| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 127 | | tagDensity | 0.126 | | leniency | 0.252 | | rawRatio | 0.063 | | effectiveRatio | 0.016 | |
| 94.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2574 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "softly" | | 1 | "sharply" | | 2 | "quickly" |
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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) | |
| 84.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2574 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "flicked" | | 2 | "silence" | | 3 | "measured" | | 4 | "stomach" | | 5 | "flickered" | | 6 | "trembled" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "let out a breath" | | count | 2 |
| | 1 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | 0 | "let out a breath" | | 1 | "stomach dropped" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 174 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 174 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 285 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2573 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 122 | | wordCount | 1721 | | uniqueNames | 14 | | maxNameDensity | 2.96 | | worstName | "Aurora" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Silas | 7 | | Eva | 48 | | Aurora | 51 | | London | 1 | | Cardiff | 3 | | Jennifer | 1 | | Carter | 1 | | Brendan | 1 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Eva" | | 4 | "Aurora" | | 5 | "Jennifer" | | 6 | "Carter" | | 7 | "Brendan" | | 8 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 0.018 | | windowScore | 0 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 111 | | glossingSentenceCount | 3 | | matches | | 0 | "as if testing its temperature" | | 1 | "looked like bait" | | 2 | "as if checking the features against memory" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.777 | | wordCount | 2573 | | matches | | 0 | "not forgiveness, not yet, but recognition" | | 1 | "not yet, but recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 285 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 192 | | mean | 13.4 | | std | 16.14 | | cv | 1.204 | | sampleLengths | | 0 | 73 | | 1 | 58 | | 2 | 2 | | 3 | 17 | | 4 | 5 | | 5 | 1 | | 6 | 40 | | 7 | 56 | | 8 | 1 | | 9 | 109 | | 10 | 6 | | 11 | 1 | | 12 | 23 | | 13 | 1 | | 14 | 57 | | 15 | 14 | | 16 | 6 | | 17 | 19 | | 18 | 4 | | 19 | 2 | | 20 | 36 | | 21 | 4 | | 22 | 2 | | 23 | 17 | | 24 | 35 | | 25 | 3 | | 26 | 7 | | 27 | 2 | | 28 | 2 | | 29 | 26 | | 30 | 4 | | 31 | 14 | | 32 | 1 | | 33 | 2 | | 34 | 26 | | 35 | 6 | | 36 | 3 | | 37 | 8 | | 38 | 10 | | 39 | 7 | | 40 | 2 | | 41 | 2 | | 42 | 35 | | 43 | 3 | | 44 | 4 | | 45 | 13 | | 46 | 5 | | 47 | 27 | | 48 | 45 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 174 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 291 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 285 | | ratio | 0.004 | | matches | | 0 | "The old habit rose at once—to deflect, to offer a dry joke, to move the light somewhere else." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1726 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 54 | | adverbRatio | 0.031286210892236384 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0034762456546929316 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 285 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 285 | | mean | 9.03 | | std | 8.02 | | cv | 0.888 | | sampleLengths | | 0 | 11 | | 1 | 17 | | 2 | 30 | | 3 | 3 | | 4 | 12 | | 5 | 7 | | 6 | 5 | | 7 | 6 | | 8 | 12 | | 9 | 17 | | 10 | 11 | | 11 | 2 | | 12 | 17 | | 13 | 5 | | 14 | 1 | | 15 | 17 | | 16 | 23 | | 17 | 47 | | 18 | 9 | | 19 | 1 | | 20 | 18 | | 21 | 26 | | 22 | 11 | | 23 | 21 | | 24 | 6 | | 25 | 3 | | 26 | 4 | | 27 | 20 | | 28 | 6 | | 29 | 1 | | 30 | 5 | | 31 | 5 | | 32 | 13 | | 33 | 1 | | 34 | 7 | | 35 | 4 | | 36 | 24 | | 37 | 22 | | 38 | 14 | | 39 | 6 | | 40 | 8 | | 41 | 11 | | 42 | 4 | | 43 | 2 | | 44 | 3 | | 45 | 4 | | 46 | 29 | | 47 | 4 | | 48 | 2 | | 49 | 11 |
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| 45.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.23157894736842105 | | totalSentences | 285 | | uniqueOpeners | 66 | |
| 20.83% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 160 | | matches | | 0 | "Then the woman turned fully," |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 160 | | matches | | 0 | "Her shoulders ached." | | 1 | "He gave her one look" | | 2 | "She had opened her mouth" | | 3 | "Her hair, once bleached into" | | 4 | "She looked smaller." | | 5 | "Her hand slipped from the" | | 6 | "He did not ask." | | 7 | "He took a clean glass" | | 8 | "His limp showed for a" | | 9 | "She looked Aurora over, and" | | 10 | "She had cut it the" | | 11 | "She took a mouthful and" | | 12 | "He had the same steady" | | 13 | "It caught the green from" | | 14 | "His ring flashed once in" | | 15 | "She looked down at her" | | 16 | "I can’t stay." | | 17 | "She hated the word fate." | | 18 | "It let everyone wash their" | | 19 | "It was weather." |
| | ratio | 0.219 | |
| 13.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 143 | | totalSentences | 160 | | matches | | 0 | "Rain had rinsed Soho and" | | 1 | "The green neon above The" | | 2 | "Aurora came in under it" | | 3 | "Her shoulders ached." | | 4 | "Glass caught the low light" | | 5 | "He gave her one look" | | 6 | "Silas set the tumbler down." | | 7 | "Aurora slid the helmet onto" | | 8 | "She had opened her mouth" | | 9 | "This woman wore a dark" | | 10 | "Her hair, once bleached into" | | 11 | "She looked smaller." | | 12 | "Her hand slipped from the" | | 13 | "Aurora stayed where she was." | | 14 | "Rain tapped the front window." | | 15 | "Someone near the back laughed" | | 16 | "Silas glanced from one to" | | 17 | "He did not ask." | | 18 | "He took a clean glass" | | 19 | "His limp showed for a" |
| | ratio | 0.894 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 160 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 2 | | matches | | 0 | "For a second the room narrowed to a profile she knew by muscle memory: the line of a cheek, the left eyebrow with its small split from school, fingers wrapped r…" | | 1 | "She looked Aurora over, and the looking felt careful, almost formal, as if she had been handed something fragile and expensive and had no idea where to place it…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 127 | | tagDensity | 0.063 | | leniency | 0.126 | | rawRatio | 0.125 | | effectiveRatio | 0.016 | |