42 | 13 | | 43 | 3 | | 44 | 26 | | 45 | 5 | | 46 | 11 | | 47 | 3 | | 48 | 5 | | 49 | 12 |
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| 63.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.39705882352941174 | | totalSentences | 68 | | uniqueOpeners | 27 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 61 | | matches | | 0 | "Just the echo of her" | | 1 | "Then, a flicker of movement—a" | | 2 | "Instead, he raised a hand," | | 3 | "Somewhere in the distance, a" |
| | ratio | 0.066 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 61 | | matches | | 0 | "She’d seen him slip inside," | | 1 | "she muttered, gripping her flashlight" | | 2 | "She’d learned to trust her" | | 3 | "She exhaled slowly, then stepped" | | 4 | "Its surface was etched with" | | 5 | "She pivoted, flashlight sweeping the" | | 6 | "She gestured to a narrow" | | 7 | "She’d heard whispers about the" | | 8 | "His eyes, however, were visible—pale," | | 9 | "she called again, her voice" | | 10 | "His chest rose and fell," | | 11 | "she demanded, stepping forward" | | 12 | "She gestured to the market’s" | | 13 | "She had a choice: turn" | | 14 | "Her boots moved before her" |
| | ratio | 0.246 | |
| 33.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 61 | | matches | | 0 | "Detective Harlow Quinn’s boots splashed" | | 1 | "Rain drummed against her leather" | | 2 | "Tommy Herrera’s silhouette vanished around" | | 3 | "She’d seen him slip inside," | | 4 | "she muttered, gripping her flashlight" | | 5 | "The beam cut through the" | | 6 | "A scent of damp earth" | | 7 | "She’d learned to trust her" | | 8 | "A sudden creak echoed from" | | 9 | "Harlow froze, her breath shallow." | | 10 | "The sound wasn’t wood settling—it" | | 11 | "She exhaled slowly, then stepped" | | 12 | "Its surface was etched with" | | 13 | "A voice, low and cautious," | | 14 | "She pivoted, flashlight sweeping the" | | 15 | "Harlow lunged, her boots skidding" | | 16 | "The figure vanished into a" | | 17 | "The air grew colder as" | | 18 | "The underground market pulsed with" | | 19 | "A woman with a scarred" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn’s boots splashed through puddles that reflected the flickering green neon of the Raven’s Nest sign." | | 1 | "She’d learned to trust her instincts; they’d kept her alive during the case that took DS Morris." | | 2 | "The figure vanished into a gap between buildings, leaving a trail of damp footprints that led downward." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "she called again, her voice steady despite the chill crawling up her spine" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "she demanded (demand)" |
| | dialogueSentences | 13 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0.6 | | effectiveRatio | 0.462 | |