| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 1 | | adverbTags | | 0 | "She spoke steadily [steadily]" |
| | dialogueSentences | 102 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0.042 | | effectiveRatio | 0.02 | |
| 97.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1989 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 79.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1989 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "silk" | | 1 | "weight" | | 2 | "impose" | | 3 | "silence" | | 4 | "tracing" | | 5 | "could feel" | | 6 | "pulse" | | 7 | "race" |
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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 | 1 | | narrationSentences | 145 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 145 | | filterMatches | (empty) | | 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 | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1989 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 29 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1311 | | uniqueNames | 6 | | maxNameDensity | 2.06 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 16 | | Aurora | 1 | | Lucien | 22 | | Moreau | 1 | | Rory | 27 | | Lane | 1 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Rory" |
| | places | | | globalScore | 0.47 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite into the flat" |
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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 | 1989 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 225 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 121 | | mean | 16.44 | | std | 16.78 | | cv | 1.021 | | sampleLengths | | 0 | 22 | | 1 | 25 | | 2 | 3 | | 3 | 15 | | 4 | 6 | | 5 | 39 | | 6 | 9 | | 7 | 18 | | 8 | 14 | | 9 | 15 | | 10 | 67 | | 11 | 17 | | 12 | 3 | | 13 | 2 | | 14 | 2 | | 15 | 4 | | 16 | 5 | | 17 | 6 | | 18 | 52 | | 19 | 7 | | 20 | 5 | | 21 | 2 | | 22 | 6 | | 23 | 45 | | 24 | 17 | | 25 | 29 | | 26 | 48 | | 27 | 2 | | 28 | 2 | | 29 | 6 | | 30 | 52 | | 31 | 3 | | 32 | 5 | | 33 | 7 | | 34 | 65 | | 35 | 22 | | 36 | 3 | | 37 | 9 | | 38 | 2 | | 39 | 25 | | 40 | 5 | | 41 | 7 | | 42 | 7 | | 43 | 4 | | 44 | 14 | | 45 | 9 | | 46 | 4 | | 47 | 6 | | 48 | 27 | | 49 | 68 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 247 | | matches | | 0 | "was watching" | | 1 | "was already reaching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 225 | | ratio | 0.009 | | matches | | 0 | "Rory had cleared a space among them that afternoon for a plate of toast; Eva had since covered it with notes." | | 1 | "The top one stuck; she had to shove it home with the heel of her hand." |
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| 98.30% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 763 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.04193971166448231 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005242463958060288 | |
| 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 | 8.84 | | std | 6.26 | | cv | 0.708 | | sampleLengths | | 0 | 22 | | 1 | 8 | | 2 | 9 | | 3 | 8 | | 4 | 3 | | 5 | 4 | | 6 | 6 | | 7 | 5 | | 8 | 6 | | 9 | 13 | | 10 | 21 | | 11 | 5 | | 12 | 9 | | 13 | 9 | | 14 | 6 | | 15 | 3 | | 16 | 14 | | 17 | 15 | | 18 | 3 | | 19 | 18 | | 20 | 19 | | 21 | 6 | | 22 | 21 | | 23 | 9 | | 24 | 6 | | 25 | 2 | | 26 | 3 | | 27 | 2 | | 28 | 2 | | 29 | 4 | | 30 | 5 | | 31 | 5 | | 32 | 1 | | 33 | 7 | | 34 | 4 | | 35 | 16 | | 36 | 9 | | 37 | 16 | | 38 | 7 | | 39 | 5 | | 40 | 2 | | 41 | 6 | | 42 | 8 | | 43 | 19 | | 44 | 18 | | 45 | 13 | | 46 | 4 | | 47 | 4 | | 48 | 11 | | 49 | 14 |
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| 48.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.29777777777777775 | | totalSentences | 225 | | uniqueOpeners | 67 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 132 | | matches | | 0 | "Dark green silk, neatly knotted" | | 1 | "Then she noticed the blood" | | 2 | "Instead he waited." | | 3 | "Of course he did." | | 4 | "Even hurt, Lucien placed himself" | | 5 | "Then she went into the" | | 6 | "Then he’d hailed a cab" | | 7 | "Instead he reached for his" | | 8 | "Then she stepped into the" |
| | ratio | 0.068 | |
| 80.61% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 132 | | matches | | 0 | "His amber eye narrowed." | | 1 | "He shifted his weight onto" | | 2 | "It was such a small" | | 3 | "He could have answered for" | | 4 | "she called back, and opened" | | 5 | "Her eyes went to Lucien’s" | | 6 | "He took the chair nearest" | | 7 | "She had spent the last" | | 8 | "She opened the box and" | | 9 | "He unbuttoned his cuff with" | | 10 | "His fingers fumbled on the" | | 11 | "He went still." | | 12 | "She freed the button, then" | | 13 | "She fetched a bowl of" | | 14 | "His hair, usually so immaculate," | | 15 | "She remembered putting her hand" | | 16 | "She pushed the memory away" | | 17 | "He looked at her over" | | 18 | "She pressed a clean pad" | | 19 | "He had always been good" |
| | ratio | 0.348 | |
| 54.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 107 | | totalSentences | 132 | | matches | | 0 | "The third deadbolt slid back," | | 1 | "His amber eye narrowed." | | 2 | "The black one gave nothing" | | 3 | "He shifted his weight onto" | | 4 | "It was such a small" | | 5 | "Lucien looked past her, not" | | 6 | "He could have answered for" | | 7 | "Rory hated that she still" | | 8 | "she called back, and opened" | | 9 | "Lucien stepped inside." | | 10 | "Ptolemy, who had been winding" | | 11 | "The flat smelled of curry" | | 12 | "Books and scrolls crowded every" | | 13 | "Rory had cleared a space" | | 14 | "Eva emerged from the kitchen" | | 15 | "Her eyes went to Lucien’s" | | 16 | "He took the chair nearest" | | 17 | "Rory shut the door and" | | 18 | "The top one stuck; she" | | 19 | "The words landed before she" |
| | ratio | 0.811 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 132 | | matches | | 0 | "Now he had been to" | | 1 | "By the time she came" | | 2 | "Now she could feel it" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 1 | | matches | | 0 | "She spoke steadily, though her pulse had begun to race" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 2 | | fancyTags | | 0 | "she called back (call back)" | | 1 | "She spoke steadily (speak)" |
| | dialogueSentences | 102 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.118 | | effectiveRatio | 0.039 | |