| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1203 | | totalAiIsmAdverbs | 2 | | 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) | |
| 50.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1203 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "echoed" | | 1 | "facade" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "weight" | | 5 | "familiar" | | 6 | "chill" | | 7 | "fragmented" | | 8 | "constructed" | | 9 | "etched" |
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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 | 78 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1203 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1117 | | uniqueNames | 11 | | maxNameDensity | 0.98 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 2 | | Quinn | 1 | | Herrera | 10 | | London | 2 | | Saint | 1 | | Christopher | 1 | | Underground | 1 | | Morris | 1 | | Harlow | 11 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "something close to pity" |
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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 | 1203 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 29.34 | | std | 19.07 | | cv | 0.65 | | sampleLengths | | 0 | 57 | | 1 | 58 | | 2 | 68 | | 3 | 39 | | 4 | 3 | | 5 | 14 | | 6 | 56 | | 7 | 37 | | 8 | 27 | | 9 | 30 | | 10 | 22 | | 11 | 62 | | 12 | 41 | | 13 | 57 | | 14 | 35 | | 15 | 23 | | 16 | 18 | | 17 | 44 | | 18 | 72 | | 19 | 3 | | 20 | 38 | | 21 | 9 | | 22 | 3 | | 23 | 20 | | 24 | 44 | | 25 | 8 | | 26 | 24 | | 27 | 24 | | 28 | 15 | | 29 | 19 | | 30 | 20 | | 31 | 25 | | 32 | 51 | | 33 | 3 | | 34 | 11 | | 35 | 14 | | 36 | 21 | | 37 | 14 | | 38 | 15 | | 39 | 44 | | 40 | 15 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 78 | | matches | | 0 | "was choked" | | 1 | "was hidden" | | 2 | "was slicked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 167 | | matches | | 0 | "was heading" | | 1 | "was nursing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 86 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1124 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, grease-hardened leather" |
| | adverbCount | 24 | | adverbRatio | 0.021352313167259787 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006227758007117438 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 13.99 | | std | 6.61 | | cv | 0.472 | | sampleLengths | | 0 | 19 | | 1 | 15 | | 2 | 23 | | 3 | 8 | | 4 | 28 | | 5 | 17 | | 6 | 5 | | 7 | 15 | | 8 | 25 | | 9 | 13 | | 10 | 15 | | 11 | 3 | | 12 | 23 | | 13 | 13 | | 14 | 3 | | 15 | 14 | | 16 | 4 | | 17 | 24 | | 18 | 28 | | 19 | 7 | | 20 | 14 | | 21 | 16 | | 22 | 12 | | 23 | 15 | | 24 | 14 | | 25 | 16 | | 26 | 18 | | 27 | 4 | | 28 | 16 | | 29 | 14 | | 30 | 10 | | 31 | 15 | | 32 | 7 | | 33 | 12 | | 34 | 9 | | 35 | 20 | | 36 | 34 | | 37 | 6 | | 38 | 10 | | 39 | 7 | | 40 | 15 | | 41 | 20 | | 42 | 9 | | 43 | 14 | | 44 | 13 | | 45 | 5 | | 46 | 16 | | 47 | 12 | | 48 | 16 | | 49 | 17 |
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| 53.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.32558139534883723 | | totalSentences | 86 | | uniqueOpeners | 28 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Instead, his fingers tightened around" |
| | ratio | 0.013 | |
| 91.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 78 | | matches | | 0 | "Her boots kicked up sprays" | | 1 | "She reached down, her thumb" | | 2 | "He collided with a row" | | 3 | "He scrambled up, his hands" | | 4 | "She leaped over a rolling" | | 5 | "He rounded a sharp corner," | | 6 | "She tucked her arms close" | | 7 | "They were in the yard" | | 8 | "He was heading down." | | 9 | "She paused at the top" | | 10 | "It wasn't the sound of" | | 11 | "She began her descent, her" | | 12 | "She kept her left hand" | | 13 | "It smelled of sulfur, damp" | | 14 | "She peered around the curve." | | 15 | "He was nursing his left" | | 16 | "His face was hidden beneath" | | 17 | "His olive skin was slicked" | | 18 | "He looked at the gun," | | 19 | "He didn't raise his hands." |
| | ratio | 0.321 | |
| 24.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 78 | | matches | | 0 | "Harlow Quinn kept her eyes" | | 1 | "Tomás Herrera ran with the" | | 2 | "Her boots kicked up sprays" | | 3 | "She reached down, her thumb" | | 4 | "Midnight was a shadow away." | | 5 | "Herrera bolted across the slick" | | 6 | "He collided with a row" | | 7 | "A shower of discarded takeaway" | | 8 | "He scrambled up, his hands" | | 9 | "Harlow didn't slow." | | 10 | "She leaped over a rolling" | | 11 | "The sharp scent of iron" | | 12 | "The wind whipped the words" | | 13 | "Herrera didn't look back." | | 14 | "He rounded a sharp corner," | | 15 | "The silver Saint Christopher medallion" | | 16 | "Harlow reached the fence, her" | | 17 | "She tucked her arms close" | | 18 | "The jagged wire bit into" | | 19 | "They were in the yard" |
| | ratio | 0.872 | |
| 64.10% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 78 | | matches | | 0 | "Even from here, Harlow could" |
| | ratio | 0.013 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 4 | | matches | | 0 | "He collided with a row of metal rubbish bins, sending them crashing onto the pavement with a metallic roar that echoed through the narrow alleyways." | | 1 | "She leaped over a rolling bin, her footing secure, her eyes tracking the dark wet smear of blood he left on the brickwork." | | 2 | "It smelled of sulfur, damp tallow, and a heavy, sickly sweetness that reminded her of crushed cardamom and old paper." | | 3 | "The scent of exotic spices and burning copper filled her nose, warring with the cold, familiar smell of London rain dripping from her coat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 1 | | effectiveRatio | 0.222 | |