| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.395 | | leniency | 0.789 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1472 | | totalAiIsmAdverbs | 1 | | 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) | |
| 45.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1472 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "weight" | | 3 | "standard" | | 4 | "stomach" | | 5 | "gloom" | | 6 | "could feel" | | 7 | "perfect" | | 8 | "methodical" | | 9 | "familiar" | | 10 | "echoed" | | 11 | "glistening" | | 12 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 164 | | matches | (empty) | |
| 81.88% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 2 | | narrationSentences | 164 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 187 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1472 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.05% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1251 | | uniqueNames | 16 | | maxNameDensity | 1.36 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Earlham | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Veil | 1 | | Market | 1 | | Harlow | 17 | | Morris | 6 | | Metropolitan | 1 | | Police | 1 | | Saint | 1 | | Christopher | 1 | | London | 1 | | Quinns | 1 | | Three | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Market" | | 4 | "Harlow" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Earlham" | | 1 | "Street" | | 2 | "London" |
| | globalScore | 0.821 | | windowScore | 0.833 | |
| 56.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 3 | | matches | | 0 | "Letters that seemed to shift when she looked at them directly" | | 1 | "quite masks at all" | | 2 | "walls that seemed to close in around her" |
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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 | 1472 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 187 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 19.37 | | std | 14.08 | | cv | 0.727 | | sampleLengths | | 0 | 14 | | 1 | 50 | | 2 | 28 | | 3 | 32 | | 4 | 8 | | 5 | 37 | | 6 | 17 | | 7 | 35 | | 8 | 8 | | 9 | 3 | | 10 | 43 | | 11 | 8 | | 12 | 30 | | 13 | 32 | | 14 | 2 | | 15 | 41 | | 16 | 45 | | 17 | 21 | | 18 | 26 | | 19 | 5 | | 20 | 5 | | 21 | 10 | | 22 | 25 | | 23 | 8 | | 24 | 2 | | 25 | 28 | | 26 | 11 | | 27 | 2 | | 28 | 47 | | 29 | 9 | | 30 | 20 | | 31 | 18 | | 32 | 2 | | 33 | 12 | | 34 | 2 | | 35 | 38 | | 36 | 34 | | 37 | 26 | | 38 | 4 | | 39 | 11 | | 40 | 14 | | 41 | 2 | | 42 | 29 | | 43 | 9 | | 44 | 42 | | 45 | 24 | | 46 | 2 | | 47 | 15 | | 48 | 2 | | 49 | 33 |
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| 98.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 164 | | matches | | 0 | "been condemned" | | 1 | "was gone" | | 2 | "were hidden" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 228 | | matches | | 0 | "were moving" | | 1 | "wasn't folding" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 187 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1255 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short curly dark hair," |
| | adverbCount | 43 | | adverbRatio | 0.034262948207171316 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008764940239043825 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 187 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 187 | | mean | 7.87 | | std | 6.38 | | cv | 0.811 | | sampleLengths | | 0 | 9 | | 1 | 3 | | 2 | 2 | | 3 | 14 | | 4 | 20 | | 5 | 13 | | 6 | 3 | | 7 | 11 | | 8 | 8 | | 9 | 9 | | 10 | 6 | | 11 | 26 | | 12 | 3 | | 13 | 5 | | 14 | 3 | | 15 | 10 | | 16 | 19 | | 17 | 5 | | 18 | 5 | | 19 | 12 | | 20 | 7 | | 21 | 13 | | 22 | 4 | | 23 | 11 | | 24 | 3 | | 25 | 5 | | 26 | 3 | | 27 | 6 | | 28 | 19 | | 29 | 18 | | 30 | 5 | | 31 | 3 | | 32 | 6 | | 33 | 1 | | 34 | 4 | | 35 | 19 | | 36 | 5 | | 37 | 3 | | 38 | 1 | | 39 | 6 | | 40 | 1 | | 41 | 16 | | 42 | 2 | | 43 | 15 | | 44 | 9 | | 45 | 17 | | 46 | 3 | | 47 | 20 | | 48 | 7 | | 49 | 6 |
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| 73.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.48663101604278075 | | totalSentences | 187 | | uniqueOpeners | 91 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 132 | | matches | | 0 | "Then the ground dropped away." | | 1 | "Then she was gone, slipping" | | 2 | "Too many harmonics." | | 3 | "Then, moving in perfect synchronisation," | | 4 | "Just a hole in the" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 132 | | matches | | 0 | "She didn't slow." | | 1 | "She plunged in after her." | | 2 | "Her radio crackled." | | 3 | "She ignored it." | | 4 | "Her leather watch caught the" | | 5 | "Her father's, before that." | | 6 | "They dealt in objects Harlow" | | 7 | "She spotted the woman immediately." | | 8 | "Her hand moved to her" | | 9 | "She might as well have" | | 10 | "It wasn't a human smile." | | 11 | "He wore a Saint Christopher" | | 12 | "Her voice came out steadier" | | 13 | "He held up his hands," | | 14 | "He glanced at something behind" | | 15 | "They wore coats that moved" | | 16 | "Their faces were hidden beneath" | | 17 | "They ran this market." | | 18 | "They ran everything underneath London," | | 19 | "She had perhaps thirty seconds" |
| | ratio | 0.227 | |
| 92.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 132 | | matches | | 0 | "The stolen wallet weighed heavy" | | 1 | "Harlow Quinn's boots hammered the" | | 2 | "The suspect had turned left" | | 3 | "Rain lashed against her face," | | 4 | "She didn't slow." | | 5 | "The market stood silent behind" | | 6 | "The woman glanced back once," | | 7 | "Harlow didn't hesitate." | | 8 | "She plunged in after her." | | 9 | "The alley narrowed." | | 10 | "Brick walls pressed close, slick" | | 11 | "The suspiration of the city" | | 12 | "Stone steps descended into blackness." | | 13 | "The woman had vanished into" | | 14 | "Harlow pulled her torch from" | | 15 | "The beam cut through the" | | 16 | "Symbols she didn't recognise." | | 17 | "Letters that seemed to shift" | | 18 | "Her radio crackled." | | 19 | "She ignored it." |
| | ratio | 0.735 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 132 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 2 | | matches | | 0 | "The angles were wrong, the teeth too sharp, the eyes reflecting the green light in ways that made Harlow's stomach clench." | | 1 | "And from the darkness beyond the altar, something emerged that Harlow's mind refused to process, something vast and hungry and wrong in ways that made her eyes …" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "She pulled, the familiar weight steadying her" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.105 | | leniency | 0.211 | | rawRatio | 0 | | effectiveRatio | 0 | |