| 68.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 4 | | adverbTags | | 0 | "she said flatly [flatly]" | | 1 | "Lucien said softly [softly]" | | 2 | "His fingers tightened around [around]" | | 3 | "he said finally [finally]" |
| | dialogueSentences | 61 | | tagDensity | 0.344 | | leniency | 0.689 | | rawRatio | 0.19 | | effectiveRatio | 0.131 | |
| 94.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1681 | | totalAiIsmAdverbs | 2 | | 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) | |
| 82.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1681 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "warmth" | | 1 | "flicked" | | 2 | "familiar" | | 3 | "weight" | | 4 | "unraveling" |
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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 | 123 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 162 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1685 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 37 | | unquotedAttributions | 1 | | matches | | 0 | "Then, because he was Lucien, he corrected himself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1148 | | uniqueNames | 15 | | maxNameDensity | 0.7 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 4 | | Brick | 2 | | Lane | 2 | | Moreau | 1 | | Marseille-soft | 1 | | English | 2 | | Lucien | 8 | | Silas | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Cardiff | 1 | | London | 1 | | Rory | 8 | | Ptolemy | 5 | | French | 1 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Silas" | | 4 | "Yu-Fei" | | 5 | "Cheung" | | 6 | "Rory" | | 7 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "English" | | 3 | "Cardiff" | | 4 | "London" | | 5 | "French" |
| | globalScore | 1 | | windowScore | 1 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 2 | | matches | | 0 | "as if testing the name in the air" | | 1 | "as if respecting an invisible line" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1685 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 162 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 22.47 | | std | 19.58 | | cv | 0.872 | | sampleLengths | | 0 | 6 | | 1 | 101 | | 2 | 33 | | 3 | 4 | | 4 | 42 | | 5 | 2 | | 6 | 36 | | 7 | 27 | | 8 | 14 | | 9 | 21 | | 10 | 18 | | 11 | 19 | | 12 | 34 | | 13 | 6 | | 14 | 19 | | 15 | 10 | | 16 | 13 | | 17 | 27 | | 18 | 6 | | 19 | 39 | | 20 | 12 | | 21 | 7 | | 22 | 102 | | 23 | 10 | | 24 | 22 | | 25 | 26 | | 26 | 11 | | 27 | 14 | | 28 | 5 | | 29 | 32 | | 30 | 8 | | 31 | 26 | | 32 | 8 | | 33 | 16 | | 34 | 31 | | 35 | 83 | | 36 | 16 | | 37 | 4 | | 38 | 34 | | 39 | 9 | | 40 | 5 | | 41 | 29 | | 42 | 4 | | 43 | 35 | | 44 | 5 | | 45 | 25 | | 46 | 32 | | 47 | 14 | | 48 | 44 | | 49 | 6 |
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| 99.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 123 | | matches | | 0 | "was cramped" | | 1 | "was frustrated" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 213 | | matches | | 0 | "was watching" | | 1 | "were getting" | | 2 | "wasn’t saying" |
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| 89.95% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 162 | | ratio | 0.019 | | matches | | 0 | "Rory had learned to put them on in a particular order — top, middle, bottom — because Eva insisted it was unlucky to do it any other way, and because the third click had started to feel like a kind of prayer." | | 1 | "Lucien had come to her with a problem — a nest, he’d said, under the basement kitchen." | | 2 | "He studied her face the way he studied everything — with an information broker’s patience." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 896 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03571428571428571 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0078125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 162 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 162 | | mean | 10.4 | | std | 8.23 | | cv | 0.791 | | sampleLengths | | 0 | 6 | | 1 | 42 | | 2 | 33 | | 3 | 26 | | 4 | 5 | | 5 | 9 | | 6 | 3 | | 7 | 3 | | 8 | 13 | | 9 | 4 | | 10 | 19 | | 11 | 11 | | 12 | 5 | | 13 | 7 | | 14 | 2 | | 15 | 7 | | 16 | 16 | | 17 | 13 | | 18 | 12 | | 19 | 10 | | 20 | 5 | | 21 | 5 | | 22 | 3 | | 23 | 6 | | 24 | 17 | | 25 | 4 | | 26 | 4 | | 27 | 14 | | 28 | 19 | | 29 | 7 | | 30 | 8 | | 31 | 7 | | 32 | 6 | | 33 | 6 | | 34 | 6 | | 35 | 5 | | 36 | 8 | | 37 | 6 | | 38 | 7 | | 39 | 3 | | 40 | 13 | | 41 | 10 | | 42 | 5 | | 43 | 4 | | 44 | 8 | | 45 | 6 | | 46 | 4 | | 47 | 7 | | 48 | 28 | | 49 | 3 |
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| 41.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.25925925925925924 | | totalSentences | 162 | | uniqueOpeners | 42 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 108 | | matches | | 0 | "Then the middle." | | 1 | "Then, because he was Lucien," | | 2 | "Of course he’d called Silas." |
| | ratio | 0.028 | |
| 27.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 108 | | matches | | 0 | "She heard the knock first." | | 1 | "Her hand was already on" | | 2 | "She didn’t need to." | | 3 | "He leaned lightly on an" | | 4 | "She could see the faint" | | 5 | "he said, quietly, as if" | | 6 | "His accent was still Marseille-soft" | | 7 | "She unlocked the top bolt." | | 8 | "She left the bottom for" | | 9 | "He smiled without warmth." | | 10 | "She knew what was inside" | | 11 | "She didn’t move to unhook" | | 12 | "She felt the familiar prick" | | 13 | "He shifted his weight." | | 14 | "She’d been working part-time deliveries" | | 15 | "He’d wanted something subtle, something" | | 16 | "She’d done the research at" | | 17 | "He’d paid her in information" | | 18 | "She’d told him to get" | | 19 | "He’d said she didn’t understand" |
| | ratio | 0.481 | |
| 47.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 108 | | matches | | 0 | "The deadbolts were already a" | | 1 | "Rory had learned to put" | | 2 | "The flat above the curry" | | 3 | "Ptolemy was asleep on the" | | 4 | "She heard the knock first." | | 5 | "Her hand was already on" | | 6 | "She didn’t need to." | | 7 | "The man standing in the" | | 8 | "He leaned lightly on an" | | 9 | "Rory’s fingers froze on the" | | 10 | "The flat was cramped, the" | | 11 | "She could see the faint" | | 12 | "he said, quietly, as if" | | 13 | "His accent was still Marseille-soft" | | 14 | "She unlocked the top bolt." | | 15 | "She left the bottom for" | | 16 | "He smiled without warmth." | | 17 | "Ptolemy chose that moment to" | | 18 | "Rory’s blue eyes flicked over" | | 19 | "The suit was new, or" |
| | ratio | 0.824 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 108 | | matches | (empty) | | ratio | 0 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "He’d wanted something subtle, something that wouldn’t scare the human staff." | | 1 | "She’d done the research at Eva’s kitchen table, surrounded by scrolls, and sketched a containment lattice that was clever enough to be elegant." | | 2 | "He’d been cool and precise and dangerous and kind in a way that made her stupid." | | 3 | "He stopped a careful foot away, as if respecting an invisible line." | | 4 | "Inside, the books leaned and the cat watched and two people who had parted on complicated terms stood in a doorway that was finally, after all this time, open." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, not 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 | 61 | | tagDensity | 0.295 | | leniency | 0.59 | | rawRatio | 0 | | effectiveRatio | 0 | |