| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1471 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "gently" | | 1 | "very" | | 2 | "slowly" | | 3 | "quickly" |
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| 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) | |
| 62.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1471 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "footsteps" | | 2 | "whisper" | | 3 | "pulse" | | 4 | "fluttered" | | 5 | "pulsed" | | 6 | "racing" |
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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 | 162 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 162 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 165 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1471 | | ratio | 0 | | matches | (empty) | |
| 41.67% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 1 | | matches | | 0 | "In thick, dark strokes, it said: COME BACK." |
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| 91.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1456 | | uniqueNames | 10 | | maxNameDensity | 1.17 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 17 | | Park | 1 | | November | 1 | | Golden | 3 | | Empress | 3 | | Heartstone | 3 | | Yu-Fei | 2 | | Hel | 2 | | Eva | 1 | | Silas | 1 |
| | persons | | 0 | "Rory" | | 1 | "Heartstone" | | 2 | "Yu-Fei" | | 3 | "Eva" | | 4 | "Silas" |
| | places | | 0 | "Park" | | 1 | "November" | | 2 | "Golden" | | 3 | "Hel" |
| | globalScore | 0.916 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.68 | | wordCount | 1471 | | matches | | 0 | "not at the gap but at the place in the grass" |
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| 85.86% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 4 | | totalSentences | 165 | | matches | | 0 | "knew that much" | | 1 | "through that door" | | 2 | "times that day" | | 3 | "touched that oak" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 20.15 | | std | 16.4 | | cv | 0.814 | | sampleLengths | | 0 | 10 | | 1 | 54 | | 2 | 8 | | 3 | 9 | | 4 | 14 | | 5 | 46 | | 6 | 6 | | 7 | 54 | | 8 | 51 | | 9 | 4 | | 10 | 41 | | 11 | 1 | | 12 | 5 | | 13 | 11 | | 14 | 39 | | 15 | 2 | | 16 | 33 | | 17 | 5 | | 18 | 20 | | 19 | 9 | | 20 | 40 | | 21 | 7 | | 22 | 19 | | 23 | 15 | | 24 | 30 | | 25 | 4 | | 26 | 22 | | 27 | 26 | | 28 | 3 | | 29 | 6 | | 30 | 37 | | 31 | 10 | | 32 | 46 | | 33 | 38 | | 34 | 7 | | 35 | 27 | | 36 | 3 | | 37 | 9 | | 38 | 38 | | 39 | 14 | | 40 | 51 | | 41 | 2 | | 42 | 7 | | 43 | 15 | | 44 | 10 | | 45 | 14 | | 46 | 30 | | 47 | 4 | | 48 | 40 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 162 | | matches | | 0 | "was gone" | | 1 | "were hidden" |
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| 59.35% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 237 | | matches | | 0 | "was not going" | | 1 | "was still moving" | | 2 | "was keeping" | | 3 | "was approaching" | | 4 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 165 | | ratio | 0.006 | | matches | | 0 | "She had touched that oak when she tucked in the receipt; she remembered the rough split beneath her fingertips." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 108 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.027777777777777776 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 165 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 165 | | mean | 8.92 | | std | 5.31 | | cv | 0.595 | | sampleLengths | | 0 | 10 | | 1 | 11 | | 2 | 10 | | 3 | 11 | | 4 | 7 | | 5 | 15 | | 6 | 4 | | 7 | 2 | | 8 | 2 | | 9 | 9 | | 10 | 9 | | 11 | 4 | | 12 | 1 | | 13 | 2 | | 14 | 19 | | 15 | 19 | | 16 | 6 | | 17 | 6 | | 18 | 11 | | 19 | 19 | | 20 | 7 | | 21 | 8 | | 22 | 9 | | 23 | 21 | | 24 | 19 | | 25 | 11 | | 26 | 4 | | 27 | 16 | | 28 | 12 | | 29 | 13 | | 30 | 1 | | 31 | 3 | | 32 | 2 | | 33 | 11 | | 34 | 5 | | 35 | 5 | | 36 | 20 | | 37 | 9 | | 38 | 2 | | 39 | 3 | | 40 | 14 | | 41 | 16 | | 42 | 5 | | 43 | 14 | | 44 | 2 | | 45 | 4 | | 46 | 9 | | 47 | 5 | | 48 | 15 | | 49 | 13 |
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| 45.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3151515151515151 | | totalSentences | 165 | | uniqueOpeners | 52 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 150 | | matches | | 0 | "Even her breathing sounded as" | | 1 | "Then she’d put on her" | | 2 | "Then she walked in." | | 3 | "Somewhere beyond the clearing came" | | 4 | "Instead, she heard footsteps in" | | 5 | "Then she walked three quick" | | 6 | "Perhaps the grove had its" | | 7 | "Perhaps something had followed her" | | 8 | "Perhaps that was all the" | | 9 | "Only then did she look" |
| | ratio | 0.067 | |
| 76.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 150 | | matches | | 0 | "She had heard a car" | | 1 | "She checked her phone." | | 2 | "You want to know who" | | 3 | "She had found it under" | | 4 | "She’d spent half an hour" | | 5 | "She knew that much about" | | 6 | "She also knew time could" | | 7 | "She had told herself she" | | 8 | "Its faint inner glow showed" | | 9 | "She drew it out and" | | 10 | "Her voice travelled a little" | | 11 | "She took another few steps." | | 12 | "It sounded again." | | 13 | "She had heard Yu-Fei come" | | 14 | "She waited for a voice," | | 15 | "She looked straight at it." | | 16 | "It was still moving when" | | 17 | "She could hear the strain" | | 18 | "She listened for an answer," | | 19 | "She turned at once." |
| | ratio | 0.36 | |
| 93.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 110 | | totalSentences | 150 | | matches | | 0 | "The first thing Rory noticed" | | 1 | "Richmond Park lay behind her," | | 2 | "She had heard a car" | | 3 | "She checked her phone." | | 4 | "The note in her coat" | | 5 | "You want to know who" | | 6 | "She had found it under" | | 7 | "She’d spent half an hour" | | 8 | "Rory stepped between the oak" | | 9 | "Wildflowers grew thick among the" | | 10 | "She knew that much about" | | 11 | "She also knew time could" | | 12 | "She had told herself she" | | 13 | "The paper was printed with" | | 14 | "The Heartstone rested against her" | | 15 | "Its faint inner glow showed" | | 16 | "She drew it out and" | | 17 | "Her voice travelled a little" | | 18 | "She took another few steps." | | 19 | "The grass brushed her calves." |
| | ratio | 0.733 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 150 | | matches | | 0 | "Now, between the upright pillars" | | 1 | "If the paths shifted, she" | | 2 | "Whoever had sent it had" | | 3 | "Either way, she had no" | | 4 | "Now she drove her shoulder" |
| | ratio | 0.033 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 4 | | matches | | 0 | "Wildflowers grew thick among the grasses, small white and yellow blooms that had no business being there in November." | | 1 | "An ordinary branch, slender and bare, hanging from an oak that leaned over the grass." | | 2 | "She remembered the little scrap of paper that had arrived with it, before the note under her door: a warning, not an explanation." | | 3 | "Rory stood in the grey morning with the screen trembling in her hand, trying to think which call to return first." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |