| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 31 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "she said softly [softly]" |
| | dialogueSentences | 53 | | tagDensity | 0.585 | | leniency | 1 | | rawRatio | 0.065 | | effectiveRatio | 0.065 | |
| 84.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1963 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "perfectly" | | 1 | "truly" | | 2 | "carefully" | | 3 | "gently" | | 4 | "softly" |
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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) | |
| 71.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1963 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "electric" | | 1 | "pulse" | | 2 | "flicker" | | 3 | "stomach" | | 4 | "traced" | | 5 | "weight" | | 6 | "perfect" | | 7 | "flicked" | | 8 | "raced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 98 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1971 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1603 | | uniqueNames | 31 | | maxNameDensity | 0.69 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 11 | | Brick | 1 | | Lane | 1 | | Shoreditch | 1 | | Ptolemy | 5 | | Moreau | 1 | | Eva | 9 | | Bengali | 1 | | Cool | 2 | | Rain | 1 | | Golden | 1 | | Empress | 1 | | Yu-Fei | 1 | | Pre-Law | 2 | | Aurora | 1 | | Marseille-softened | 1 | | French | 1 | | Lucien | 3 | | Don | 1 | | Luc | 1 | | Avaros | 1 | | Soho | 2 | | You | 1 | | Frenchman | 1 | | Despite | 1 | | Carter | 2 | | Cardiff | 1 | | Evan | 1 | | London | 1 | | Please | 1 | | Clack | 3 |
| | persons | | 0 | "Rory" | | 1 | "Ptolemy" | | 2 | "Moreau" | | 3 | "Eva" | | 4 | "Cool" | | 5 | "Yu-Fei" | | 6 | "Lucien" | | 7 | "Luc" | | 8 | "You" | | 9 | "Carter" | | 10 | "Evan" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Shoreditch" | | 3 | "Bengali" | | 4 | "Golden" | | 5 | "Avaros" | | 6 | "Soho" | | 7 | "Cardiff" | | 8 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 30.95% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 3 | | matches | | 0 | "not quite a smile" | | 1 | "looked like an advertisement for somethin" | | 2 | "as if confessing to a parking ticket" |
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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 | 1971 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 102 | | matches | | 0 | "complaining that his" | | 1 | "was that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 33.41 | | std | 29.08 | | cv | 0.87 | | sampleLengths | | 0 | 24 | | 1 | 87 | | 2 | 8 | | 3 | 11 | | 4 | 91 | | 5 | 3 | | 6 | 49 | | 7 | 8 | | 8 | 11 | | 9 | 68 | | 10 | 60 | | 11 | 18 | | 12 | 46 | | 13 | 125 | | 14 | 16 | | 15 | 11 | | 16 | 3 | | 17 | 9 | | 18 | 14 | | 19 | 62 | | 20 | 6 | | 21 | 75 | | 22 | 42 | | 23 | 48 | | 24 | 11 | | 25 | 7 | | 26 | 25 | | 27 | 57 | | 28 | 31 | | 29 | 6 | | 30 | 11 | | 31 | 73 | | 32 | 5 | | 33 | 21 | | 34 | 64 | | 35 | 26 | | 36 | 2 | | 37 | 2 | | 38 | 51 | | 39 | 73 | | 40 | 36 | | 41 | 38 | | 42 | 12 | | 43 | 68 | | 44 | 41 | | 45 | 65 | | 46 | 33 | | 47 | 97 | | 48 | 57 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 84 | | matches | | |
| 51.30% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 269 | | matches | | 0 | "was winding" | | 1 | "was doing" | | 2 | "was trying" | | 3 | "was watching" | | 4 | "was hurting" | | 5 | "wasn't touching" |
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| 2.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 102 | | ratio | 0.049 | | matches | | 0 | "He ducked under the low lintel — he was 5'11\" and Eva's doorway was built for shorter Victorians — and brought the smell of rain and cedar cologne and London petrol with him. The flat shrank instantly. It was a cramped one-bedroom at the best of times, and with Lucien in it, dripping quietly onto the rug Eva had stolen from a flea market, there was nowhere to stand that wasn't too close to him." | | 1 | "\"Lucien.\" She was across the tiny room before she decided to move, her delivery-bag reflexes from dodging Soho traffic kicking in. Her fingers were already at his shirt, and he caught her wrist — gently, his thumb landing directly over that crescent scar — and the contact stopped them both cold." | | 2 | "When she came back he had his shirt half unbuttoned, a shallow gash along his ribs already closing faster than human skin should. She knelt on the rug — too close, unavoidably close, her knees brushing his shins — and pressed gauze to it anyway because her hands needed something to do that wasn't touching his mouth." | | 3 | "The wards will hold till morning, and —\" He hesitated, the broker's smoothness cracking. \"And I missed you." | | 4 | "Rory looked at him — rain-wet and banged up and absurdly handsome in Eva's terrible orange lamplight, Ptolemy purring on his thighs like a traitor — and felt the hurt and the attraction tangle into something she couldn't out-think." |
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| 92.54% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1032 | | adjectiveStacks | 1 | | stackExamples | | 0 | "stupid, electric, inconvenient kiss" |
| | adverbCount | 43 | | adverbRatio | 0.041666666666666664 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.013565891472868217 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 19.32 | | std | 19.63 | | cv | 1.016 | | sampleLengths | | 0 | 24 | | 1 | 6 | | 2 | 44 | | 3 | 37 | | 4 | 8 | | 5 | 11 | | 6 | 10 | | 7 | 10 | | 8 | 37 | | 9 | 24 | | 10 | 10 | | 11 | 3 | | 12 | 3 | | 13 | 24 | | 14 | 22 | | 15 | 8 | | 16 | 7 | | 17 | 4 | | 18 | 14 | | 19 | 47 | | 20 | 7 | | 21 | 22 | | 22 | 5 | | 23 | 30 | | 24 | 3 | | 25 | 13 | | 26 | 5 | | 27 | 16 | | 28 | 8 | | 29 | 9 | | 30 | 13 | | 31 | 5 | | 32 | 11 | | 33 | 2 | | 34 | 71 | | 35 | 36 | | 36 | 16 | | 37 | 11 | | 38 | 3 | | 39 | 9 | | 40 | 5 | | 41 | 4 | | 42 | 5 | | 43 | 15 | | 44 | 17 | | 45 | 30 | | 46 | 6 | | 47 | 75 | | 48 | 42 | | 49 | 48 |
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| 74.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.47058823529411764 | | totalSentences | 102 | | uniqueOpeners | 48 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 76 | | matches | | 0 | "Somewhere I needed to stop" | | 1 | "Just for tonight." |
| | ratio | 0.026 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 76 | | matches | | 0 | "She hadn't bothered with the" | | 1 | "Her hair was still damp" | | 2 | "She pulled the door open" | | 3 | "He was too crisp for" | | 4 | "His charcoal suit had gone" | | 5 | "He leaned a little heavier" | | 6 | "She didn't move." | | 7 | "His mouth twitched, not quite" | | 8 | "Her hand stayed on the" | | 9 | "He glanced past her shoulder" | | 10 | "He said it simply, like" | | 11 | "She wanted to say no." | | 12 | "Her body had its own" | | 13 | "It had been three weeks" | | 14 | "She had told him to" | | 15 | "she said, which was brilliant," | | 16 | "He covered it fast." | | 17 | "She stepped back because Ptolemy" | | 18 | "He ducked under the low" | | 19 | "He remembered. She could tell" |
| | ratio | 0.579 | |
| 45.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 76 | | matches | | 0 | "The third deadbolt gave under" | | 1 | "She hadn't bothered with the" | | 2 | "Eva's place above the curry" | | 3 | "Her hair was still damp" | | 4 | "She pulled the door open" | | 5 | "Lucien Moreau stood in the" | | 6 | "He was too crisp for" | | 7 | "His charcoal suit had gone" | | 8 | "He leaned a little heavier" | | 9 | "She didn't move." | | 10 | "The tabby pushed his head" | | 11 | "Downstairs someone dropped a tray" | | 12 | "His mouth twitched, not quite" | | 13 | "Her hand stayed on the" | | 14 | "He glanced past her shoulder" | | 15 | "Eva never threw anything away." | | 16 | "Every surface was buried under" | | 17 | "Rory didn't step back" | | 18 | "He said it simply, like" | | 19 | "Rain ticked on the skylight" |
| | ratio | 0.829 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 76 | | matches | | 0 | "Now he was here, and" | | 1 | "If this is about the" |
| | ratio | 0.026 | |
| 50.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 4 | | matches | | 0 | "Her hair was still damp from the walk over from Shoreditch, straight black strands sticking to the back of her neck, and Ptolemy was winding hard around her ank…" | | 1 | "He went still. The amber eye caught the warm lamp light and held it a fraction too long, the way it did when his father's blood was close to the surface. Half-d…" | | 2 | "She found the towel in Eva's airing cupboard and the kit under the sink, her hands moving on autopilot while her mind raced. Brendan Carter's daughter did not l…" | | 3 | "When she came back he had his shirt half unbuttoned, a shallow gash along his ribs already closing faster than human skin should. She knelt on the rug — too clo…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 29 | | uselessAdditionCount | 1 | | matches | | |
| 93.40% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 3 | | fancyTags | | 0 | "Don't flatter are you here (do flatter be)" | | 1 | "he admitted (admit)" | | 2 | "she muttered (mutter)" |
| | dialogueSentences | 53 | | tagDensity | 0.264 | | leniency | 0.528 | | rawRatio | 0.214 | | effectiveRatio | 0.113 | |