| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 68 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1102 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 72.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1102 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "measured" | | 1 | "whisper" | | 2 | "pulse" | | 3 | "flickered" | | 4 | "silence" | | 5 | "weight" |
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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 | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1102 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 6 | | wordCount | 787 | | uniqueNames | 6 | | maxNameDensity | 0.13 | | worstName | "Rory" | | maxWindowNameDensity | 0 | | worstWindowName | (null) | | discoveredNames | | Rory | 1 | | London | 1 | | Golden | 1 | | Empress | 1 | | Soho | 1 | | Lane | 1 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like two different questions being" |
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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 | 1102 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 86 | | mean | 12.81 | | std | 18.61 | | cv | 1.452 | | sampleLengths | | 0 | 49 | | 1 | 6 | | 2 | 1 | | 3 | 5 | | 4 | 4 | | 5 | 63 | | 6 | 1 | | 7 | 1 | | 8 | 9 | | 9 | 1 | | 10 | 1 | | 11 | 39 | | 12 | 59 | | 13 | 2 | | 14 | 2 | | 15 | 22 | | 16 | 3 | | 17 | 1 | | 18 | 82 | | 19 | 4 | | 20 | 3 | | 21 | 1 | | 22 | 17 | | 23 | 8 | | 24 | 2 | | 25 | 1 | | 26 | 43 | | 27 | 4 | | 28 | 8 | | 29 | 1 | | 30 | 6 | | 31 | 48 | | 32 | 26 | | 33 | 10 | | 34 | 2 | | 35 | 4 | | 36 | 77 | | 37 | 8 | | 38 | 2 | | 39 | 12 | | 40 | 4 | | 41 | 1 | | 42 | 3 | | 43 | 1 | | 44 | 1 | | 45 | 35 | | 46 | 2 | | 47 | 1 | | 48 | 6 | | 49 | 2 |
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| 89.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 68 | | matches | | 0 | "been tidied" | | 1 | "was thrown" | | 2 | "being asked" |
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| 58.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 141 | | matches | | 0 | "was sharpening" | | 1 | "was not touching" | | 2 | "were standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 468 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.021367521367521368 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.004273504273504274 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 8.54 | | std | 9.44 | | cv | 1.106 | | sampleLengths | | 0 | 26 | | 1 | 23 | | 2 | 6 | | 3 | 1 | | 4 | 5 | | 5 | 4 | | 6 | 20 | | 7 | 8 | | 8 | 35 | | 9 | 1 | | 10 | 1 | | 11 | 7 | | 12 | 2 | | 13 | 1 | | 14 | 1 | | 15 | 6 | | 16 | 5 | | 17 | 28 | | 18 | 8 | | 19 | 10 | | 20 | 17 | | 21 | 7 | | 22 | 17 | | 23 | 2 | | 24 | 2 | | 25 | 3 | | 26 | 19 | | 27 | 3 | | 28 | 1 | | 29 | 4 | | 30 | 32 | | 31 | 7 | | 32 | 32 | | 33 | 7 | | 34 | 4 | | 35 | 3 | | 36 | 1 | | 37 | 17 | | 38 | 5 | | 39 | 3 | | 40 | 2 | | 41 | 1 | | 42 | 9 | | 43 | 5 | | 44 | 4 | | 45 | 25 | | 46 | 4 | | 47 | 8 | | 48 | 1 | | 49 | 6 |
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| 67.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.4806201550387597 | | totalSentences | 129 | | uniqueOpeners | 62 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 60 | | matches | | 0 | "Then the unmistakable, dull thud" |
| | ratio | 0.017 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 60 | | matches | | 0 | "She opened the door an" | | 1 | "He looked, in fact, like" | | 2 | "He tasted it, found it" | | 3 | "She stepped back from the" | | 4 | "She didn't invite him in." | | 5 | "He came in anyway, the" | | 6 | "Her jacket was thrown over" | | 7 | "She watched him catalogue all" | | 8 | "She glanced down." | | 9 | "He did not sit." | | 10 | "He placed the cane against" | | 11 | "Her pulse did something stupid." | | 12 | "She ignored it." | | 13 | "She didn't know the name." | | 14 | "She didn't need to." | | 15 | "She could read his face," | | 16 | "She walked past him to" | | 17 | "She filled it." | | 18 | "She plugged it in." | | 19 | "She watched the little blue" |
| | ratio | 0.567 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 60 | | matches | | 0 | "The third deadbolt stuck, the" | | 1 | "Nobody else in London knocked" | | 2 | "She opened the door an" | | 3 | "Lucien leaned on the ivory" | | 4 | "The charcoal suit looked fresh" | | 5 | "He looked, in fact, like" | | 6 | "He tasted it, found it" | | 7 | "She stepped back from the" | | 8 | "She didn't invite him in." | | 9 | "He came in anyway, the" | | 10 | "The flat had not been" | | 11 | "A mug with a lipstick" | | 12 | "A delivery thermal bag slumped" | | 13 | "Her jacket was thrown over" | | 14 | "She watched him catalogue all" | | 15 | "She glanced down." | | 16 | "A scratch along the heel" | | 17 | "He did not sit." | | 18 | "He placed the cane against" | | 19 | "Her pulse did something stupid." |
| | ratio | 0.867 | |
| 83.33% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 60 | | matches | | 0 | "As though orbits were something" |
| | ratio | 0.017 | |
| 18.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 4 | | matches | | 0 | "Nobody else in London knocked like that, three measured taps, the last one lighter, as if politeness were a weapon he was sharpening." | | 1 | "He came in anyway, the way smoke came in, folding himself past her shoulder with a whisper of wool and something that smelled faintly of bergamot and iron." | | 2 | "Not close, not the way he had been once, a hand at the small of her back in a stairwell in Soho, breath at her ear telling her to keep walking, don't look at th…" | | 3 | "He'd picked up her mug, the one with the lipstick, and set it in the sink without looking at it, the small domestic gesture of a man who could not help arrangin…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 68 | | tagDensity | 0.074 | | leniency | 0.147 | | rawRatio | 0 | | effectiveRatio | 0 | |