| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 839 | | 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) | |
| 64.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 839 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "glint" | | 1 | "quickened" | | 2 | "predictable" | | 3 | "pulse" | | 4 | "flicker" | | 5 | "could feel" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 58 | | matches | | |
| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 61 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 839 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 806 | | uniqueNames | 15 | | maxNameDensity | 0.62 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 5 | | Camden | 1 | | High | 1 | | Street | 1 | | Herrera | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Thames | 1 | | Morris | 2 | | English | 1 | | Tomás | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Tomás" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Soho" | | 5 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 839 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 61 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 46.61 | | std | 25.38 | | cv | 0.544 | | sampleLengths | | 0 | 85 | | 1 | 78 | | 2 | 72 | | 3 | 63 | | 4 | 22 | | 5 | 86 | | 6 | 68 | | 7 | 7 | | 8 | 57 | | 9 | 15 | | 10 | 23 | | 11 | 12 | | 12 | 62 | | 13 | 39 | | 14 | 55 | | 15 | 22 | | 16 | 36 | | 17 | 37 |
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| 93.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 58 | | matches | | 0 | "being followed" | | 1 | "been painted" |
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| 93.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 125 | | matches | | 0 | "was already descending" | | 1 | "was watching" |
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| 96.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 61 | | ratio | 0.016 | | matches | | 0 | "Someone was watching from the rooftops; she could feel it on the back of her neck, the same prickle she had felt the night Morris died." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 808 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.024752475247524754 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0024752475247524753 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 61 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 61 | | mean | 13.75 | | std | 9.91 | | cv | 0.721 | | sampleLengths | | 0 | 21 | | 1 | 30 | | 2 | 6 | | 3 | 28 | | 4 | 7 | | 5 | 32 | | 6 | 13 | | 7 | 12 | | 8 | 14 | | 9 | 22 | | 10 | 4 | | 11 | 24 | | 12 | 2 | | 13 | 6 | | 14 | 14 | | 15 | 3 | | 16 | 13 | | 17 | 21 | | 18 | 15 | | 19 | 5 | | 20 | 6 | | 21 | 12 | | 22 | 4 | | 23 | 6 | | 24 | 14 | | 25 | 10 | | 26 | 19 | | 27 | 7 | | 28 | 36 | | 29 | 39 | | 30 | 6 | | 31 | 8 | | 32 | 15 | | 33 | 7 | | 34 | 4 | | 35 | 24 | | 36 | 4 | | 37 | 25 | | 38 | 11 | | 39 | 4 | | 40 | 9 | | 41 | 14 | | 42 | 5 | | 43 | 7 | | 44 | 4 | | 45 | 38 | | 46 | 20 | | 47 | 9 | | 48 | 4 | | 49 | 26 |
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| 48.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.36065573770491804 | | totalSentences | 61 | | uniqueOpeners | 22 | |
| 59.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 56 | | matches | | | ratio | 0.018 | |
| 62.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 56 | | matches | | 0 | "She had been watching him" | | 1 | "She had seen the glint" | | 2 | "He cut left past the" | | 3 | "Her shoes found the slick" | | 4 | "Her father had called it" | | 5 | "Her last commanding officer had" | | 6 | "Her pulse was steady." | | 7 | "It always was, until it" | | 8 | "He held it up to" | | 9 | "She took a breath and" | | 10 | "She reached the top of" | | 11 | "He didn't look surprised." | | 12 | "He looked tired, and he" | | 13 | "he called up, his voice" | | 14 | "He laughed, short and humourless." | | 15 | "She could see it happen," | | 16 | "Her hand went to her" | | 17 | "She did not turn." | | 18 | "She could radio it in." | | 19 | "She could wait at the" |
| | ratio | 0.393 | |
| 13.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 56 | | matches | | 0 | "The rain had settled into" | | 1 | "Harlow Quinn kept her distance" | | 2 | "The hands read twenty past" | | 3 | "Tomás Herrera had not looked" | | 4 | "She had been watching him" | | 5 | "The green neon above the" | | 6 | "She had seen the glint" | | 7 | "A Saint Christopher, the kind" | | 8 | "The kind of thing a" | | 9 | "He cut left past the" | | 10 | "Quinn quickened her pace." | | 11 | "Her shoes found the slick" | | 12 | "Her father had called it" | | 13 | "Her last commanding officer had" | | 14 | "The street narrowed." | | 15 | "Iron railings, a dead streetlamp," | | 16 | "Tomás slowed at a boarded-up" | | 17 | "The sign had been painted" | | 18 | "Someone had chained the grille." | | 19 | "Someone had also cut the" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 4 | | matches | | 0 | "The rain had settled into the kind of steady, indifferent drizzle that made London look like an old photograph of itself." | | 1 | "The green neon above the Raven's Nest had buzzed behind her as he stepped out, collar up, a hand pressed flat against his chest as if to steady something beneat…" | | 2 | "Tomás slowed at a boarded-up entrance set into the hillside, a squat stone arch above a stairway that dropped into darkness." | | 3 | "Three years ago, she had stood in a corridor very much like this one with DS Morris's blood drying on her glove and a witness who wouldn't stop repeating that t…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "he called up, his voice carrying strangely in the stone" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "he called up (call up)" |
| | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.5 | | effectiveRatio | 0.4 | |