| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 98.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2538 | | 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) | |
| 76.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2538 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "pulse" | | 1 | "weight" | | 2 | "trembled" | | 3 | "silence" | | 4 | "etched" | | 5 | "flickered" | | 6 | "velvet" | | 7 | "porcelain" |
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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 | 215 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 215 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 343 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2537 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 36.12% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 109 | | wordCount | 1844 | | uniqueNames | 24 | | maxNameDensity | 2.28 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Wardour | 1 | | Street | 2 | | Raven | 1 | | Nest | 2 | | Harlow | 1 | | Quinn | 42 | | Herrera | 38 | | Old | 1 | | Compton | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Camden | 1 | | Market | 2 | | Morris | 1 | | Candle | 2 | | Row | 2 | | London | 1 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Rain" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Shaftesbury" | | 5 | "Avenue" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Road" | | 9 | "Market" | | 10 | "Candle" | | 11 | "Row" | | 12 | "London" |
| | globalScore | 0.361 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 150 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.788 | | wordCount | 2537 | | matches | | 0 | "not at her face but at her grip, heel clipping her knuckles" | | 1 | "not in prayer but to stop its faint tapping against the zip of his jacket" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 343 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 253 | | mean | 10.03 | | std | 11.3 | | cv | 1.127 | | sampleLengths | | 0 | 19 | | 1 | 42 | | 2 | 6 | | 3 | 5 | | 4 | 4 | | 5 | 48 | | 6 | 11 | | 7 | 6 | | 8 | 2 | | 9 | 2 | | 10 | 34 | | 11 | 2 | | 12 | 2 | | 13 | 8 | | 14 | 6 | | 15 | 44 | | 16 | 2 | | 17 | 12 | | 18 | 33 | | 19 | 3 | | 20 | 4 | | 21 | 4 | | 22 | 6 | | 23 | 17 | | 24 | 4 | | 25 | 7 | | 26 | 5 | | 27 | 44 | | 28 | 6 | | 29 | 6 | | 30 | 2 | | 31 | 6 | | 32 | 6 | | 33 | 3 | | 34 | 2 | | 35 | 19 | | 36 | 7 | | 37 | 35 | | 38 | 6 | | 39 | 1 | | 40 | 2 | | 41 | 46 | | 42 | 6 | | 43 | 21 | | 44 | 5 | | 45 | 2 | | 46 | 4 | | 47 | 2 | | 48 | 12 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 215 | | matches | | 0 | "been removed" | | 1 | "been painted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 324 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 343 | | ratio | 0.006 | | matches | | 0 | "His front wheel snapped sideways; both of them staggered." | | 1 | "Then came smells that had no place in an abandoned Tube station—woodsmoke, hot sugar, damp fur, copper, crushed mint." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1846 | | adjectiveStacks | 2 | | stackExamples | | 0 | "tracked cold beneath her" | | 1 | "bent wrong beneath their" |
| | adverbCount | 24 | | adverbRatio | 0.013001083423618635 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0005417118093174431 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 343 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 343 | | mean | 7.4 | | std | 5.01 | | cv | 0.678 | | sampleLengths | | 0 | 19 | | 1 | 20 | | 2 | 13 | | 3 | 9 | | 4 | 6 | | 5 | 5 | | 6 | 4 | | 7 | 9 | | 8 | 12 | | 9 | 14 | | 10 | 13 | | 11 | 11 | | 12 | 6 | | 13 | 2 | | 14 | 2 | | 15 | 9 | | 16 | 6 | | 17 | 19 | | 18 | 2 | | 19 | 2 | | 20 | 8 | | 21 | 6 | | 22 | 7 | | 23 | 4 | | 24 | 11 | | 25 | 22 | | 26 | 2 | | 27 | 3 | | 28 | 5 | | 29 | 4 | | 30 | 3 | | 31 | 15 | | 32 | 15 | | 33 | 3 | | 34 | 4 | | 35 | 4 | | 36 | 6 | | 37 | 7 | | 38 | 10 | | 39 | 4 | | 40 | 7 | | 41 | 5 | | 42 | 14 | | 43 | 9 | | 44 | 9 | | 45 | 4 | | 46 | 8 | | 47 | 6 | | 48 | 6 | | 49 | 2 |
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| 47.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3032069970845481 | | totalSentences | 343 | | uniqueOpeners | 104 | |
| 32.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 206 | | matches | | 0 | "Then came smells that had" | | 1 | "Further down, a butcher’s rail" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 206 | | matches | | 0 | "Her worn leather watch showed" | | 1 | "She had waited fifty-seven minutes." | | 2 | "His short curls shone with" | | 3 | "He paused beneath the green" | | 4 | "His reflection looked straight at" | | 5 | "He cut between a cab" | | 6 | "He glanced back." | | 7 | "His heel slid off the" | | 8 | "His sleeve tore at the" | | 9 | "He plunged into a narrow" | | 10 | "He seized the bars and" | | 11 | "He kicked once, not at" | | 12 | "She released him before the" | | 13 | "He dropped on the other" | | 14 | "He hit the ground and" | | 15 | "Her coat snagged on a" | | 16 | "She wrenched it free and" | | 17 | "She displayed her warrant card" | | 18 | "He slapped both hands onto" | | 19 | "His front wheel snapped sideways;" |
| | ratio | 0.218 | |
| 8.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 186 | | totalSentences | 206 | | matches | | 0 | "Rain glazed Wardour Street black" | | 1 | "Detective Harlow Quinn stood across" | | 2 | "The bar’s neon raven flashed" | | 3 | "Her worn leather watch showed" | | 4 | "She had waited fifty-seven minutes." | | 5 | "The Nest’s door opened." | | 6 | "Tomás Herrera stepped out, head" | | 7 | "His short curls shone with" | | 8 | "A canvas medical bag hung" | | 9 | "He paused beneath the green" | | 10 | "Quinn turned towards the chemist’s" | | 11 | "His reflection looked straight at" | | 12 | "He cut between a cab" | | 13 | "A horn cracked through the" | | 14 | "Quinn left the kerb at" | | 15 | "Herrera vanished into Old Compton" | | 16 | "Quinn drove through the crowd," | | 17 | "Umbrellas buckled against her." | | 18 | "A man spilled chips down" | | 19 | "He glanced back." |
| | ratio | 0.903 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 206 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 1 | | matches | | 0 | "Then came smells that had no place in an abandoned Tube station—woodsmoke, hot sugar, damp fur, copper, crushed mint." |
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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 | |