| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 63 | | tagDensity | 0.159 | | leniency | 0.317 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1066 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "precisely" | | 1 | "really" | | 2 | "quickly" | | 3 | "completely" |
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| 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) | |
| 95.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1066 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 59 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1073 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 738 | | uniqueNames | 9 | | maxNameDensity | 0.95 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 7 | | Lucien | 5 | | Moreau | 1 | | Brick | 1 | | Lane | 1 | | French | 1 | | Ptolemy | 4 | | Frenchman | 1 | | Welsh | 1 |
| | persons | | 0 | "Rory" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "French" | | 4 | "Ptolemy" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | 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 | 1073 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 112 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 14.5 | | std | 16.54 | | cv | 1.14 | | sampleLengths | | 0 | 51 | | 1 | 28 | | 2 | 1 | | 3 | 6 | | 4 | 4 | | 5 | 28 | | 6 | 58 | | 7 | 4 | | 8 | 3 | | 9 | 7 | | 10 | 34 | | 11 | 5 | | 12 | 7 | | 13 | 30 | | 14 | 1 | | 15 | 5 | | 16 | 49 | | 17 | 53 | | 18 | 5 | | 19 | 2 | | 20 | 4 | | 21 | 5 | | 22 | 4 | | 23 | 37 | | 24 | 6 | | 25 | 16 | | 26 | 1 | | 27 | 10 | | 28 | 5 | | 29 | 1 | | 30 | 3 | | 31 | 24 | | 32 | 35 | | 33 | 8 | | 34 | 4 | | 35 | 15 | | 36 | 3 | | 37 | 1 | | 38 | 3 | | 39 | 2 | | 40 | 33 | | 41 | 7 | | 42 | 42 | | 43 | 7 | | 44 | 6 | | 45 | 8 | | 46 | 34 | | 47 | 39 | | 48 | 4 | | 49 | 4 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 59 | | matches | | |
| 47.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 131 | | matches | | 0 | "was still sliding" | | 1 | "was leaning" | | 2 | "was not trying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 112 | | ratio | 0.054 | | matches | | 0 | "She'd catalogued it, once — the particular dry rasp Lucien Moreau made when he was about to say something he'd rehearsed in a cab ride over." | | 1 | "Lucien was leaning on the ivory cane the way other men leaned on door frames — an arrangement of limbs designed to look accidental." | | 2 | "He took in the flat the way he took in everything — quickly, completely, and without the decency to pretend he hadn't." | | 3 | "She laughed — a short, unlovely sound." | | 4 | "She watched him do it — the small, careful re-gathering of a man who had forgotten, briefly, to be the Frenchman in the charcoal suit, and remembered now that she'd seen him without it." | | 5 | "She should have stepped back; she didn't, and that was her own fault, and she'd examine it later when he wasn't near enough that she could smell the bergamot on his collar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 734 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.039509536784741145 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008174386920980926 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 9.58 | | std | 9.99 | | cv | 1.043 | | sampleLengths | | 0 | 21 | | 1 | 4 | | 2 | 26 | | 3 | 28 | | 4 | 1 | | 5 | 6 | | 6 | 4 | | 7 | 19 | | 8 | 2 | | 9 | 7 | | 10 | 24 | | 11 | 21 | | 12 | 7 | | 13 | 6 | | 14 | 4 | | 15 | 3 | | 16 | 7 | | 17 | 21 | | 18 | 13 | | 19 | 5 | | 20 | 7 | | 21 | 2 | | 22 | 7 | | 23 | 16 | | 24 | 5 | | 25 | 1 | | 26 | 5 | | 27 | 3 | | 28 | 46 | | 29 | 22 | | 30 | 5 | | 31 | 16 | | 32 | 10 | | 33 | 5 | | 34 | 2 | | 35 | 4 | | 36 | 5 | | 37 | 4 | | 38 | 3 | | 39 | 3 | | 40 | 31 | | 41 | 6 | | 42 | 13 | | 43 | 3 | | 44 | 1 | | 45 | 10 | | 46 | 5 | | 47 | 1 | | 48 | 3 | | 49 | 6 |
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| 58.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4107142857142857 | | totalSentences | 112 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 52 | | matches | | 0 | "She knew that sound." | | 1 | "She'd catalogued it, once —" | | 2 | "She pulled the door open" | | 3 | "She nudged him back with" | | 4 | "She opened the door another" | | 5 | "She stepped back." | | 6 | "She told herself it was" | | 7 | "He took in the flat" | | 8 | "He didn't sit." | | 9 | "She didn't offer." | | 10 | "They stood in the narrow" | | 11 | "He lifted his eyes" | | 12 | "She hated the way her" | | 13 | "He said it like it" | | 14 | "She breathed out through her" | | 15 | "He didn't move." | | 16 | "He set the cane against" | | 17 | "he corrected, with the patience" | | 18 | "She laughed — a short," | | 19 | "She kept her voice low" |
| | ratio | 0.577 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 52 | | matches | | 0 | "The third deadbolt was still" | | 1 | "She knew that sound." | | 2 | "She'd catalogued it, once —" | | 3 | "She pulled the door open" | | 4 | "Ptolemy wound between her ankles," | | 5 | "She nudged him back with" | | 6 | "Lucien was leaning on the" | | 7 | "Charcoal suit, no rain on" | | 8 | "The amber eye caught the" | | 9 | "The black one gave nothing" | | 10 | "She opened the door another" | | 11 | "The other one, smaller, the" | | 12 | "She stepped back." | | 13 | "She told herself it was" | | 14 | "He took in the flat" | | 15 | "Books stacked on the radiator." | | 16 | "Eva's research pinned to the" | | 17 | "A half-drunk mug of tea" | | 18 | "He didn't sit." | | 19 | "She didn't offer." |
| | ratio | 0.923 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 2 | | matches | | 0 | "They stood in the narrow stretch of carpet between the sofa and the kitchenette, and Rory folded her arms because she needed somewhere to put her hands that was…" | | 1 | "She watched him do it — the small, careful re-gathering of a man who had forgotten, briefly, to be the Frenchman in the charcoal suit, and remembered now that s…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "She breathed (breathe)" | | 1 | "he corrected (correct)" |
| | dialogueSentences | 63 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.286 | | effectiveRatio | 0.063 | |