| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva’s face tightened barely [barely]" |
| | dialogueSentences | 100 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.04 | | effectiveRatio | 0.02 | |
| 82.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1958 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "softly" | | 1 | "suddenly" | | 2 | "very" | | 3 | "slightly" | | 4 | "slowly" | | 5 | "quickly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 79.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1958 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "silence" | | 1 | "flicked" | | 2 | "familiar" | | 3 | "determined" | | 4 | "stomach" | | 5 | "trembled" |
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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 | 0 | | narrationSentences | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 149 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 224 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1957 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 31 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 105 | | wordCount | 1451 | | uniqueNames | 11 | | maxNameDensity | 3.03 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Silas | 10 | | Rory | 42 | | Eva | 44 | | Thursday | 1 | | Islington | 1 | | Tom | 1 | | London | 2 | | Chinese | 1 | | You | 1 | | Aurora | 1 | | Soho | 1 |
| | persons | | 0 | "Silas" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Tom" | | 4 | "You" |
| | places | | 0 | "Islington" | | 1 | "London" | | 2 | "Soho" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like someone who had learned to be" |
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| 97.80% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.022 | | wordCount | 1957 | | matches | | 0 | "not as she had been but as Rory had carried her" | | 1 | "Not the careful, composed expression she had worn at the bar, but something brief" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 224 | | matches | | 0 | "hated that it" | | 1 | "learned that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 128 | | mean | 15.29 | | std | 18.25 | | cv | 1.194 | | sampleLengths | | 0 | 23 | | 1 | 77 | | 2 | 1 | | 3 | 48 | | 4 | 33 | | 5 | 2 | | 6 | 23 | | 7 | 3 | | 8 | 12 | | 9 | 1 | | 10 | 69 | | 11 | 38 | | 12 | 9 | | 13 | 5 | | 14 | 5 | | 15 | 24 | | 16 | 18 | | 17 | 41 | | 18 | 9 | | 19 | 2 | | 20 | 2 | | 21 | 13 | | 22 | 5 | | 23 | 3 | | 24 | 2 | | 25 | 9 | | 26 | 6 | | 27 | 8 | | 28 | 3 | | 29 | 12 | | 30 | 11 | | 31 | 51 | | 32 | 7 | | 33 | 32 | | 34 | 5 | | 35 | 5 | | 36 | 2 | | 37 | 5 | | 38 | 17 | | 39 | 90 | | 40 | 5 | | 41 | 1 | | 42 | 8 | | 43 | 3 | | 44 | 9 | | 45 | 8 | | 46 | 69 | | 47 | 6 | | 48 | 5 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 149 | | matches | | 0 | "was meant" | | 1 | "was determined" |
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| 75.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 268 | | matches | | 0 | "was laughing" | | 1 | "were trying" | | 2 | "were trying" | | 3 | "was wiping" | | 4 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 224 | | ratio | 0.009 | | matches | | 0 | "“You helped me get here. You found me the room. You let me sleep on your floor. You called when I was scared to leave him.” She kept her voice even; she had learned that much." | | 1 | "She could see the girl too, not as she had been but as Rory had carried her—fierce and sure, her knees always scraped, her chin lifted against whatever came next." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1457 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same grey-green they’d" |
| | adverbCount | 53 | | adverbRatio | 0.0363761153054221 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.0068634179821551134 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 224 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 224 | | mean | 8.74 | | std | 7.03 | | cv | 0.805 | | sampleLengths | | 0 | 23 | | 1 | 30 | | 2 | 18 | | 3 | 29 | | 4 | 1 | | 5 | 12 | | 6 | 6 | | 7 | 17 | | 8 | 13 | | 9 | 7 | | 10 | 11 | | 11 | 3 | | 12 | 12 | | 13 | 2 | | 14 | 7 | | 15 | 16 | | 16 | 3 | | 17 | 12 | | 18 | 1 | | 19 | 2 | | 20 | 27 | | 21 | 4 | | 22 | 22 | | 23 | 14 | | 24 | 29 | | 25 | 9 | | 26 | 9 | | 27 | 5 | | 28 | 5 | | 29 | 20 | | 30 | 4 | | 31 | 14 | | 32 | 4 | | 33 | 13 | | 34 | 15 | | 35 | 13 | | 36 | 7 | | 37 | 2 | | 38 | 2 | | 39 | 2 | | 40 | 6 | | 41 | 7 | | 42 | 5 | | 43 | 3 | | 44 | 2 | | 45 | 9 | | 46 | 6 | | 47 | 8 | | 48 | 3 | | 49 | 3 |
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| 45.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.22767857142857142 | | totalSentences | 224 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 125 | | matches | | 0 | "Then by the dark, blunt" | | 1 | "Then by the little gold" | | 2 | "Then he moved away to" | | 3 | "Once, it would have been" | | 4 | "Then, after a month of" | | 5 | "Then she had stopped calling" | | 6 | "Maybe both of them had." | | 7 | "Then Eva nodded and stepped" | | 8 | "Somewhere upstairs, the pipes knocked" |
| | ratio | 0.072 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 125 | | matches | | 0 | "She knew her by the" | | 1 | "His hazel eyes moved from" | | 2 | "He didn’t ask." | | 3 | "He was good at leaving" | | 4 | "She wore a camel coat" | | 5 | "Her face had thinned." | | 6 | "She looked like someone who" | | 7 | "She set the delivery bag" | | 8 | "he said, with the air" | | 9 | "Her gaze flicked over Rory’s" | | 10 | "It was meant kindly." | | 11 | "She ordered a half pint" | | 12 | "She had a flat in" | | 13 | "She delivered food for a" | | 14 | "They landed hard anyway." | | 15 | "She remembered Eva at sixteen," | | 16 | "They had made promises then," | | 17 | "She knew he was watching," | | 18 | "She kept her voice even;" | | 19 | "They were neat now, nails" |
| | ratio | 0.28 | |
| 72.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 125 | | matches | | 0 | "The woman at the end" | | 1 | "She knew her by the" | | 2 | "Rory stopped with one hand" | | 3 | "The bar smelled of old" | | 4 | "Silas glanced over from behind" | | 5 | "His hazel eyes moved from" | | 6 | "He didn’t ask." | | 7 | "He was good at leaving" | | 8 | "The years between them seemed" | | 9 | "The name, in that voice," | | 10 | "She wore a camel coat" | | 11 | "Her face had thinned." | | 12 | "She looked like someone who" | | 13 | "Rory felt suddenly aware of" | | 14 | "She set the delivery bag" | | 15 | "Rory heard the edge in" | | 16 | "Eva looked at her as" | | 17 | "Silas set a glass of" | | 18 | "he said, with the air" | | 19 | "Rory pulled out the stool" |
| | ratio | 0.776 | |
| 40.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 125 | | matches | | | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 2 | | matches | | 0 | "She knew her by the shape of her shoulders, first: the slight lift on the left when she laughed, as if she were trying to keep the sound from escaping." | | 1 | "They had made promises then, with the confidence of people who mistook wanting something for keeping it." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 100 | | tagDensity | 0.19 | | leniency | 0.38 | | rawRatio | 0 | | effectiveRatio | 0 | |