| 72.34% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 3 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" | | 1 | "she said quietly [quietly]" | | 2 | "Silas said dryly [dryly]" |
| | dialogueSentences | 47 | | tagDensity | 0.468 | | leniency | 0.936 | | rawRatio | 0.136 | | effectiveRatio | 0.128 | |
| 94.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1749 | | totalAiIsmAdverbs | 2 | | found | | 0 | | adverb | "reluctantly" | | count | 1 |
| | 1 | |
| | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 88.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1749 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "reminder" | | 2 | "standard" | | 3 | "stark" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1761 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1409 | | uniqueNames | 24 | | maxNameDensity | 0.99 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Rory | 14 | | Soho | 3 | | Golden | 2 | | Empress | 2 | | Cardiff | 7 | | London | 3 | | Lynx | 1 | | Africa | 1 | | Eva | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Born | 1 | | Pre-Law | 1 | | University | 1 | | Brendan | 1 | | Silas | 11 | | Evan | 3 | | Year | 1 | | Six | 1 | | Mikey | 6 | | Prague | 1 | | Thames | 1 |
| | persons | | 0 | "Nest" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Yu-Fei" | | 4 | "Cheung" | | 5 | "Brendan" | | 6 | "Silas" | | 7 | "Evan" | | 8 | "Mikey" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "Golden" | | 3 | "Cardiff" | | 4 | "London" | | 5 | "Africa" | | 6 | "Prague" | | 7 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 28.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like the kind of man who took the" | | 1 | "smelled like rain and expensive cologne an" | | 2 | "sounded like an apology" | | 3 | "felt like grief" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.568 | | wordCount | 1761 | | matches | | 0 | "Not for him, exactly, but for the two people they had been" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 161 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 27.52 | | std | 23.28 | | cv | 0.846 | | sampleLengths | | 0 | 23 | | 1 | 82 | | 2 | 55 | | 3 | 22 | | 4 | 10 | | 5 | 26 | | 6 | 56 | | 7 | 8 | | 8 | 2 | | 9 | 116 | | 10 | 1 | | 11 | 25 | | 12 | 4 | | 13 | 29 | | 14 | 20 | | 15 | 93 | | 16 | 29 | | 17 | 8 | | 18 | 61 | | 19 | 38 | | 20 | 74 | | 21 | 9 | | 22 | 30 | | 23 | 21 | | 24 | 8 | | 25 | 19 | | 26 | 65 | | 27 | 6 | | 28 | 40 | | 29 | 37 | | 30 | 15 | | 31 | 6 | | 32 | 9 | | 33 | 2 | | 34 | 64 | | 35 | 11 | | 36 | 5 | | 37 | 8 | | 38 | 12 | | 39 | 25 | | 40 | 14 | | 41 | 36 | | 42 | 24 | | 43 | 16 | | 44 | 12 | | 45 | 52 | | 46 | 12 | | 47 | 46 | | 48 | 38 | | 49 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 135 | | matches | | 0 | "being asked" | | 1 | "was gone" |
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| 33.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 240 | | matches | | 0 | "was still buzzing" | | 1 | "was wiping" | | 2 | "was raining" | | 3 | "was standing" | | 4 | "was hugging" | | 5 | "was trying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 161 | | ratio | 0.006 | | matches | | 0 | "The intelligent, quick, out-of-the-box thinking voice that had gotten her out of Cardiff in the first place, with Eva's terrified voice on the phone - just come to London, Rory, please - and a duffel bag in the middle of the night after Evan." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 817 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 32 | | adverbRatio | 0.03916768665850673 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008567931456548347 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 10.94 | | std | 8.41 | | cv | 0.769 | | sampleLengths | | 0 | 23 | | 1 | 3 | | 2 | 2 | | 3 | 34 | | 4 | 26 | | 5 | 14 | | 6 | 3 | | 7 | 13 | | 8 | 3 | | 9 | 27 | | 10 | 3 | | 11 | 9 | | 12 | 22 | | 13 | 10 | | 14 | 6 | | 15 | 20 | | 16 | 3 | | 17 | 13 | | 18 | 22 | | 19 | 18 | | 20 | 6 | | 21 | 2 | | 22 | 2 | | 23 | 21 | | 24 | 3 | | 25 | 5 | | 26 | 5 | | 27 | 33 | | 28 | 5 | | 29 | 2 | | 30 | 13 | | 31 | 29 | | 32 | 1 | | 33 | 4 | | 34 | 14 | | 35 | 7 | | 36 | 3 | | 37 | 1 | | 38 | 13 | | 39 | 7 | | 40 | 9 | | 41 | 5 | | 42 | 2 | | 43 | 11 | | 44 | 2 | | 45 | 19 | | 46 | 20 | | 47 | 10 | | 48 | 28 | | 49 | 4 |
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| 43.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3105590062111801 | | totalSentences | 161 | | uniqueOpeners | 50 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 113 | | matches | | 0 | "Of course he was older." | | 1 | "Bright blue, hers were, her" | | 2 | "Of course he remembered." | | 3 | "Maybe not ever." |
| | ratio | 0.035 | |
| 14.69% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 58 | | totalSentences | 113 | | matches | | 0 | "It was late." | | 1 | "She had her bike chained" | | 2 | "Her bag from Golden Empress" | | 3 | "Her shoulders ached." | | 4 | "He did that." | | 5 | "He was wiping down the" | | 6 | "It was raining." | | 7 | "She could hear it ticking" | | 8 | "She knew that voice before" | | 9 | "His hazel eyes narrowed, then" | | 10 | "He had grey-streaked auburn hair" | | 11 | "He was standing just inside" | | 12 | "He was older." | | 13 | "He looked like the kind" | | 14 | "His eyes found hers." | | 15 | "His were brown and had" | | 16 | "His mouth opened." | | 17 | "He crossed the bar in" | | 18 | "He smelled like rain and" | | 19 | "Her straight shoulder-length black hair" |
| | ratio | 0.513 | |
| 21.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 99 | | totalSentences | 113 | | matches | | 0 | "The bell over the door" | | 1 | "It was late." | | 2 | "The after-midnight hour when Soho" | | 3 | "She had her bike chained" | | 4 | "Her bag from Golden Empress" | | 5 | "Her shoulders ached." | | 6 | "Silas had left a pint" | | 7 | "He did that." | | 8 | "He was wiping down the" | | 9 | "It was raining." | | 10 | "She could hear it ticking" | | 11 | "The door opened again and" | | 12 | "Rory's hand stilled around her" | | 13 | "She knew that voice before" | | 14 | "Silas looked up." | | 15 | "His hazel eyes narrowed, then" | | 16 | "He had grey-streaked auburn hair" | | 17 | "The silver signet ring on" | | 18 | "He was standing just inside" | | 19 | "He was older." |
| | ratio | 0.876 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 113 | | matches | (empty) | | ratio | 0 | |
| 12.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 10 | | matches | | 0 | "The after-midnight hour when Soho was still buzzing outside but the bar had gone soft and quiet inside, the rush tapering to the last faithful drunks and the pe…" | | 1 | "She had her bike chained to the railing out front under the distinctive green neon sign that buzzed and threw sick light onto the wet pavement." | | 2 | "He was wiping down the far end of the bar, his movements economical despite the slight limp in his left leg that got worse when it rained." | | 3 | "He was standing just inside the doorway, shaking water from a dark wool coat that cost more than her monthly rent." | | 4 | "The kid she had grown up three doors down from in Cardiff, who'd had a permanent scab on his knee and a lopsided front tooth, had filled out into something shar…" | | 5 | "The intelligent, quick, out-of-the-box thinking voice that had gotten her out of Cardiff in the first place, with Eva's terrified voice on the phone - just come…" | | 6 | "Her mother, who taught Year Six and still underlined things in red pen." | | 7 | "She was twenty-five and sitting in a dimly lit bar in Soho that was covered wall to wall in old maps and black-and-white photographs, places Silas had been and …" | | 8 | "Silas placed a whisky in front of Mikey without being asked and disappeared into the back, past the bookshelf that wasn't just a bookshelf." | | 9 | "Silas reappeared, carrying a crate of glasses, his knee clicking faintly as he moved." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "Silas said dryly, without looking up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 18 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.383 | | leniency | 0.766 | | rawRatio | 0 | | effectiveRatio | 0 | |