| 82.35% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said finally [finally]" | | 1 | "she said quietly [quietly]" |
| | dialogueSentences | 34 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0.143 | | effectiveRatio | 0.118 | |
| 86.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1089 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "really" | | 2 | "gently" |
| |
| 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) | |
| 31.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1089 | | totalAiIsms | 15 | | found | | 0 | | | 1 | | | 2 | | word | "skipped a beat" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | word | "the last thing" | | count | 1 |
| | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | |
| | highlights | | 0 | "weight" | | 1 | "tinged" | | 2 | "skipped a beat" | | 3 | "encounter" | | 4 | "flickered" | | 5 | "eyebrow" | | 6 | "the last thing" | | 7 | "echoing" | | 8 | "tension" | | 9 | "silence" | | 10 | "warmth" | | 11 | "sense of" | | 12 | "flicker" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
| | 2 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" | | 2 | "a flicker of hope" |
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| 94.51% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 82 | | matches | | 0 | "a sense of relief" | | 1 | "a flicker of hope" | | 2 | "was grateful" |
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| 90.59% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 82 | | filterMatches | (empty) | | hedgeMatches | | |
| 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 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1091 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 843 | | uniqueNames | 7 | | maxNameDensity | 2.37 | | worstName | "Lucien" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Eva | 4 | | London | 1 | | Moreau | 1 | | French | 1 | | Aurora | 18 | | Lucien | 20 |
| | persons | | 0 | "Carter" | | 1 | "Eva" | | 2 | "Moreau" | | 3 | "Aurora" | | 4 | "Lucien" |
| | places | | | globalScore | 0.314 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1091 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 28.71 | | std | 16.31 | | cv | 0.568 | | sampleLengths | | 0 | 86 | | 1 | 51 | | 2 | 54 | | 3 | 19 | | 4 | 48 | | 5 | 14 | | 6 | 30 | | 7 | 37 | | 8 | 15 | | 9 | 18 | | 10 | 36 | | 11 | 34 | | 12 | 35 | | 13 | 44 | | 14 | 14 | | 15 | 36 | | 16 | 6 | | 17 | 27 | | 18 | 9 | | 19 | 21 | | 20 | 40 | | 21 | 13 | | 22 | 34 | | 23 | 12 | | 24 | 19 | | 25 | 19 | | 26 | 38 | | 27 | 19 | | 28 | 30 | | 29 | 10 | | 30 | 33 | | 31 | 14 | | 32 | 48 | | 33 | 29 | | 34 | 8 | | 35 | 11 | | 36 | 36 | | 37 | 44 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 170 | | matches | | 0 | "was, standing" | | 1 | "was, standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 102 | | ratio | 0.01 | | matches | | 0 | "Lucien Moreau stood in the doorway, his heterochromatic eyes — one amber, one black — locking onto hers." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 845 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 30 | | adverbRatio | 0.03550295857988166 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008284023668639054 | |
| 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 | 10.7 | | std | 6.2 | | cv | 0.58 | | sampleLengths | | 0 | 21 | | 1 | 23 | | 2 | 22 | | 3 | 2 | | 4 | 18 | | 5 | 15 | | 6 | 5 | | 7 | 12 | | 8 | 19 | | 9 | 10 | | 10 | 18 | | 11 | 11 | | 12 | 15 | | 13 | 12 | | 14 | 7 | | 15 | 5 | | 16 | 17 | | 17 | 10 | | 18 | 16 | | 19 | 14 | | 20 | 9 | | 21 | 1 | | 22 | 1 | | 23 | 5 | | 24 | 14 | | 25 | 3 | | 26 | 3 | | 27 | 11 | | 28 | 20 | | 29 | 15 | | 30 | 4 | | 31 | 14 | | 32 | 2 | | 33 | 16 | | 34 | 11 | | 35 | 7 | | 36 | 9 | | 37 | 10 | | 38 | 15 | | 39 | 19 | | 40 | 6 | | 41 | 10 | | 42 | 11 | | 43 | 14 | | 44 | 19 | | 45 | 9 | | 46 | 5 | | 47 | 5 | | 48 | 23 | | 49 | 6 |
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| 57.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3627450980392157 | | totalSentences | 102 | | uniqueOpeners | 37 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Maybe, just maybe, they could" |
| | ratio | 0.014 | |
| 84.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 74 | | matches | | 0 | "She shifted her weight from" | | 1 | "She knew them well, had" | | 2 | "She raised her hand to" | | 3 | "She had fled to London" | | 4 | "He was impeccably dressed, as" | | 5 | "His platinum blond hair was" | | 6 | "he said, his voice smooth" | | 7 | "She hadn't seen Lucien in" | | 8 | "She had stormed out, vowing" | | 9 | "she said, her voice steadier" | | 10 | "She had known." | | 11 | "she said, her tone sharper" | | 12 | "He gestured to the worn" | | 13 | "She watched as Lucien moved" | | 14 | "He filled the kettle and" | | 15 | "he said, folding his arms" | | 16 | "She had rehearsed this conversation" | | 17 | "she said finally" | | 18 | "he said, his voice low" | | 19 | "She wrapped her hands around" |
| | ratio | 0.338 | |
| 61.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 74 | | matches | | 0 | "Aurora Carter stood in the" | | 1 | "The air was thick with" | | 2 | "She shifted her weight from" | | 3 | "She knew them well, had" | | 4 | "She raised her hand to" | | 5 | "She had fled to London" | | 6 | "Lucien Moreau stood in the" | | 7 | "He was impeccably dressed, as" | | 8 | "His platinum blond hair was" | | 9 | "he said, his voice smooth" | | 10 | "Aurora's heart skipped a beat." | | 11 | "She hadn't seen Lucien in" | | 12 | "She had stormed out, vowing" | | 13 | "she said, her voice steadier" | | 14 | "Lucien's gaze flickered with something" | | 15 | "Aurora's cheeks flushed." | | 16 | "She had known." | | 17 | "Eva had mentioned it in" | | 18 | "she said, her tone sharper" | | 19 | "Lucien raised an eyebrow." |
| | ratio | 0.797 | |
| 67.57% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 74 | | matches | | 0 | "Before she could second-guess herself" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 1 | | matches | | 0 | "Yet here she was, standing at the threshold of a place that held too many memories, too many ghosts." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 6 | | matches | | 0 | "he said, his voice smooth and tinged with a French accent" | | 1 | "she said, her voice steadier than she felt" | | 2 | "she said, her tone sharper than she intended" | | 3 | "Aurora said, her voice softening" | | 4 | "he said, his voice low" | | 5 | "Lucien said, his tone all business now" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0 | | effectiveRatio | 0 | |