| 82.35% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 4 | | adverbTags | | 0 | "she said again [again]" | | 1 | "he said carefully [carefully]" | | 2 | "He paused again [again]" | | 3 | "he said quietly [quietly]" |
| | dialogueSentences | 68 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0.167 | | effectiveRatio | 0.118 | |
| 67.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1548 | | totalAiIsmAdverbs | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "perfectly" | | 1 | "carefully" | | 2 | "very" | | 3 | "really" | | 4 | "slightly" | | 5 | "deliberately" |
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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) | |
| 93.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1548 | | totalAiIsms | 2 | | found | | | highlights | | |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "room fell silent" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 89 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 89 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1562 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1198 | | uniqueNames | 11 | | maxNameDensity | 0.58 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 6 | | Moreau | 2 | | Lucien | 7 | | Silence | 1 | | Rory | 4 | | London | 1 | | Thames | 1 | | Carter | 1 | | Brick | 1 | | Lane | 1 | | Ptolemy | 3 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Rory" | | 4 | "Carter" | | 5 | "Ptolemy" |
| | places | | 0 | "London" | | 1 | "Thames" | | 2 | "Brick" | | 3 | "Lane" |
| | globalScore | 1 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 3 | | matches | | 0 | "something like surprise; Lucien, who spoke f" | | 1 | "said, because apparently they were doing thi" | | 2 | "not quite touching, and then just barely touching, and she didn't move her hand away" |
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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 | 1562 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 21.4 | | std | 22.1 | | cv | 1.033 | | sampleLengths | | 0 | 5 | | 1 | 62 | | 2 | 31 | | 3 | 6 | | 4 | 90 | | 5 | 3 | | 6 | 13 | | 7 | 5 | | 8 | 4 | | 9 | 36 | | 10 | 4 | | 11 | 8 | | 12 | 3 | | 13 | 23 | | 14 | 77 | | 15 | 14 | | 16 | 15 | | 17 | 3 | | 18 | 2 | | 19 | 27 | | 20 | 40 | | 21 | 6 | | 22 | 15 | | 23 | 4 | | 24 | 5 | | 25 | 65 | | 26 | 6 | | 27 | 8 | | 28 | 2 | | 29 | 4 | | 30 | 34 | | 31 | 29 | | 32 | 58 | | 33 | 11 | | 34 | 5 | | 35 | 4 | | 36 | 10 | | 37 | 70 | | 38 | 41 | | 39 | 6 | | 40 | 6 | | 41 | 2 | | 42 | 35 | | 43 | 1 | | 44 | 6 | | 45 | 5 | | 46 | 62 | | 47 | 53 | | 48 | 29 | | 49 | 27 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 89 | | matches | | |
| 13.08% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 214 | | matches | | 0 | "was staring" | | 1 | "was sitting" | | 2 | "was reading" | | 3 | "was reading" | | 4 | "were doing" | | 5 | "was doing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 3 | | flaggedSentences | 13 | | totalSentences | 132 | | ratio | 0.098 | | matches | | 0 | "It gave with a groan, and she stepped inside, shrugging out of her jacket and dropping her bag on the nearest stack of books — which was to say, the floor — and freezing." | | 1 | "When she came in, he looked up, and she caught the flash of it — one amber eye, one eye like a hole punched in the night — before he composed his expression into something politely neutral." | | 2 | "Rory set her jacket on the hook behind the door — the only free hook, Eva had six of them loaded with scarves and coats and things that appeared to be ritual sashes — and walked to the kitchen because she needed something to do with her hands." | | 3 | "It was involuntary; she'd done it before she'd thought about it, the bone-deep habit of him, and she stood there looking at the two mugs and felt the back of her neck flush." | | 4 | "He looked at her instead, the way he always looked at her — too long, too level, like he was reading something written in small print." | | 5 | "Something crossed his face that she couldn't name, which was unusual — she was good at faces, good at reading people, it was the thing that had kept her useful and alive in the year since London had cracked open and shown her what lived in its underside." | | 6 | "\"And you are not—\" He paused again, and she watched him search for the word with something like surprise; Lucien, who spoke four languages, who rarely faltered." | | 7 | "She set her mug down on a stack of Eva's research notes — something about ley line degradation in the Thames basin, she didn't look at it — and pulled her knees up, wrapping her arms around them." | | 8 | "The amber eye caught the light; the dark one absorbed it." | | 9 | "It was the first time he'd used her name, really used it, and she felt it the same way she always did — a small, embarrassing jolt, like a step she hadn't seen coming." | | 10 | "The corner of his mouth moved again, and this time she saw it for what it was — rueful, genuine, slightly pained." | | 11 | "This close, she could see the faint asymmetry in his face that his composure usually ironed out — the way his brow pulled slightly, the set of his jaw." | | 12 | "Outside, Brick Lane went about its evening — voices, a motorbike, the warm-spice smell of the curry house drifting up through the floorboards the way it always did this time of night." |
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| 92.97% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1190 | | adjectiveStacks | 1 | | stackExamples | | 0 | "operatic, pink-tongued yawn," |
| | adverbCount | 49 | | adverbRatio | 0.041176470588235294 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.014285714285714285 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 11.83 | | std | 10.83 | | cv | 0.915 | | sampleLengths | | 0 | 5 | | 1 | 28 | | 2 | 34 | | 3 | 25 | | 4 | 6 | | 5 | 6 | | 6 | 34 | | 7 | 10 | | 8 | 9 | | 9 | 37 | | 10 | 3 | | 11 | 13 | | 12 | 5 | | 13 | 4 | | 14 | 19 | | 15 | 17 | | 16 | 4 | | 17 | 5 | | 18 | 3 | | 19 | 3 | | 20 | 4 | | 21 | 19 | | 22 | 48 | | 23 | 10 | | 24 | 19 | | 25 | 5 | | 26 | 9 | | 27 | 15 | | 28 | 3 | | 29 | 2 | | 30 | 27 | | 31 | 7 | | 32 | 33 | | 33 | 6 | | 34 | 12 | | 35 | 3 | | 36 | 1 | | 37 | 3 | | 38 | 5 | | 39 | 46 | | 40 | 19 | | 41 | 6 | | 42 | 8 | | 43 | 2 | | 44 | 4 | | 45 | 29 | | 46 | 5 | | 47 | 6 | | 48 | 18 | | 49 | 5 |
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| 46.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3106060606060606 | | totalSentences | 132 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 76 | | matches | | 0 | "It gave with a groan," | | 1 | "He was staring at the" | | 2 | "He had his cane propped" | | 3 | "His platinum hair was slicked" | | 4 | "His charcoal suit had not" | | 5 | "Her voice was steadier than" | | 6 | "He rolled the scroll closed" | | 7 | "He didn't move" | | 8 | "She filled the kettle at" | | 9 | "She heard him get up." | | 10 | "he said, from somewhere just" | | 11 | "she said again, and she" | | 12 | "She pulled two mugs from" | | 13 | "It was involuntary; she'd done" | | 14 | "She kept her eyes on" | | 15 | "He came to stand in" | | 16 | "he said, The corner of" | | 17 | "He came to sit in" | | 18 | "He didn't drink." | | 19 | "He looked at her instead," |
| | ratio | 0.605 | |
| 6.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 76 | | matches | | 0 | "The third deadbolt always stuck." | | 1 | "Rory dragged her thumbnail along" | | 2 | "It gave with a groan," | | 3 | "Ptolemy sat in the centre" | | 4 | "He was staring at the" | | 5 | "Lucien Moreau was sitting on" | | 6 | "He had his cane propped" | | 7 | "His platinum hair was slicked" | | 8 | "His charcoal suit had not" | | 9 | "Her voice was steadier than" | | 10 | "He rolled the scroll closed" | | 11 | "He didn't move" | | 12 | "Neither of them moved." | | 13 | "Ptolemy yawned, an operatic, pink-tongued" | | 14 | "Rory set her jacket on" | | 15 | "The galley kitchen was barely" | | 16 | "She filled the kettle at" | | 17 | "She heard him get up." | | 18 | "The soft sound of the" | | 19 | "he said, from somewhere just" |
| | ratio | 0.908 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "It gave with a groan, and she stepped inside, shrugging out of her jacket and dropping her bag on the nearest stack of books — which was to say, the floor — and…" | | 1 | "Something crossed his face that she couldn't name, which was unusual — she was good at faces, good at reading people, it was the thing that had kept her useful …" | | 2 | "Lucien Moreau, who operated in certainties and contingencies and carefully managed odds, sat in Eva's cramped armchair and considered the open-ended thing she h…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 68 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0 | | effectiveRatio | 0 | |