| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "She stepped backward [backward]" |
| | dialogueSentences | 53 | | tagDensity | 0.17 | | leniency | 0.34 | | rawRatio | 0.111 | | effectiveRatio | 0.038 | |
| 92.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1922 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "completely" | | 1 | "slightly" | | 2 | "very" |
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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) | |
| 84.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1922 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "could feel" | | 1 | "silence" | | 2 | "pulse" | | 3 | "perfect" | | 4 | "flicker" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 121 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 121 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 165 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1944 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1377 | | uniqueNames | 12 | | maxNameDensity | 0.73 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 10 | | Golden | 1 | | Empress | 1 | | Moreau | 1 | | Ptolemy | 5 | | Eva | 4 | | Yu-Fei | 2 | | Hackney | 1 | | Sumerian | 1 | | Lucien | 9 | | Flat | 1 | | French | 1 |
| | persons | | 0 | "Rory" | | 1 | "Empress" | | 2 | "Moreau" | | 3 | "Ptolemy" | | 4 | "Eva" | | 5 | "Yu-Fei" | | 6 | "Lucien" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite human, like woodsmoke from a fire burning in a color she couldn't name" |
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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 | 1944 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 165 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 24.3 | | std | 22.93 | | cv | 0.943 | | sampleLengths | | 0 | 48 | | 1 | 68 | | 2 | 3 | | 3 | 3 | | 4 | 24 | | 5 | 5 | | 6 | 54 | | 7 | 3 | | 8 | 4 | | 9 | 2 | | 10 | 52 | | 11 | 10 | | 12 | 57 | | 13 | 64 | | 14 | 8 | | 15 | 6 | | 16 | 27 | | 17 | 4 | | 18 | 37 | | 19 | 25 | | 20 | 2 | | 21 | 33 | | 22 | 9 | | 23 | 8 | | 24 | 9 | | 25 | 74 | | 26 | 11 | | 27 | 5 | | 28 | 6 | | 29 | 66 | | 30 | 43 | | 31 | 11 | | 32 | 5 | | 33 | 24 | | 34 | 48 | | 35 | 9 | | 36 | 13 | | 37 | 17 | | 38 | 3 | | 39 | 2 | | 40 | 31 | | 41 | 30 | | 42 | 10 | | 43 | 20 | | 44 | 3 | | 45 | 12 | | 46 | 15 | | 47 | 44 | | 48 | 64 | | 49 | 12 |
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| 96.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 121 | | matches | | 0 | "been stopped" | | 1 | "was allowed" | | 2 | "was cramped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 238 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 165 | | ratio | 0.079 | | matches | | 0 | "His charcoal suit was immaculate — not a wrinkle, not a thread loose — which meant he hadn't come from a fight." | | 1 | "His mismatched eyes — one amber, one black as a well's bottom — tracked from her face to the takeaway bag and back again." | | 2 | "Rory exhaled through her nose and looked at the ceiling — water-stained, peeling at the edges, no help at all." | | 3 | "She could feel it — that particular quality of silence he produced, denser than other people's." | | 4 | "Lucien bent at the waist — not crouching, because crouching would crease the suit — and scratched behind the cat's ear with two fingers." | | 5 | "He set the leather portfolio on the kitchen table — or what passed for one, a card table buried under Eva's notes about Sumerian binding rites and takeaway menus." | | 6 | "She watched his jaw tighten — just once, a brief flex of muscle under clean-shaven skin — before it smoothed again." | | 7 | "Not toward her — sideways, into the kitchen, angling himself beside the refrigerator covered in Eva's postcards and pinned receipts." | | 8 | "The black one — she could read the black one now, and she wished she couldn't, because what she saw there was grief." | | 9 | "She stopped close enough that she could smell him — cedar and something darker underneath, something not quite human, like woodsmoke from a fire burning in a color she couldn't name." | | 10 | "His hand came up — slow, careful, broadcasting every inch of the movement — and closed over hers." | | 11 | "Just the raw French vowels and the flicker of both eyes — amber and black — holding hers with a steadiness that terrified her." | | 12 | "Turned each lock — one, two, three — and stood with her hand on the last one for a moment, forehead against the cool wood." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1112 | | adjectiveStacks | 1 | | stackExamples | | 0 | "under clean-shaven skin —" |
| | adverbCount | 37 | | adverbRatio | 0.03327338129496403 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007194244604316547 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 165 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 165 | | mean | 11.78 | | std | 11.33 | | cv | 0.962 | | sampleLengths | | 0 | 20 | | 1 | 28 | | 2 | 6 | | 3 | 10 | | 4 | 20 | | 5 | 22 | | 6 | 5 | | 7 | 5 | | 8 | 3 | | 9 | 3 | | 10 | 24 | | 11 | 5 | | 12 | 13 | | 13 | 4 | | 14 | 37 | | 15 | 3 | | 16 | 4 | | 17 | 2 | | 18 | 11 | | 19 | 21 | | 20 | 20 | | 21 | 10 | | 22 | 2 | | 23 | 24 | | 24 | 21 | | 25 | 8 | | 26 | 2 | | 27 | 11 | | 28 | 3 | | 29 | 3 | | 30 | 20 | | 31 | 27 | | 32 | 8 | | 33 | 6 | | 34 | 2 | | 35 | 16 | | 36 | 9 | | 37 | 4 | | 38 | 5 | | 39 | 1 | | 40 | 24 | | 41 | 7 | | 42 | 11 | | 43 | 14 | | 44 | 2 | | 45 | 13 | | 46 | 10 | | 47 | 10 | | 48 | 9 | | 49 | 8 |
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| 45.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3212121212121212 | | totalSentences | 165 | | uniqueOpeners | 53 | |
| 97.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 103 | | matches | | 0 | "Then he straightened and looked" | | 1 | "Just the raw French vowels" | | 2 | "Then she walked back into" |
| | ratio | 0.029 | |
| 45.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 103 | | matches | | 0 | "She pulled the door open" | | 1 | "His charcoal suit was immaculate" | | 2 | "He'd come from somewhere deliberate." | | 3 | "He'd chosen to be here." | | 4 | "His mismatched eyes — one" | | 5 | "She stepped backward into the" | | 6 | "His cane caught it." | | 7 | "She left the door open" | | 8 | "He moved through the narrow" | | 9 | "His cane tapped once against" | | 10 | "She arranged each one with" | | 11 | "he said from the doorway" | | 12 | "She could feel it —" | | 13 | "She sucked it clean and" | | 14 | "His gaze shifted to the" | | 15 | "He set the leather portfolio" | | 16 | "He moved a scroll aside" | | 17 | "He filled the doorway." | | 18 | "She'd burned through the anger" | | 19 | "He absorbed this." |
| | ratio | 0.437 | |
| 66.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 103 | | matches | | 0 | "The third deadbolt stuck the" | | 1 | "She pulled the door open" | | 2 | "Lucien Moreau stood on the" | | 3 | "The other held a slim" | | 4 | "His charcoal suit was immaculate" | | 5 | "He'd come from somewhere deliberate." | | 6 | "He'd chosen to be here." | | 7 | "That was worse." | | 8 | "His mismatched eyes — one" | | 9 | "She stepped backward into the" | | 10 | "His cane caught it." | | 11 | "The smell of cumin drifted" | | 12 | "Rory exhaled through her nose" | | 13 | "She left the door open" | | 14 | "He moved through the narrow" | | 15 | "His cane tapped once against" | | 16 | "The flat was too small" | | 17 | "Rory set the bag on" | | 18 | "Kung pao chicken." | | 19 | "Yu-Fei's garlic noodles." |
| | ratio | 0.786 | |
| 48.54% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 103 | | matches | | 0 | "If she wanted to leave," |
| | ratio | 0.01 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 5 | | matches | | 0 | "Not forceful, just precise, the ivory handle slotting into the gap between door and frame with the ease of someone who'd been stopped from entering places his e…" | | 1 | "Two spring rolls she'd nicked from the kitchen because Yu-Fei owed her for the Hackney delivery that had gone sideways." | | 2 | "She arranged each one with enormous concentration, as though the containers were chess pieces and this was a game she could win by not looking at him." | | 3 | "She'd burned through the anger the first week, sitting in this kitchen at three in the morning with Ptolemy in her lap and Eva's hand in her hair, untangling wh…" | | 4 | "Just the raw French vowels and the flicker of both eyes — amber and black — holding hers with a steadiness that terrified her." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.151 | | leniency | 0.302 | | rawRatio | 0 | | effectiveRatio | 0 | |