| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said instead [instead]" |
| | dialogueSentences | 115 | | tagDensity | 0.191 | | leniency | 0.383 | | rawRatio | 0.045 | | effectiveRatio | 0.017 | |
| 92.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1910 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "gently" | | 2 | "slowly" |
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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) | |
| 84.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1910 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "silence" | | 1 | "flicked" | | 2 | "perfect" |
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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 | 132 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 132 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 225 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1908 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 1 | | matches | | 0 | "She remembered the two of them walking down by the river, Eva talking too fast, Rory saying she was fine." |
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| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 83 | | wordCount | 1311 | | uniqueNames | 9 | | maxNameDensity | 2.82 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Rory" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Soho | 2 | | Nest | 1 | | Rory | 37 | | Eva | 33 | | Cardiff | 2 | | London | 1 | | Silas | 5 |
| | persons | | 0 | "Empress" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.089 | | windowScore | 0 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like someone who had learned to ma" | | 1 | "looked like to Eva" |
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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 | 1908 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 225 | | matches | | 0 | "said that name" | | 1 | "see that the" | | 2 | "found that wanting" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 136 | | mean | 14.03 | | std | 17.14 | | cv | 1.221 | | sampleLengths | | 0 | 67 | | 1 | 18 | | 2 | 4 | | 3 | 67 | | 4 | 3 | | 5 | 2 | | 6 | 2 | | 7 | 2 | | 8 | 46 | | 9 | 7 | | 10 | 44 | | 11 | 44 | | 12 | 5 | | 13 | 9 | | 14 | 1 | | 15 | 19 | | 16 | 13 | | 17 | 6 | | 18 | 3 | | 19 | 4 | | 20 | 5 | | 21 | 27 | | 22 | 13 | | 23 | 63 | | 24 | 25 | | 25 | 6 | | 26 | 3 | | 27 | 4 | | 28 | 4 | | 29 | 7 | | 30 | 18 | | 31 | 22 | | 32 | 1 | | 33 | 63 | | 34 | 11 | | 35 | 2 | | 36 | 2 | | 37 | 5 | | 38 | 69 | | 39 | 4 | | 40 | 7 | | 41 | 7 | | 42 | 7 | | 43 | 2 | | 44 | 3 | | 45 | 53 | | 46 | 7 | | 47 | 5 | | 48 | 3 | | 49 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 132 | | matches | | |
| 63.39% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 244 | | matches | | 0 | "was always pushing" | | 1 | "was choosing" | | 2 | "was mostly choosing" | | 3 | "was talking" | | 4 | "were leaving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 225 | | ratio | 0.009 | | matches | | 0 | "The Golden Empress had been short-staffed; three trips across Soho in the rain had left her thighs aching and the cuffs of her jeans dark with water." | | 1 | "She wore a dark coat buttoned to the throat, and her hair—short now, cropped close at the nape—shone black beneath the lamps." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1319 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.0310841546626232 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004548900682335102 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 225 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 225 | | mean | 8.48 | | std | 6.94 | | cv | 0.819 | | sampleLengths | | 0 | 18 | | 1 | 22 | | 2 | 27 | | 3 | 7 | | 4 | 11 | | 5 | 4 | | 6 | 25 | | 7 | 7 | | 8 | 14 | | 9 | 9 | | 10 | 12 | | 11 | 3 | | 12 | 2 | | 13 | 2 | | 14 | 2 | | 15 | 8 | | 16 | 11 | | 17 | 16 | | 18 | 11 | | 19 | 7 | | 20 | 13 | | 21 | 22 | | 22 | 9 | | 23 | 7 | | 24 | 37 | | 25 | 5 | | 26 | 9 | | 27 | 1 | | 28 | 7 | | 29 | 12 | | 30 | 7 | | 31 | 6 | | 32 | 6 | | 33 | 3 | | 34 | 4 | | 35 | 5 | | 36 | 10 | | 37 | 3 | | 38 | 11 | | 39 | 3 | | 40 | 8 | | 41 | 5 | | 42 | 2 | | 43 | 15 | | 44 | 19 | | 45 | 11 | | 46 | 16 | | 47 | 15 | | 48 | 10 | | 49 | 6 |
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| 41.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.27555555555555555 | | totalSentences | 225 | | uniqueOpeners | 62 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 113 | | matches | | 0 | "Somewhere behind the bookshelf, the" | | 1 | "Then the woman said," | | 2 | "Maybe it had been both," | | 3 | "Instead Rory put a hand" | | 4 | "Then she stepped out into" |
| | ratio | 0.044 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 113 | | matches | | 0 | "He gave her a look" | | 1 | "He poured her a small" | | 2 | "His silver signet ring flashed" | | 3 | "She had just lifted it" | | 4 | "She wore a dark coat" | | 5 | "She looked toward the bar," | | 6 | "She knew the face, but" | | 7 | "She heard her own voice" | | 8 | "It had been a long" | | 9 | "She’d cut it once, years" | | 10 | "She had once dyed the" | | 11 | "She looked like someone who" | | 12 | "She’d fill it with a" | | 13 | "She remembered the two of" | | 14 | "She had been good at" | | 15 | "She had seen the scar" | | 16 | "It was how she used" | | 17 | "she said instead" | | 18 | "It was a quiet laugh," | | 19 | "It had taken what it" |
| | ratio | 0.239 | |
| 48.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 113 | | matches | | 0 | "The green neon above the" | | 1 | "Rory came in with her" | | 2 | "The Golden Empress had been" | | 3 | "Silas glanced up from polishing" | | 4 | "He gave her a look" | | 5 | "Rory set the bag beside" | | 6 | "The Nest was nearly empty," | | 7 | "He poured her a small" | | 8 | "His silver signet ring flashed" | | 9 | "Rory wrapped her fingers around" | | 10 | "She had just lifted it" | | 11 | "The bell gave its little" | | 12 | "A woman came in, folded" | | 13 | "She wore a dark coat" | | 14 | "She looked toward the bar," | | 15 | "She knew the face, but" | | 16 | "The whisky burned where Rory" | | 17 | "She heard her own voice" | | 18 | "It had been a long" | | 19 | "Eva set the umbrella against" |
| | ratio | 0.823 | |
| 88.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 113 | | matches | | 0 | "Now the crop exposed the" | | 1 | "When they were young, Eva" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 3 | | matches | | 0 | "She knew the face, but not the arrangement of it: the sharper jaw, the fine lines at the corners of the eyes, the mouth held still as if it had learned not to g…" | | 1 | "It was how she used to handle questions that reached too close: cut them with a little brightness and leave the other person holding the scraps." | | 2 | "Eva gave her a small wave, as if they were leaving each other at the end of an ordinary evening, not after six years of silence." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 115 | | tagDensity | 0.122 | | leniency | 0.243 | | rawRatio | 0 | | effectiveRatio | 0 | |