| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2031 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "tightly" | | 1 | "very" | | 2 | "slowly" |
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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) | |
| 58.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2031 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "warmth" | | 1 | "whisper" | | 2 | "trembled" | | 3 | "lurch" | | 4 | "stomach" | | 5 | "silence" | | 6 | "weight" | | 7 | "pulse" | | 8 | "quivered" | | 9 | "velvet" |
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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 | 241 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 241 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 250 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2031 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1975 | | uniqueNames | 12 | | maxNameDensity | 0.76 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Laila" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Heartstone | 2 | | Roehampton | 1 | | Gate | 1 | | London | 1 | | Rory | 2 | | Fae | 1 | | Grove | 4 | | January | 1 | | Laila | 3 | | Aurora | 15 |
| | persons | | 0 | "Park" | | 1 | "Heartstone" | | 2 | "Rory" | | 3 | "Laila" | | 4 | "Aurora" |
| | places | | 0 | "Richmond" | | 1 | "Roehampton" | | 2 | "London" | | 3 | "Fae" | | 4 | "Grove" | | 5 | "January" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 139 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like leaves rubbed to powder" |
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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 | 2031 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 250 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 116 | | mean | 17.51 | | std | 18.01 | | cv | 1.029 | | sampleLengths | | 0 | 58 | | 1 | 13 | | 2 | 47 | | 3 | 12 | | 4 | 27 | | 5 | 20 | | 6 | 10 | | 7 | 73 | | 8 | 3 | | 9 | 40 | | 10 | 6 | | 11 | 2 | | 12 | 7 | | 13 | 10 | | 14 | 12 | | 15 | 11 | | 16 | 20 | | 17 | 9 | | 18 | 3 | | 19 | 56 | | 20 | 10 | | 21 | 7 | | 22 | 44 | | 23 | 2 | | 24 | 37 | | 25 | 7 | | 26 | 20 | | 27 | 4 | | 28 | 24 | | 29 | 6 | | 30 | 6 | | 31 | 57 | | 32 | 5 | | 33 | 10 | | 34 | 9 | | 35 | 7 | | 36 | 13 | | 37 | 39 | | 38 | 6 | | 39 | 47 | | 40 | 60 | | 41 | 13 | | 42 | 6 | | 43 | 21 | | 44 | 4 | | 45 | 3 | | 46 | 45 | | 47 | 6 | | 48 | 8 | | 49 | 27 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 241 | | matches | | 0 | "was headed" | | 1 | "been folded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 291 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 250 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1981 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 74 | | adverbRatio | 0.03735487127713276 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.006562342251388188 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 250 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 250 | | mean | 8.12 | | std | 6.07 | | cv | 0.747 | | sampleLengths | | 0 | 19 | | 1 | 11 | | 2 | 8 | | 3 | 20 | | 4 | 13 | | 5 | 10 | | 6 | 5 | | 7 | 2 | | 8 | 10 | | 9 | 20 | | 10 | 11 | | 11 | 1 | | 12 | 15 | | 13 | 12 | | 14 | 7 | | 15 | 13 | | 16 | 5 | | 17 | 5 | | 18 | 6 | | 19 | 37 | | 20 | 4 | | 21 | 4 | | 22 | 22 | | 23 | 3 | | 24 | 14 | | 25 | 18 | | 26 | 4 | | 27 | 4 | | 28 | 6 | | 29 | 2 | | 30 | 7 | | 31 | 10 | | 32 | 4 | | 33 | 8 | | 34 | 5 | | 35 | 2 | | 36 | 4 | | 37 | 9 | | 38 | 8 | | 39 | 3 | | 40 | 9 | | 41 | 3 | | 42 | 10 | | 43 | 12 | | 44 | 12 | | 45 | 5 | | 46 | 17 | | 47 | 10 | | 48 | 3 | | 49 | 4 |
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| 44.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.312 | | totalSentences | 250 | | uniqueOpeners | 78 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 15 | | totalSentences | 202 | | matches | | 0 | "Only the scrape of her" | | 1 | "Somewhere to her left, a" | | 2 | "Again, a movement on the" | | 3 | "Then one of the trunks" | | 4 | "Then to her right." | | 5 | "Only the little metallic tremor" | | 6 | "Then another patch farther off." | | 7 | "Then, from the far side" | | 8 | "Then she caught it: a" | | 9 | "Only an absence cut into" | | 10 | "Then the cloud passed back" | | 11 | "Even the old oak in" | | 12 | "Only the shape of speech," | | 13 | "Then, from somewhere near her" | | 14 | "Then a fourth, near the" |
| | ratio | 0.074 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 202 | | matches | | 0 | "She shoved her hands into" | | 1 | "she muttered into the dark" | | 2 | "Her voice died at once." | | 3 | "She knew where she was" | | 4 | "She cut off the main" | | 5 | "Her fingers closed around the" | | 6 | "She waited for the laugh" | | 7 | "It did not." | | 8 | "she said, and heard how" | | 9 | "She walked on." | | 10 | "She turned her head and" | | 11 | "She took another few steps." | | 12 | "She stopped a second time" | | 13 | "She pulled it free from" | | 14 | "She looked ahead." | | 15 | "She had fixed on a" | | 16 | "Her mouth had gone dry." | | 17 | "She left the path and" | | 18 | "Her stomach dipped." | | 19 | "She stepped through, and the" |
| | ratio | 0.208 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 135 | | totalSentences | 202 | | matches | | 0 | "The gate into Richmond Park" | | 1 | "Aurora slipped through sideways and" | | 2 | "The sound carried farther than" | | 3 | "She shoved her hands into" | | 4 | "The Heartstone pendant rested beneath" | | 5 | "That warmth had started near" | | 6 | "she muttered into the dark" | | 7 | "Her voice died at once." | | 8 | "The park swallowed it whole." | | 9 | "She knew where she was" | | 10 | "Eva had once called it" | | 11 | "Wildflowers blooming in winter." | | 12 | "A pocket of land that" | | 13 | "The Fae Grove." | | 14 | "She cut off the main" | | 15 | "The moon hung thin and" | | 16 | "Every trunk looked charred." | | 17 | "Every bush looked crouched." | | 18 | "The warmth at her chest" | | 19 | "The sound rang once, high" |
| | ratio | 0.668 | |
| 74.26% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 202 | | matches | | 0 | "Now it felt less like" | | 1 | "While she stared, it stood" | | 2 | "Now it leaned halfway from" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 80 | | technicalSentenceCount | 2 | | matches | | 0 | "Across the sleeping fields, over black grass silvered by frost, into clumps of trees that looked packed too tightly together." | | 1 | "Again, a movement on the right, quick and low, as if something had darted behind a beech." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "she whispered, half to herself now" |
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| 16.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 15 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.4 | | effectiveRatio | 0.267 | |