| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1574 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 65.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1574 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "quickened" | | 3 | "chill" | | 4 | "raced" | | 5 | "flicker" | | 6 | "flicked" | | 7 | "scanned" | | 8 | "shattered" | | 9 | "stomach" | | 10 | "intensity" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
| | 2 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "The air was thick with" | | 2 | "sent a chill through" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 145 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 168 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 5 | | totalWords | 1547 | | ratio | 0.003 | | matches | | 0 | "realm" | | 1 | "moved" | | 2 | "rippled" | | 3 | "knew" | | 4 | "Now." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1343 | | uniqueNames | 12 | | maxNameDensity | 1.49 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 2 | | Harlow | 20 | | Quinn | 1 | | Morris | 3 | | Raven | 1 | | Nest | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Market | 3 | | Tomás | 11 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Raven" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Veil" |
| | places | | | globalScore | 0.755 | | windowScore | 0.667 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like people" | | 1 | "looked like jars of preserved shadows—a f" |
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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 | 1547 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 168 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 29.19 | | std | 19.16 | | cv | 0.656 | | sampleLengths | | 0 | 73 | | 1 | 48 | | 2 | 60 | | 3 | 61 | | 4 | 5 | | 5 | 39 | | 6 | 51 | | 7 | 44 | | 8 | 17 | | 9 | 79 | | 10 | 26 | | 11 | 5 | | 12 | 16 | | 13 | 39 | | 14 | 13 | | 15 | 54 | | 16 | 8 | | 17 | 20 | | 18 | 25 | | 19 | 11 | | 20 | 34 | | 21 | 6 | | 22 | 29 | | 23 | 12 | | 24 | 53 | | 25 | 12 | | 26 | 16 | | 27 | 41 | | 28 | 34 | | 29 | 55 | | 30 | 4 | | 31 | 46 | | 32 | 46 | | 33 | 39 | | 34 | 19 | | 35 | 34 | | 36 | 27 | | 37 | 42 | | 38 | 46 | | 39 | 35 | | 40 | 22 | | 41 | 7 | | 42 | 14 | | 43 | 40 | | 44 | 6 | | 45 | 27 | | 46 | 38 | | 47 | 6 | | 48 | 31 | | 49 | 14 |
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| 93.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 145 | | matches | | 0 | "was connected" | | 1 | "been sealed" | | 2 | "been found" | | 3 | "was lost" | | 4 | "were lined" | | 5 | "being drawn" |
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| 56.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 232 | | matches | | 0 | "was closing" | | 1 | "were looking" | | 2 | "was chasing" | | 3 | "was letting" | | 4 | "was closing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 27 | | semicolonCount | 0 | | flaggedSentences | 20 | | totalSentences | 168 | | ratio | 0.119 | | matches | | 0 | "The suspect—a wiry man in a dark coat—moved with the kind of urgency that only guilt could fuel." | | 1 | "She rounded a corner just in time to see the man yank open a door she hadn’t noticed before—a blackened slab of wood set into the brick, nearly invisible unless you were looking for it." | | 2 | "She had lost men like this before—DS Morris flashing through her mind, his last case still unsolved, his disappearance still a gnawing hole in her gut." | | 3 | "Then she saw it—a faint green glow seeping from beneath the door." | | 4 | "And if he was connected to the clique—if this was where they hid their secrets—then she couldn’t afford to lose him." | | 5 | "She recognized him from the files—former paramedic, now a ghost in the system." | | 6 | "She’d heard whispers of it in the underworld—an underground black market for things that didn’t exist." | | 7 | "The man she was chasing—the one who had slipped through that door—was he part of this?" | | 8 | "There was something in his bearing—something that told her he knew more than he was letting on." | | 9 | "The Market—if it was real—was a goldmine of information." | | 10 | "“You’re either the bravest cop I’ve ever met or the stupidest.” He reached into his coat and pulled out a small, smooth object—a bone token, carved with symbols she didn’t recognize." | | 11 | "The air smelled of damp earth and something else—something metallic, like old blood." | | 12 | "The steps spiraled downward, the torchlight revealing carvings in the walls—symbols, warnings, things that made Harlow’s skin prickle." | | 13 | "And then there were the people—or the things that looked like people." | | 14 | "But there—near a stall selling what looked like jars of preserved shadows—a figure in a dark coat." | | 15 | "The crowd parted around them, eyes tracking their progress with open curiosity—or hunger." | | 16 | "A knife—but not like any she’d seen." | | 17 | "It was tall—too tall—and its limbs were too long, its joints bending in ways that made her stomach twist." | | 18 | "Its face was a shifting mass of shadows, but its eyes—two pinpricks of red—locked onto her with terrifying intensity." | | 19 | "But this—this was something else." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 476 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.0273109243697479 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0063025210084033615 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 168 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 168 | | mean | 9.21 | | std | 6.35 | | cv | 0.69 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 18 | | 3 | 18 | | 4 | 18 | | 5 | 17 | | 6 | 1 | | 7 | 12 | | 8 | 12 | | 9 | 3 | | 10 | 19 | | 11 | 11 | | 12 | 15 | | 13 | 35 | | 14 | 8 | | 15 | 11 | | 16 | 2 | | 17 | 2 | | 18 | 3 | | 19 | 2 | | 20 | 3 | | 21 | 7 | | 22 | 26 | | 23 | 6 | | 24 | 12 | | 25 | 1 | | 26 | 11 | | 27 | 3 | | 28 | 4 | | 29 | 13 | | 30 | 2 | | 31 | 5 | | 32 | 10 | | 33 | 1 | | 34 | 7 | | 35 | 5 | | 36 | 21 | | 37 | 6 | | 38 | 11 | | 39 | 12 | | 40 | 22 | | 41 | 2 | | 42 | 13 | | 43 | 16 | | 44 | 14 | | 45 | 10 | | 46 | 10 | | 47 | 6 | | 48 | 3 | | 49 | 2 |
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| 43.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.25748502994011974 | | totalSentences | 167 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 130 | | matches | | 0 | "Too late for honest folk" | | 1 | "Just a door." | | 2 | "Then she saw it—a faint" | | 3 | "Then, with a groan of" | | 4 | "Then he bolted, shoving through" | | 5 | "Then the passage opened into" | | 6 | "Then the wall behind the" |
| | ratio | 0.054 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 130 | | matches | | 0 | "She had seen it before," | | 1 | "Her breath came in controlled" | | 2 | "She followed, her sharp jaw" | | 3 | "She rounded a corner just" | | 4 | "It swung shut behind him" | | 5 | "It didn’t budge." | | 6 | "She had lost men like" | | 7 | "She wouldn’t let it happen" | | 8 | "Her pulse quickened." | | 9 | "She knew that bar." | | 10 | "She pressed her shoulder against" | | 11 | "She recognized him from the" | | 12 | "His left forearm bore the" | | 13 | "he said, stepping closer" | | 14 | "His voice was calm, but" | | 15 | "She’d heard whispers of it" | | 16 | "She thought of Morris again," | | 17 | "She weighed her options." | | 18 | "He was a wildcard." | | 19 | "He reached into his coat" |
| | ratio | 0.269 | |
| 48.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 107 | | totalSentences | 130 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The suspect—a wiry man in" | | 3 | "She had seen it before," | | 4 | "Her breath came in controlled" | | 5 | "The leather watch on her" | | 6 | "The man ducked into an" | | 7 | "Harlow didn’t hesitate." | | 8 | "She followed, her sharp jaw" | | 9 | "The alley was narrow, the" | | 10 | "A fire escape groaned overhead," | | 11 | "She rounded a corner just" | | 12 | "It swung shut behind him" | | 13 | "Harlow reached for it, her" | | 14 | "It didn’t budge." | | 15 | "A low growl of frustration" | | 16 | "She had lost men like" | | 17 | "She wouldn’t let it happen" | | 18 | "The same distinctive green as" | | 19 | "Her pulse quickened." |
| | ratio | 0.823 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 130 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 7 | | matches | | 0 | "She’d heard whispers of it in the underworld—an underground black market for things that didn’t exist." | | 1 | "There was something in his bearing—something that told her he knew more than he was letting on." | | 2 | "The air that rushed out was thick, stale, like the breath of something long buried." | | 3 | "The steps spiraled downward, the torchlight revealing carvings in the walls—symbols, warnings, things that made Harlow’s skin prickle." | | 4 | "Then he bolted, shoving through the crowd, knocking over a display of glowing orbs that shattered like glass, releasing a scent like ozone and decay." | | 5 | "And from it stepped something that made her blood turn to ice." | | 6 | "It was tall—too tall—and its limbs were too long, its joints bending in ways that made her stomach twist." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "Tomás said, his voice dry" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Tomás murmured (murmur)" |
| | dialogueSentences | 28 | | tagDensity | 0.179 | | leniency | 0.357 | | rawRatio | 0.2 | | effectiveRatio | 0.071 | |