| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.24 | | leniency | 0.48 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1642 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "suddenly" |
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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) | |
| 48.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1642 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "flickered" | | 1 | "coded" | | 2 | "glint" | | 3 | "footsteps" | | 4 | "pulse" | | 5 | "quickened" | | 6 | "glinting" | | 7 | "stark" | | 8 | "scanned" | | 9 | "jaw clenched" | | 10 | "stomach" | | 11 | "whisper" | | 12 | "unraveling" | | 13 | "weight" | | 14 | "silence" | | 15 | "raced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 152 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 4 | | totalWords | 1628 | | ratio | 0.002 | | matches | | 0 | "crossroads" | | 1 | "eat" | | 2 | "Eat" | | 3 | "touch" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1425 | | uniqueNames | 15 | | maxNameDensity | 1.33 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Harlow | 19 | | Quinn | 2 | | Raven | 2 | | Nest | 3 | | Silas | 2 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 2 | | Veil | 2 | | Market | 3 | | Tomás | 14 | | Tube | 1 | | Camden | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Tomás" | | 9 | "Camden" |
| | places | | | globalScore | 0.833 | | windowScore | 0.833 | |
| 14.13% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 5 | | matches | | 0 | "as if beckoning her" | | 1 | "quite human" | | 2 | "something like pity" | | 3 | "felt like a physical presence" | | 4 | "looked like people" |
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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 | 1628 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 171 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 31.31 | | std | 22.48 | | cv | 0.718 | | sampleLengths | | 0 | 106 | | 1 | 77 | | 2 | 65 | | 3 | 71 | | 4 | 73 | | 5 | 25 | | 6 | 71 | | 7 | 10 | | 8 | 59 | | 9 | 63 | | 10 | 9 | | 11 | 10 | | 12 | 26 | | 13 | 10 | | 14 | 41 | | 15 | 23 | | 16 | 10 | | 17 | 24 | | 18 | 55 | | 19 | 37 | | 20 | 11 | | 21 | 21 | | 22 | 42 | | 23 | 9 | | 24 | 16 | | 25 | 24 | | 26 | 36 | | 27 | 51 | | 28 | 33 | | 29 | 35 | | 30 | 2 | | 31 | 42 | | 32 | 8 | | 33 | 20 | | 34 | 2 | | 35 | 38 | | 36 | 11 | | 37 | 31 | | 38 | 23 | | 39 | 40 | | 40 | 49 | | 41 | 28 | | 42 | 11 | | 43 | 16 | | 44 | 29 | | 45 | 36 | | 46 | 13 | | 47 | 10 | | 48 | 7 | | 49 | 29 |
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| 91.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 152 | | matches | | 0 | "was gone" | | 1 | "were lined" | | 2 | "was fixed" | | 3 | "been found" | | 4 | "been filled" | | 5 | "been transformed" |
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| 59.94% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 238 | | matches | | 0 | "was, leading" | | 1 | "wasn’t looking" | | 2 | "was looking" | | 3 | "was already standing" | | 4 | "was resisting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 15 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 171 | | ratio | 0.07 | | matches | | 0 | "Her leather watch, worn smooth by years of use, pressed against the inside of her left wrist as she checked the time—11:47 PM." | | 1 | "She caught a glimpse of him—tall, lean, a dark coat flapping behind him as he turned sharply into a narrow side street." | | 2 | "The suspect—she still didn’t have a name for him—glanced back once, his face half-hidden beneath the hood of his coat." | | 3 | "That wasn’t an exit—it was the back entrance to the Nest." | | 4 | "Then, from the darkness beyond, a voice—low, urgent—drifted out." | | 5 | "The interior was dim, the air thick with the scent of aged whiskey and something else—something metallic, like old coins or fresh blood." | | 6 | "She’d felt this before—three years ago, the night she lost Morris." | | 7 | "But the way he was looking at her—like she was already standing on the edge of a cliff—made her hesitate." | | 8 | "Of the way his body had been found—or what was left of it." | | 9 | "But this—this was different." | | 10 | "He pulled out a small, smooth token—white, carved with symbols she didn’t recognize." | | 11 | "A figure detached itself from the crowd—a woman with skin like polished obsidian and a smile that didn’t reach her eyes." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 333 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.03003003003003003 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.006006006006006006 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 9.52 | | std | 6.66 | | cv | 0.699 | | sampleLengths | | 0 | 15 | | 1 | 22 | | 2 | 23 | | 3 | 20 | | 4 | 26 | | 5 | 22 | | 6 | 3 | | 7 | 7 | | 8 | 34 | | 9 | 11 | | 10 | 19 | | 11 | 20 | | 12 | 14 | | 13 | 2 | | 14 | 2 | | 15 | 8 | | 16 | 23 | | 17 | 17 | | 18 | 13 | | 19 | 14 | | 20 | 4 | | 21 | 20 | | 22 | 3 | | 23 | 11 | | 24 | 21 | | 25 | 18 | | 26 | 2 | | 27 | 8 | | 28 | 9 | | 29 | 6 | | 30 | 3 | | 31 | 4 | | 32 | 2 | | 33 | 3 | | 34 | 13 | | 35 | 19 | | 36 | 19 | | 37 | 8 | | 38 | 5 | | 39 | 5 | | 40 | 10 | | 41 | 23 | | 42 | 13 | | 43 | 11 | | 44 | 2 | | 45 | 15 | | 46 | 15 | | 47 | 18 | | 48 | 5 | | 49 | 10 |
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| 44.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.2573099415204678 | | totalSentences | 171 | | uniqueOpeners | 44 | |
| 73.53% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 136 | | matches | | 0 | "Just enough for her to" | | 1 | "Then, from the darkness beyond," | | 2 | "Then, suddenly, the staircase opened" |
| | ratio | 0.022 | |
| 96.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 136 | | matches | | 0 | "Her leather watch, worn smooth" | | 1 | "She hadn’t planned on ending" | | 2 | "She caught a glimpse of" | | 3 | "She wasn’t about to lose" | | 4 | "She pushed forward, her boots" | | 5 | "It was gone before she" | | 6 | "He ducked into the alley" | | 7 | "She rounded a corner and" | | 8 | "She’d been in there once," | | 9 | "She knew that voice." | | 10 | "She’d interviewed him once, after" | | 11 | "He’d been tight-lipped then, his" | | 12 | "She’d written him off as" | | 13 | "She stepped through the door" | | 14 | "His curly dark hair was" | | 15 | "He wasn’t looking at her." | | 16 | "His gaze was fixed on" | | 17 | "She scanned the room, her" | | 18 | "It was slightly ajar." | | 19 | "She took a step forward," |
| | ratio | 0.309 | |
| 40.88% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 136 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn moved with" | | 2 | "Her leather watch, worn smooth" | | 3 | "The neon glow of The" | | 4 | "She hadn’t planned on ending" | | 5 | "She caught a glimpse of" | | 6 | "Harlow’s jaw tightened." | | 7 | "She wasn’t about to lose" | | 8 | "She pushed forward, her boots" | | 9 | "The suspect—she still didn’t have" | | 10 | "It was gone before she" | | 11 | "He ducked into the alley" | | 12 | "The alley was tighter than" | | 13 | "The rain drummed against the" | | 14 | "She rounded a corner and" | | 15 | "A door creaked open at" | | 16 | "Harlow’s pulse quickened." | | 17 | "That wasn’t an exit—it was" | | 18 | "She’d been in there once," | | 19 | "The place had eyes in" |
| | ratio | 0.838 | |
| 36.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 136 | | matches | | 0 | "If the answers were down" |
| | ratio | 0.007 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn moved with the precision of a woman who had spent nearly two decades chasing shadows through these same alleys." | | 1 | "A cold draft slithered out from the hidden passage, carrying with it the scent of damp earth and something older, something that didn’t belong in the world abov…" | | 2 | "The air was thicker here, heavier, as if the world itself was resisting her." | | 3 | "He adjusted the medallion around his neck, as if it might offer him some protection." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "She took, her boots clicking against the wooden floor" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.12 | | leniency | 0.24 | | rawRatio | 0 | | effectiveRatio | 0 | |