| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.024 | | leniency | 0.049 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2049 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "really" | | 1 | "carefully" | | 2 | "softly" | | 3 | "quickly" | | 4 | "very" |
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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) | |
| 24.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2049 | | totalAiIsms | 31 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | | | 24 | |
| | highlights | | 0 | "tracing" | | 1 | "weight" | | 2 | "standard" | | 3 | "echoed" | | 4 | "fluttered" | | 5 | "shattered" | | 6 | "scanned" | | 7 | "velvet" | | 8 | "perfect" | | 9 | "stark" | | 10 | "silk" | | 11 | "etched" | | 12 | "magnetic" | | 13 | "vibrated" | | 14 | "dancing" | | 15 | "echoing" | | 16 | "calculating" | | 17 | "footsteps" | | 18 | "rhythmic" | | 19 | "whisper" | | 20 | "flickered" | | 21 | "chill" | | 22 | "silence" | | 23 | "familiar" | | 24 | "synchronized" |
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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 | 112 | | matches | (empty) | |
| 66.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 3 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2040 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1416 | | uniqueNames | 19 | | maxNameDensity | 1.48 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 21 | | Tube | 1 | | Blitz | 1 | | Detective | 2 | | Sergeant | 1 | | Miller | 6 | | North | 1 | | British | 1 | | Museum | 1 | | Morris | 1 | | Eva | 6 | | Kowalski | 2 | | Crunching | 1 | | Victorian | 1 | | Veil | 2 | | Compass | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Detective" | | 3 | "Sergeant" | | 4 | "Miller" | | 5 | "Morris" | | 6 | "Eva" | | 7 | "Kowalski" |
| | places | | 0 | "Camden" | | 1 | "British" | | 2 | "Compass" |
| | globalScore | 0.758 | | windowScore | 0.667 | |
| 97.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like velvet against her fingertip" | | 1 | "looked like someone who knew the shape of" |
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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 | 2040 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 152 | | matches | | 0 | "reinforces that predated" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 27.57 | | std | 17.64 | | cv | 0.64 | | sampleLengths | | 0 | 64 | | 1 | 43 | | 2 | 28 | | 3 | 3 | | 4 | 56 | | 5 | 29 | | 6 | 58 | | 7 | 5 | | 8 | 11 | | 9 | 60 | | 10 | 17 | | 11 | 28 | | 12 | 61 | | 13 | 15 | | 14 | 11 | | 15 | 23 | | 16 | 41 | | 17 | 55 | | 18 | 19 | | 19 | 30 | | 20 | 54 | | 21 | 8 | | 22 | 12 | | 23 | 7 | | 24 | 32 | | 25 | 8 | | 26 | 18 | | 27 | 50 | | 28 | 16 | | 29 | 31 | | 30 | 42 | | 31 | 31 | | 32 | 53 | | 33 | 2 | | 34 | 41 | | 35 | 13 | | 36 | 12 | | 37 | 31 | | 38 | 29 | | 39 | 11 | | 40 | 23 | | 41 | 31 | | 42 | 19 | | 43 | 8 | | 44 | 48 | | 45 | 18 | | 46 | 16 | | 47 | 25 | | 48 | 18 | | 49 | 47 |
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| 92.73% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 112 | | matches | | 0 | "was draped" | | 1 | "were etched" | | 2 | "were carved" | | 3 | "was gone" |
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| 56.94% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 233 | | matches | | 0 | "was beginning" | | 1 | "was bleeding" | | 2 | "was still clicking" | | 3 | "was spreading" | | 4 | "was pulsing" |
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| 30.08% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 152 | | ratio | 0.039 | | matches | | 0 | "The walls transitioned from standard-issue transit tiles to something older—rough-hewn stone and timber reinforces that predated the Blitz." | | 1 | "She moved toward a tipped-over display case made of dark, polished wood—a stark contrast to the rotting crates nearby." | | 2 | "Sigils—jagged, hungry-looking things—were etched into the casing." | | 3 | "She recognized the typeface on one of the spines—a specific, archaic font she’d seen in a file regarding a certain research assistant at the British Museum." | | 4 | "She remembered the way Morris’s eyes had looked at the end—the same hollow, echoing emptiness as the man on the tracks." | | 5 | "Only the sound of something heavy dragging itself through the tunnels further down the line—a rhythmic, metallic scrape that echoed against the Victorian stone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1436 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.022284122562674095 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006963788300835654 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 13.42 | | std | 7.55 | | cv | 0.562 | | sampleLengths | | 0 | 13 | | 1 | 17 | | 2 | 9 | | 3 | 25 | | 4 | 12 | | 5 | 15 | | 6 | 12 | | 7 | 4 | | 8 | 10 | | 9 | 18 | | 10 | 3 | | 11 | 8 | | 12 | 15 | | 13 | 11 | | 14 | 12 | | 15 | 10 | | 16 | 29 | | 17 | 3 | | 18 | 15 | | 19 | 5 | | 20 | 16 | | 21 | 7 | | 22 | 12 | | 23 | 5 | | 24 | 11 | | 25 | 13 | | 26 | 5 | | 27 | 13 | | 28 | 3 | | 29 | 26 | | 30 | 17 | | 31 | 28 | | 32 | 7 | | 33 | 12 | | 34 | 6 | | 35 | 3 | | 36 | 21 | | 37 | 12 | | 38 | 15 | | 39 | 11 | | 40 | 23 | | 41 | 19 | | 42 | 12 | | 43 | 10 | | 44 | 18 | | 45 | 7 | | 46 | 4 | | 47 | 6 | | 48 | 20 | | 49 | 19 |
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| 43.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3026315789473684 | | totalSentences | 152 | | uniqueOpeners | 46 | |
| 89.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 112 | | matches | | 0 | "Further down the spiral stairs," | | 1 | "Instead, long, rhythmic grooves were" | | 2 | "Only the sound of something" |
| | ratio | 0.027 | |
| 77.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 112 | | matches | | 0 | "It hung heavy with the" | | 1 | "Her flashlight cut a harsh" | | 2 | "She adjusted the worn leather" | | 3 | "He gestured toward a huddle" | | 4 | "She walked past him, her" | | 5 | "She crouched by the body." | | 6 | "His skin held a translucent," | | 7 | "She leaned closer." | | 8 | "She scanned the as the" | | 9 | "They were altars." | | 10 | "She moved toward a tipped-over" | | 11 | "She flipped the lid." | | 12 | "It spun in a lazy," | | 13 | "It vibrated, pointing directly at" | | 14 | "She noticed a faint shimmer" | | 15 | "He clicked the trigger, the" | | 16 | "He squinted at the readout," | | 17 | "She recognized the typeface on" | | 18 | "She remembered the way Morris’s" | | 19 | "She stepped into the alcove," |
| | ratio | 0.357 | |
| 18.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 99 | | totalSentences | 112 | | matches | | 0 | "The iron gates of the" | | 1 | "Detective Harlow Quinn stepped through" | | 2 | "The air in the abandoned" | | 3 | "It hung heavy with the" | | 4 | "Her flashlight cut a harsh" | | 5 | "She adjusted the worn leather" | | 6 | "This one felt bloated." | | 7 | "The walls transitioned from standard-issue" | | 8 | "The voice echoed, distorted by" | | 9 | "Quinn reached the platform level" | | 10 | "Miller looked pale under the" | | 11 | "He gestured toward a huddle" | | 12 | "Quinn didn't answer." | | 13 | "She walked past him, her" | | 14 | "She crouched by the body." | | 15 | "The man lay among shattered" | | 16 | "His skin held a translucent," | | 17 | "Quinn pulled a pen from" | | 18 | "The fingertips weren't just dirty." | | 19 | "A deep, violet stain saturated" |
| | ratio | 0.884 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 112 | | matches | (empty) | | ratio | 0 | |
| 55.98% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 9 | | matches | | 0 | "It hung heavy with the scent of damp brick, ozone, and a cloying sweetness that reminded her of lilies left too long in a vase." | | 1 | "She walked past him, her military bearing making the younger detective seem slouching and uncertain." | | 2 | "One table held rows of glass vials filled with a shimmering, iridescent liquid that hummed at the edge of her hearing." | | 3 | "The brass was heavy, bitten by a thick patina of verdigris that felt like velvet against her fingertip." | | 4 | "Instead, long, rhythmic grooves were carved into the metal, as if something heavy and multifaceted had dragged itself along the line." | | 5 | "The violet ink from the compass’s sigils was bleeding onto her glove, soaking through the latex and staining her skin." | | 6 | "She adjusted the leather watch on her wrist, marking the second the world stopped making sense." | | 7 | "The violet light on the rails was spreading, illuminating symbols carved into the ties that hadn't been there a minute before." | | 8 | "Only the sound of something heavy dragging itself through the tunnels further down the line—a rhythmic, metallic scrape that echoed against the Victorian stone." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |