| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1548 | | 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) | |
| 74.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1548 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "clandestine" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "pumping" | | 4 | "weight" | | 5 | "silence" | | 6 | "familiar" |
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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 | 1 | | narrationSentences | 139 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1548 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.52% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1442 | | uniqueNames | 24 | | maxNameDensity | 1.11 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 2 | | Nest | 3 | | Soho | 2 | | Greek | 1 | | Street | 3 | | Silas | 1 | | Quinn | 16 | | Tube | 4 | | Bateman | 1 | | Fitzrovia | 2 | | Mornington | 1 | | Crescent | 1 | | Camden | 2 | | High | 1 | | Ministry | 1 | | Veil | 1 | | Market | 1 | | Morris | 3 | | London | 1 | | Spanish | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Regulations | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Quinn" | | 4 | "Morris" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Regulations" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Tube" | | 4 | "Bateman" | | 5 | "Fitzrovia" | | 6 | "Mornington" | | 7 | "Crescent" | | 8 | "Camden" | | 9 | "High" | | 10 | "Ministry" | | 11 | "London" |
| | globalScore | 0.945 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.646 | | wordCount | 1548 | | matches | | 0 | "not onto another street but into a service corridor" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 24.97 | | std | 21.74 | | cv | 0.871 | | sampleLengths | | 0 | 2 | | 1 | 2 | | 2 | 26 | | 3 | 62 | | 4 | 4 | | 5 | 10 | | 6 | 68 | | 7 | 4 | | 8 | 47 | | 9 | 5 | | 10 | 62 | | 11 | 6 | | 12 | 19 | | 13 | 3 | | 14 | 28 | | 15 | 62 | | 16 | 4 | | 17 | 41 | | 18 | 29 | | 19 | 3 | | 20 | 7 | | 21 | 3 | | 22 | 34 | | 23 | 51 | | 24 | 56 | | 25 | 4 | | 26 | 21 | | 27 | 21 | | 28 | 57 | | 29 | 45 | | 30 | 7 | | 31 | 11 | | 32 | 33 | | 33 | 3 | | 34 | 45 | | 35 | 46 | | 36 | 3 | | 37 | 49 | | 38 | 13 | | 39 | 14 | | 40 | 20 | | 41 | 30 | | 42 | 9 | | 43 | 53 | | 44 | 10 | | 45 | 6 | | 46 | 52 | | 47 | 28 | | 48 | 12 | | 49 | 17 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 139 | | matches | | 0 | "been gutted" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 234 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 149 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1119 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.025915996425379804 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006255585344057194 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 10.39 | | std | 7.55 | | cv | 0.727 | | sampleLengths | | 0 | 2 | | 1 | 2 | | 2 | 26 | | 3 | 20 | | 4 | 28 | | 5 | 3 | | 6 | 1 | | 7 | 10 | | 8 | 4 | | 9 | 6 | | 10 | 4 | | 11 | 12 | | 12 | 13 | | 13 | 35 | | 14 | 1 | | 15 | 7 | | 16 | 4 | | 17 | 28 | | 18 | 19 | | 19 | 5 | | 20 | 8 | | 21 | 15 | | 22 | 25 | | 23 | 14 | | 24 | 6 | | 25 | 3 | | 26 | 4 | | 27 | 12 | | 28 | 3 | | 29 | 18 | | 30 | 10 | | 31 | 6 | | 32 | 4 | | 33 | 20 | | 34 | 4 | | 35 | 16 | | 36 | 12 | | 37 | 4 | | 38 | 13 | | 39 | 2 | | 40 | 10 | | 41 | 16 | | 42 | 12 | | 43 | 2 | | 44 | 9 | | 45 | 6 | | 46 | 3 | | 47 | 7 | | 48 | 3 | | 49 | 15 |
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| 55.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3959731543624161 | | totalSentences | 149 | | uniqueOpeners | 59 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 124 | | matches | | 0 | "Then he pulled." | | 1 | "Then the doors slammed shut" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 124 | | matches | | 0 | "He cut left into Greek" | | 1 | "Her lungs burned, but her" | | 2 | "It kept everything narrowed to" | | 3 | "He knew these streets." | | 4 | "He ducked a delivery trolley," | | 5 | "It was the service cut" | | 6 | "He glanced back." | | 7 | "His hood had fallen." | | 8 | "He pressed it against the" | | 9 | "It hadn't given, exactly." | | 10 | "She knew about it from" | | 11 | "It stood ajar, revealing a" | | 12 | "Her boots struck concrete still," | | 13 | "She burst out into the" | | 14 | "He ran like a hare" | | 15 | "She swore and shoved the" | | 16 | "He turned sharply into Camden" | | 17 | "He made straight for the" | | 18 | "He didn't stop at the" | | 19 | "He slid down the maintenance" |
| | ratio | 0.274 | |
| 48.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 124 | | matches | | 0 | "The kid in the black" | | 1 | "Quinn ran hard after him," | | 2 | "The distinctive green neon sign" | | 3 | "He cut left into Greek" | | 4 | "Quinn cut with him." | | 5 | "Her lungs burned, but her" | | 6 | "The rain had turned the" | | 7 | "It kept everything narrowed to" | | 8 | "He knew these streets." | | 9 | "He ducked a delivery trolley," | | 10 | "It was the service cut" | | 11 | "Quinn followed him into it." | | 12 | "The alley smelled of fat" | | 13 | "The walls were close enough" | | 14 | "The old maps and black-and-white" | | 15 | "The boy was already at" | | 16 | "He glanced back." | | 17 | "His hood had fallen." | | 18 | "A small white shape flashed" | | 19 | "He pressed it against the" |
| | ratio | 0.823 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 124 | | matches | (empty) | | ratio | 0 | |
| 98.90% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 4 | | matches | | 0 | "He ducked a delivery trolley, shouldered through a pair of tourists huddled under a shared umbrella, then disappeared into the mouth of an alley that wasn't an …" | | 1 | "The abandoned station had been gutted and rebuilt into a bazaar that stretched further than the original tunnels should have allowed." | | 2 | "Her black waterproof jacket, her warrant card still clutched in her hand, the rainwater pooling at her feet on stone that had been dry for fifty years." | | 3 | "In his place, stepping out from behind a curtain of hanging bones, stood a tall man in a clean grey suit that had no business in this place." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |