| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1288 | | 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) | |
| 72.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1288 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "warmth" | | 1 | "chill" | | 2 | "glinting" | | 3 | "weight" | | 4 | "echo" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 79 | | matches | (empty) | |
| 88.61% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1288 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1288 | | uniqueNames | 19 | | maxNameDensity | 0.78 | | worstName | "Morris" | | maxWindowNameDensity | 2 | | worstWindowName | "Morris" | | discoveredNames | | Harlow | 8 | | Quinn | 1 | | London | 2 | | Raven | 4 | | Nest | 4 | | Soho | 2 | | Morris | 10 | | Thames | 2 | | Camden | 3 | | Tube | 5 | | Veil | 2 | | Market | 4 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Silas | 3 | | Seville | 1 | | Tomás | 4 | | You | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Camden" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" | | 8 | "Silas" | | 9 | "Tomás" | | 10 | "You" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Thames" | | 3 | "Tube" | | 4 | "Market" | | 5 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.776 | | wordCount | 1288 | | matches | | |
| 82.28% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 79 | | matches | | 0 | "seen that night" | | 1 | "come, that her" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 80.5 | | std | 45.56 | | cv | 0.566 | | sampleLengths | | 0 | 109 | | 1 | 136 | | 2 | 75 | | 3 | 114 | | 4 | 117 | | 5 | 130 | | 6 | 7 | | 7 | 32 | | 8 | 3 | | 9 | 46 | | 10 | 21 | | 11 | 78 | | 12 | 75 | | 13 | 134 | | 14 | 76 | | 15 | 135 |
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| 96.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 79 | | matches | | 0 | "been spotted" | | 1 | "was carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 206 | | matches | | 0 | "were staking" | | 1 | "wasn’t running" | | 2 | "was luring" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 79 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1295 | | adjectiveStacks | 3 | | stackExamples | | 0 | "single polished animal bone," | | 1 | "short curly dark hair" | | 2 | "open, bustling, right beneath her" |
| | adverbCount | 22 | | adverbRatio | 0.01698841698841699 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003088803088803089 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 16.3 | | std | 10.34 | | cv | 0.634 | | sampleLengths | | 0 | 23 | | 1 | 25 | | 2 | 33 | | 3 | 28 | | 4 | 15 | | 5 | 36 | | 6 | 32 | | 7 | 14 | | 8 | 20 | | 9 | 19 | | 10 | 35 | | 11 | 22 | | 12 | 4 | | 13 | 14 | | 14 | 33 | | 15 | 27 | | 16 | 19 | | 17 | 15 | | 18 | 20 | | 19 | 17 | | 20 | 19 | | 21 | 13 | | 22 | 20 | | 23 | 26 | | 24 | 4 | | 25 | 4 | | 26 | 8 | | 27 | 6 | | 28 | 25 | | 29 | 18 | | 30 | 26 | | 31 | 23 | | 32 | 38 | | 33 | 7 | | 34 | 9 | | 35 | 23 | | 36 | 3 | | 37 | 19 | | 38 | 6 | | 39 | 12 | | 40 | 9 | | 41 | 6 | | 42 | 10 | | 43 | 5 | | 44 | 13 | | 45 | 4 | | 46 | 14 | | 47 | 13 | | 48 | 3 | | 49 | 11 |
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| 39.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.27848101265822783 | | totalSentences | 79 | | uniqueOpeners | 22 | |
| 42.19% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 79 | | matches | | | ratio | 0.013 | |
| 57.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 79 | | matches | | 0 | "She ran, her boots slapping" | | 1 | "He’d run before she could" | | 2 | "She’d found the first in" | | 3 | "She knew this stretch." | | 4 | "She’d stood here once before," | | 5 | "He held it up to" | | 6 | "She stepped out from behind" | | 7 | "She bent to pick up" | | 8 | "It was carved with the" | | 9 | "It moved locations every full" | | 10 | "He’d led her straight to" | | 11 | "He kept his hands raised," | | 12 | "You don’t want to go" | | 13 | "You’ll be dead before you" | | 14 | "He’s part of the clique" | | 15 | "I have questions for him." | | 16 | "He doesn’t know nothing beyond" | | 17 | "You want questions, you talk" | | 18 | "He’s known you were staking" | | 19 | "he was luring you." |
| | ratio | 0.405 | |
| 10.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 79 | | matches | | 0 | "Rain slanted into Harlow Quinn’s" | | 1 | "She ran, her boots slapping" | | 2 | "The bar’s secret back exit" | | 3 | "He’d run before she could" | | 4 | "Salt-and-pepper hair, cropped tight to" | | 5 | "The worn leather watch on" | | 6 | "She’d found the first in" | | 7 | "The kid darted past a" | | 8 | "Camden’s high street bled into" | | 9 | "She knew this stretch." | | 10 | "She’d stood here once before," | | 11 | "The kid skidded to a" | | 12 | "Harlow ducked behind a overflowing" | | 13 | "The wood crashed into a" | | 14 | "He held it up to" | | 15 | "A second bone clattered out" | | 16 | "She stepped out from behind" | | 17 | "She bent to pick up" | | 18 | "It was carved with the" | | 19 | "The entry token for the" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 35.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 8 | | matches | | 0 | "She ran, her boots slapping through puddles that swallowed the sound of her stride, the green neon glow of the Raven’s Nest fading behind her." | | 1 | "The kid darted past a line of idling black cabs, and Harlow broke into a sprint, dodging a group of drunk tourists who spilled out of a Camden pub, their shouts…" | | 2 | "Harlow ducked behind a overflowing dumpster, her breath coming in short, sharp bursts, and watched the kid yank three rotting pine boards loose from the entranc…" | | 3 | "The sleeve of his rainjacket slid down his left forearm as he adjusted his grip, revealing a pale, raised scar running the length of the skin." | | 4 | "The leather watch on her wrist clicked as she shifted her weight, her boots splashing in a puddle that pooled around her ankles." | | 5 | "He’s part of the clique that runs the Raven’s Nest." | | 6 | "Or she could step into the dark, into the place that had killed her partner, into the market full of enchanted goods and alchemical substances and things she co…" | | 7 | "The rain picked up again, slamming into her coat, and she stared into the black mouth of the Tube station, weighing her choice, the weight of Morris’s memory pr…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |